id	sid	tid	token	lemma	pos
cuesj-900	1	1	tx_1	tx_1	PROPN
cuesj-900	1	2	~	~	X
cuesj-900	1	3	abs	ab	NOUN
cuesj-900	1	4	:	:	PUNCT
cuesj-900	1	5	at	at	ADP
cuesj-900	1	6	/	/	SYM
cuesj-900	1	7	add	add	VERB
cuesj-900	1	8	:	:	PUNCT
cuesj-900	1	9	tx_2	tx_2	PROPN
cuesj-900	1	10	~	~	NOUN
cuesj-900	1	11	abs	ab	NOUN
cuesj-900	1	12	:	:	PUNCT
cuesj-900	1	13	at	at	ADP
cuesj-900	1	14	52	52	NUM
cuesj-900	1	15	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-900	1	16	cuesj	cuesj	NOUN
cuesj-900	1	17	2023	2023	NUM
cuesj-900	1	18	,	,	PUNCT
cuesj-900	1	19	7	7	NUM
cuesj-900	1	20	(	(	PUNCT
cuesj-900	1	21	1	1	NUM
cuesj-900	1	22	):	):	PUNCT
cuesj-900	1	23	52	52	NUM
cuesj-900	1	24	-	-	SYM
cuesj-900	1	25	59	59	NUM
cuesj-900	1	26	research	research	NOUN
cuesj-900	1	27	article	article	NOUN
cuesj-900	1	28	the	the	DET
cuesj-900	1	29	use	use	NOUN
cuesj-900	1	30	of	of	ADP
cuesj-900	1	31	clustering	clustering	NOUN
cuesj-900	1	32	and	and	CCONJ
cuesj-900	1	33	classification	classification	NOUN
cuesj-900	1	34	methods	method	NOUN
cuesj-900	1	35	in	in	ADP
cuesj-900	1	36	machine	machine	NOUN
cuesj-900	1	37	learning	learning	NOUN
cuesj-900	1	38	and	and	CCONJ
cuesj-900	1	39	comparison	comparison	NOUN
cuesj-900	1	40	of	of	ADP
cuesj-900	1	41	some	some	DET
cuesj-900	1	42	algorithms	algorithm	NOUN
cuesj-900	1	43	of	of	ADP
cuesj-900	1	44	the	the	DET
cuesj-900	1	45	methods	method	NOUN
cuesj-900	1	46	guhdar	guhdar	PROPN
cuesj-900	1	47	a.	a.	NOUN
cuesj-900	1	48	a.	a.	PROPN
cuesj-900	1	49	mulla1	mulla1	PROPN
cuesj-900	1	50	,	,	PUNCT
cuesj-900	1	51	yıldırım	yıldırım	PROPN
cuesj-900	1	52	demir2	demir2	PROPN
cuesj-900	2	1	1department	1department	NUM
cuesj-900	2	2	of	of	ADP
cuesj-900	2	3	economic	economic	ADJ
cuesj-900	2	4	,	,	PUNCT
cuesj-900	2	5	faculty	faculty	NOUN
cuesj-900	2	6	of	of	ADP
cuesj-900	2	7	economics	economic	NOUN
cuesj-900	2	8	and	and	CCONJ
cuesj-900	2	9	administrations	administration	NOUN
cuesj-900	2	10	,	,	PUNCT
cuesj-900	2	11	nawroz	nawroz	ADV
cuesj-900	2	12	university	university	NOUN
cuesj-900	2	13	,	,	PUNCT
cuesj-900	2	14	kurdistan	kurdistan	PROPN
cuesj-900	2	15	region	region	NOUN
cuesj-900	2	16	,	,	PUNCT
cuesj-900	2	17	iraq	iraq	PROPN
cuesj-900	2	18	,	,	PUNCT
cuesj-900	2	19	2department	2department	NUM
cuesj-900	2	20	of	of	ADP
cuesj-900	2	21	statistics	statistic	NOUN
cuesj-900	2	22	,	,	PUNCT
cuesj-900	2	23	faculty	faculty	NOUN
cuesj-900	2	24	of	of	ADP
cuesj-900	2	25	economics	economic	NOUN
cuesj-900	2	26	and	and	CCONJ
cuesj-900	2	27	administrative	administrative	ADJ
cuesj-900	2	28	sciences	science	NOUN
cuesj-900	2	29	,	,	PUNCT
cuesj-900	2	30	van	van	PROPN
cuesj-900	2	31	yuzuncu	yuzuncu	PROPN
cuesj-900	2	32	yil	yil	PROPN
cuesj-900	2	33	university	university	PROPN
cuesj-900	2	34	,	,	PUNCT
cuesj-900	2	35	van	van	PROPN
cuesj-900	2	36	,	,	PUNCT
cuesj-900	2	37	turkey	turkey	NOUN
cuesj-900	2	38	abstract	abstract	NOUN
cuesj-900	2	39	in	in	ADP
cuesj-900	2	40	this	this	DET
cuesj-900	2	41	article	article	NOUN
cuesj-900	2	42	,	,	PUNCT
cuesj-900	2	43	two	two	NUM
cuesj-900	2	44	machine	machine	NOUN
cuesj-900	2	45	learning	learn	VERB
cuesj-900	2	46	methods	method	NOUN
cuesj-900	2	47	such	such	ADJ
cuesj-900	2	48	as	as	ADP
cuesj-900	2	49	classification	classification	NOUN
cuesj-900	2	50	and	and	CCONJ
cuesj-900	2	51	clustering	clustering	NOUN
cuesj-900	2	52	are	be	AUX
cuesj-900	2	53	used	use	VERB
cuesj-900	2	54	for	for	ADP
cuesj-900	2	55	decision	decision	NOUN
cuesj-900	2	56	tree	tree	NOUN
cuesj-900	2	57	(	(	PUNCT
cuesj-900	2	58	dt	dt	NOUN
cuesj-900	2	59	)	)	PUNCT
cuesj-900	2	60	,	,	PUNCT
cuesj-900	2	61	artificial	artificial	ADJ
cuesj-900	2	62	neural	neural	ADJ
cuesj-900	2	63	network	network	NOUN
cuesj-900	2	64	(	(	PUNCT
cuesj-900	2	65	ann	ann	PROPN
cuesj-900	2	66	)	)	PUNCT
cuesj-900	2	67	,	,	PUNCT
cuesj-900	2	68	and	and	CCONJ
cuesj-900	2	69	k	k	X
cuesj-900	2	70	-	-	PUNCT
cuesj-900	2	71	nearest	near	ADJ
cuesj-900	2	72	neighbors	neighbor	NOUN
cuesj-900	2	73	algorithms	algorithm	NOUN
cuesj-900	2	74	.	.	PUNCT
cuesj-900	3	1	the	the	DET
cuesj-900	3	2	datasets	dataset	NOUN
cuesj-900	3	3	were	be	AUX
cuesj-900	3	4	used	use	VERB
cuesj-900	3	5	to	to	PART
cuesj-900	3	6	evaluate	evaluate	VERB
cuesj-900	3	7	the	the	DET
cuesj-900	3	8	effectiveness	effectiveness	NOUN
cuesj-900	3	9	of	of	ADP
cuesj-900	3	10	the	the	DET
cuesj-900	3	11	clustering	clustering	ADJ
cuesj-900	3	12	method	method	NOUN
cuesj-900	3	13	and	and	CCONJ
cuesj-900	3	14	the	the	DET
cuesj-900	3	15	data	data	NOUN
cuesj-900	3	16	mining	mining	NOUN
cuesj-900	3	17	tool	tool	NOUN
cuesj-900	3	18	.	.	PUNCT
cuesj-900	4	1	weather	weather	NOUN
cuesj-900	4	2	data	datum	NOUN
cuesj-900	4	3	were	be	AUX
cuesj-900	4	4	used	use	VERB
cuesj-900	4	5	to	to	PART
cuesj-900	4	6	compare	compare	VERB
cuesj-900	4	7	algorithms	algorithm	NOUN
cuesj-900	4	8	and	and	CCONJ
cuesj-900	4	9	methods	method	NOUN
cuesj-900	4	10	in	in	ADP
cuesj-900	4	11	the	the	DET
cuesj-900	4	12	study	study	NOUN
cuesj-900	4	13	.	.	PUNCT
cuesj-900	5	1	this	this	DET
cuesj-900	5	2	study	study	NOUN
cuesj-900	5	3	showed	show	VERB
cuesj-900	5	4	that	that	SCONJ
cuesj-900	5	5	the	the	DET
cuesj-900	5	6	best	good	ADJ
cuesj-900	5	7	model	model	NOUN
cuesj-900	5	8	was	be	AUX
cuesj-900	5	9	dt	dt	VERB
cuesj-900	5	10	according	accord	VERB
cuesj-900	5	11	to	to	ADP
cuesj-900	5	12	accuracy	accuracy	NOUN
cuesj-900	5	13	and	and	CCONJ
cuesj-900	5	14	precision	precision	NOUN
cuesj-900	5	15	measures	measure	NOUN
cuesj-900	5	16	but	but	CCONJ
cuesj-900	5	17	the	the	DET
cuesj-900	5	18	best	good	ADJ
cuesj-900	5	19	model	model	NOUN
cuesj-900	5	20	according	accord	VERB
cuesj-900	5	21	to	to	ADP
cuesj-900	5	22	f	f	NOUN
cuesj-900	5	23	-	-	PUNCT
cuesj-900	5	24	measure	measure	NOUN
cuesj-900	5	25	and	and	CCONJ
cuesj-900	5	26	receiver	receiver	ADJ
cuesj-900	5	27	operating	operate	VERB
cuesj-900	5	28	characteristic	characteristic	ADJ
cuesj-900	5	29	curve	curve	NOUN
cuesj-900	5	30	area	area	NOUN
cuesj-900	5	31	measures	measure	NOUN
cuesj-900	5	32	was	be	AUX
cuesj-900	5	33	ann	ann	PROPN
cuesj-900	5	34	.	.	PUNCT
cuesj-900	5	35	waikato	waikato	PROPN
cuesj-900	5	36	environment	environment	NOUN
cuesj-900	5	37	for	for	ADP
cuesj-900	5	38	knowledge	knowledge	NOUN
cuesj-900	5	39	analysis	analysis	NOUN
cuesj-900	5	40	,	,	PUNCT
cuesj-900	5	41	a	a	DET
cuesj-900	5	42	data	data	NOUN
cuesj-900	5	43	mining	mining	NOUN
cuesj-900	5	44	tool	tool	NOUN
cuesj-900	5	45	,	,	PUNCT
cuesj-900	5	46	is	be	AUX
cuesj-900	5	47	utilized	utilize	VERB
cuesj-900	5	48	in	in	ADP
cuesj-900	5	49	this	this	DET
cuesj-900	5	50	paper	paper	NOUN
cuesj-900	5	51	to	to	PART
cuesj-900	5	52	carry	carry	VERB
cuesj-900	5	53	out	out	ADP
cuesj-900	5	54	the	the	DET
cuesj-900	5	55	clustering	clustering	NOUN
cuesj-900	5	56	.	.	PUNCT
cuesj-900	6	1	keywords	keyword	NOUN
cuesj-900	6	2	:	:	PUNCT
cuesj-900	6	3	algorithms	algorithm	NOUN
cuesj-900	6	4	,	,	PUNCT
cuesj-900	6	5	classification	classification	NOUN
cuesj-900	6	6	,	,	PUNCT
cuesj-900	6	7	clustering	clustering	NOUN
cuesj-900	6	8	,	,	PUNCT
cuesj-900	6	9	machine	machine	NOUN
cuesj-900	6	10	learning	learning	NOUN
cuesj-900	6	11	,	,	PUNCT
cuesj-900	6	12	decisions	decision	VERB
cuesj-900	6	13	tree	tree	NOUN
cuesj-900	6	14	introduction	introduction	NOUN
cuesj-900	6	15	data	datum	NOUN
cuesj-900	6	16	analysis	analysis	NOUN
cuesj-900	6	17	science	science	NOUN
cuesj-900	6	18	is	be	AUX
cuesj-900	6	19	one	one	NUM
cuesj-900	6	20	of	of	ADP
cuesj-900	6	21	the	the	DET
cuesj-900	6	22	disciplines	discipline	NOUN
cuesj-900	6	23	that	that	PRON
cuesj-900	6	24	was	be	AUX
cuesj-900	6	25	created	create	VERB
cuesj-900	6	26	to	to	PART
cuesj-900	6	27	investigate	investigate	VERB
cuesj-900	6	28	natural	natural	ADJ
cuesj-900	6	29	phenomena	phenomenon	NOUN
cuesj-900	6	30	and	and	CCONJ
cuesj-900	6	31	address	address	VERB
cuesj-900	6	32	current	current	ADJ
cuesj-900	6	33	or	or	CCONJ
cuesj-900	6	34	upcoming	upcoming	ADJ
cuesj-900	6	35	issues	issue	NOUN
cuesj-900	6	36	.	.	PUNCT
cuesj-900	7	1	data	datum	NOUN
cuesj-900	7	2	analysis	analysis	NOUN
cuesj-900	7	3	science	science	NOUN
cuesj-900	7	4	aims	aim	VERB
cuesj-900	7	5	to	to	PART
cuesj-900	7	6	shed	shed	VERB
cuesj-900	7	7	light	light	NOUN
cuesj-900	7	8	on	on	ADP
cuesj-900	7	9	future	future	ADJ
cuesj-900	7	10	studies	study	NOUN
cuesj-900	7	11	by	by	ADP
cuesj-900	7	12	employing	employ	VERB
cuesj-900	7	13	techniques	technique	NOUN
cuesj-900	7	14	and	and	CCONJ
cuesj-900	7	15	theories	theory	NOUN
cuesj-900	7	16	that	that	PRON
cuesj-900	7	17	are	be	AUX
cuesj-900	7	18	appropriate	appropriate	ADJ
cuesj-900	7	19	for	for	ADP
cuesj-900	7	20	data	data	NOUN
cuesj-900	7	21	structures	structure	NOUN
cuesj-900	7	22	with	with	ADP
cuesj-900	7	23	a	a	DET
cuesj-900	7	24	small	small	ADJ
cuesj-900	7	25	number	number	NOUN
cuesj-900	7	26	of	of	ADP
cuesj-900	7	27	observations	observation	NOUN
cuesj-900	7	28	to	to	PART
cuesj-900	7	29	describe	describe	VERB
cuesj-900	7	30	the	the	DET
cuesj-900	7	31	subject	subject	NOUN
cuesj-900	7	32	with	with	ADP
cuesj-900	7	33	a	a	DET
cuesj-900	7	34	certain	certain	ADJ
cuesj-900	7	35	probability	probability	NOUN
cuesj-900	7	36	.	.	PUNCT
cuesj-900	8	1	every	every	DET
cuesj-900	8	2	science	science	NOUN
cuesj-900	8	3	discipline	discipline	NOUN
cuesj-900	8	4	in	in	ADP
cuesj-900	8	5	nature	nature	NOUN
cuesj-900	8	6	has	have	VERB
cuesj-900	8	7	a	a	DET
cuesj-900	8	8	lot	lot	NOUN
cuesj-900	8	9	of	of	ADP
cuesj-900	8	10	data	datum	NOUN
cuesj-900	8	11	,	,	PUNCT
cuesj-900	8	12	and	and	CCONJ
cuesj-900	8	13	access	access	NOUN
cuesj-900	8	14	to	to	ADP
cuesj-900	8	15	these	these	DET
cuesj-900	8	16	data	datum	NOUN
cuesj-900	8	17	is	be	AUX
cuesj-900	8	18	becoming	become	VERB
cuesj-900	8	19	simpler	simple	ADJ
cuesj-900	8	20	every	every	DET
cuesj-900	8	21	day	day	NOUN
cuesj-900	8	22	.	.	PUNCT
cuesj-900	9	1	it	it	PRON
cuesj-900	9	2	always	always	ADV
cuesj-900	9	3	raises	raise	VERB
cuesj-900	9	4	the	the	DET
cuesj-900	9	5	question	question	NOUN
cuesj-900	9	6	of	of	ADP
cuesj-900	9	7	how	how	SCONJ
cuesj-900	9	8	much	much	ADJ
cuesj-900	9	9	of	of	ADP
cuesj-900	9	10	the	the	DET
cuesj-900	9	11	collected	collect	VERB
cuesj-900	9	12	data	datum	NOUN
cuesj-900	9	13	can	can	AUX
cuesj-900	9	14	be	be	AUX
cuesj-900	9	15	used	use	VERB
cuesj-900	9	16	or	or	CCONJ
cuesj-900	9	17	how	how	SCONJ
cuesj-900	9	18	significant	significant	ADJ
cuesj-900	9	19	it	it	PRON
cuesj-900	9	20	is	be	AUX
cuesj-900	9	21	.	.	PUNCT
cuesj-900	10	1	while	while	SCONJ
cuesj-900	10	2	going	go	VERB
cuesj-900	10	3	about	about	ADP
cuesj-900	10	4	their	their	PRON
cuesj-900	10	5	regular	regular	ADJ
cuesj-900	10	6	lives	life	NOUN
cuesj-900	10	7	,	,	PUNCT
cuesj-900	10	8	humans	human	NOUN
cuesj-900	10	9	not	not	PART
cuesj-900	10	10	only	only	ADV
cuesj-900	10	11	receive	receive	VERB
cuesj-900	10	12	information	information	NOUN
cuesj-900	10	13	but	but	CCONJ
cuesj-900	10	14	also	also	ADV
cuesj-900	10	15	consume	consume	VERB
cuesj-900	10	16	it	it	PRON
cuesj-900	10	17	.	.	PUNCT
cuesj-900	11	1	due	due	ADP
cuesj-900	11	2	to	to	ADP
cuesj-900	11	3	the	the	DET
cuesj-900	11	4	extensive	extensive	ADJ
cuesj-900	11	5	use	use	NOUN
cuesj-900	11	6	of	of	ADP
cuesj-900	11	7	data	datum	NOUN
cuesj-900	11	8	,	,	PUNCT
cuesj-900	11	9	classification	classification	NOUN
cuesj-900	11	10	was	be	AUX
cuesj-900	11	11	required	require	VERB
cuesj-900	11	12	to	to	PART
cuesj-900	11	13	make	make	VERB
cuesj-900	11	14	the	the	DET
cuesj-900	11	15	data	datum	NOUN
cuesj-900	11	16	meaningful	meaningful	ADJ
cuesj-900	11	17	and	and	CCONJ
cuesj-900	11	18	produce	produce	VERB
cuesj-900	11	19	new	new	ADJ
cuesj-900	11	20	data	datum	NOUN
cuesj-900	11	21	.	.	PUNCT
cuesj-900	12	1	a	a	DET
cuesj-900	12	2	set	set	NOUN
cuesj-900	12	3	of	of	ADP
cuesj-900	12	4	data	datum	NOUN
cuesj-900	12	5	is	be	AUX
cuesj-900	12	6	classified	classify	VERB
cuesj-900	12	7	when	when	SCONJ
cuesj-900	12	8	it	it	PRON
cuesj-900	12	9	is	be	AUX
cuesj-900	12	10	categorized	categorize	VERB
cuesj-900	12	11	based	base	VERB
cuesj-900	12	12	on	on	ADP
cuesj-900	12	13	algorithmdetermined	algorithmdetermined	ADJ
cuesj-900	12	14	distinguishing	distinguish	VERB
cuesj-900	12	15	characteristics	characteristic	NOUN
cuesj-900	12	16	.	.	PUNCT
cuesj-900	13	1	data	datum	NOUN
cuesj-900	13	2	can	can	AUX
cuesj-900	13	3	not	not	PART
cuesj-900	13	4	always	always	ADV
cuesj-900	13	5	be	be	AUX
cuesj-900	13	6	categorized	categorize	VERB
cuesj-900	13	7	manually	manually	ADV
cuesj-900	13	8	because	because	SCONJ
cuesj-900	13	9	millions	million	NOUN
cuesj-900	13	10	of	of	ADP
cuesj-900	13	11	data	datum	NOUN
cuesj-900	13	12	types	type	NOUN
cuesj-900	13	13	related	relate	VERB
cuesj-900	13	14	to	to	ADP
cuesj-900	13	15	a	a	DET
cuesj-900	13	16	field	field	NOUN
cuesj-900	13	17	are	be	AUX
cuesj-900	13	18	created	create	VERB
cuesj-900	13	19	in	in	ADP
cuesj-900	13	20	just	just	ADV
cuesj-900	13	21	1	1	NUM
cuesj-900	13	22	day	day	NOUN
cuesj-900	13	23	.	.	PUNCT
cuesj-900	14	1	the	the	DET
cuesj-900	14	2	extremely	extremely	ADV
cuesj-900	14	3	convenient	convenient	ADJ
cuesj-900	14	4	operation	operation	NOUN
cuesj-900	14	5	of	of	ADP
cuesj-900	14	6	the	the	DET
cuesj-900	14	7	algorithms	algorithms	NOUN
cuesj-900	14	8	makes	make	VERB
cuesj-900	14	9	the	the	DET
cuesj-900	14	10	classification	classification	NOUN
cuesj-900	14	11	of	of	ADP
cuesj-900	14	12	data	datum	NOUN
cuesj-900	14	13	very	very	ADV
cuesj-900	14	14	easy	easy	ADV
cuesj-900	14	15	.	.	PUNCT
cuesj-900	15	1	the	the	DET
cuesj-900	15	2	classification	classification	NOUN
cuesj-900	15	3	of	of	ADP
cuesj-900	15	4	data	datum	NOUN
cuesj-900	15	5	using	use	VERB
cuesj-900	15	6	many	many	ADJ
cuesj-900	15	7	algorithms	algorithm	NOUN
cuesj-900	15	8	simultaneously	simultaneously	ADV
cuesj-900	15	9	can	can	AUX
cuesj-900	15	10	result	result	VERB
cuesj-900	15	11	in	in	ADP
cuesj-900	15	12	a	a	DET
cuesj-900	15	13	variety	variety	NOUN
cuesj-900	15	14	of	of	ADP
cuesj-900	15	15	outcomes	outcome	NOUN
cuesj-900	15	16	.	.	PUNCT
cuesj-900	16	1	which	which	DET
cuesj-900	16	2	algorithm	algorithm	NOUN
cuesj-900	16	3	is	be	AUX
cuesj-900	16	4	used	use	VERB
cuesj-900	16	5	to	to	PART
cuesj-900	16	6	classify	classify	VERB
cuesj-900	16	7	the	the	DET
cuesj-900	16	8	data	datum	NOUN
cuesj-900	16	9	set	set	VERB
cuesj-900	16	10	effectively	effectively	ADV
cuesj-900	16	11	relies	rely	VERB
cuesj-900	16	12	on	on	ADP
cuesj-900	16	13	the	the	DET
cuesj-900	16	14	data	datum	NOUN
cuesj-900	16	15	set	set	VERB
cuesj-900	16	16	.	.	PUNCT
cuesj-900	17	1	since	since	SCONJ
cuesj-900	17	2	not	not	PART
cuesj-900	17	3	all	all	DET
cuesj-900	17	4	algorithms	algorithm	NOUN
cuesj-900	17	5	will	will	AUX
cuesj-900	17	6	give	give	VERB
cuesj-900	17	7	the	the	DET
cuesj-900	17	8	same	same	ADJ
cuesj-900	17	9	accuracy	accuracy	NOUN
cuesj-900	17	10	to	to	ADP
cuesj-900	17	11	every	every	DET
cuesj-900	17	12	data	datum	NOUN
cuesj-900	17	13	set	set	VERB
cuesj-900	17	14	,	,	PUNCT
cuesj-900	17	15	it	it	PRON
cuesj-900	17	16	is	be	AUX
cuesj-900	17	17	possible	possible	ADJ
cuesj-900	17	18	that	that	SCONJ
cuesj-900	17	19	the	the	DET
cuesj-900	17	20	techniques	technique	NOUN
cuesj-900	17	21	employed	employ	VERB
cuesj-900	17	22	to	to	PART
cuesj-900	17	23	describe	describe	VERB
cuesj-900	17	24	the	the	DET
cuesj-900	17	25	data	datum	NOUN
cuesj-900	17	26	will	will	AUX
cuesj-900	17	27	also	also	ADV
cuesj-900	17	28	be	be	AUX
cuesj-900	17	29	erroneous	erroneous	ADJ
cuesj-900	17	30	for	for	ADP
cuesj-900	17	31	that	that	DET
cuesj-900	17	32	data	datum	NOUN
cuesj-900	17	33	set	set	VERB
cuesj-900	17	34	.	.	PUNCT
cuesj-900	18	1	machine	machine	NOUN
cuesj-900	18	2	learning	learning	NOUN
cuesj-900	18	3	and	and	CCONJ
cuesj-900	18	4	classification	classification	NOUN
cuesj-900	18	5	algorithms	algorithm	NOUN
cuesj-900	18	6	are	be	AUX
cuesj-900	18	7	prominent	prominent	ADJ
cuesj-900	18	8	in	in	ADP
cuesj-900	18	9	this	this	DET
cuesj-900	18	10	direction.[1	direction.[1	NOUN
cuesj-900	18	11	]	]	PUNCT
cuesj-900	18	12	data	datum	NOUN
cuesj-900	18	13	analysis	analysis	NOUN
cuesj-900	18	14	was	be	AUX
cuesj-900	18	15	done	do	VERB
cuesj-900	18	16	using	use	VERB
cuesj-900	18	17	the	the	DET
cuesj-900	18	18	open	open	ADJ
cuesj-900	18	19	-	-	PUNCT
cuesj-900	18	20	source	source	NOUN
cuesj-900	18	21	waikato	waikato	NOUN
cuesj-900	18	22	environment	environment	NOUN
cuesj-900	18	23	for	for	ADP
cuesj-900	18	24	knowledge	knowledge	NOUN
cuesj-900	18	25	analysis	analysis	NOUN
cuesj-900	18	26	(	(	PUNCT
cuesj-900	18	27	weka	weka	PROPN
cuesj-900	18	28	)	)	PUNCT
cuesj-900	18	29	program	program	NOUN
cuesj-900	18	30	,	,	PUNCT
cuesj-900	18	31	which	which	PRON
cuesj-900	18	32	is	be	AUX
cuesj-900	18	33	a	a	DET
cuesj-900	18	34	tried	try	VERB
cuesj-900	18	35	-	-	PUNCT
cuesj-900	18	36	and	and	CCONJ
cuesj-900	18	37	-	-	PUNCT
cuesj-900	18	38	true	true	ADJ
cuesj-900	18	39	data	datum	NOUN
cuesj-900	18	40	mining	mining	NOUN
cuesj-900	18	41	tool	tool	NOUN
cuesj-900	18	42	with	with	ADP
cuesj-900	18	43	a	a	DET
cuesj-900	18	44	graphical	graphical	ADJ
cuesj-900	18	45	user	user	NOUN
cuesj-900	18	46	interface	interface	NOUN
cuesj-900	18	47	and	and	CCONJ
cuesj-900	18	48	regular	regular	ADJ
cuesj-900	18	49	terminal	terminal	ADJ
cuesj-900	18	50	programs	program	NOUN
cuesj-900	18	51	.	.	PUNCT
cuesj-900	19	1	this	this	DET
cuesj-900	19	2	program	program	NOUN
cuesj-900	19	3	has	have	AUX
cuesj-900	19	4	helped	help	VERB
cuesj-900	19	5	to	to	PART
cuesj-900	19	6	classify	classify	VERB
cuesj-900	19	7	the	the	DET
cuesj-900	19	8	data	datum	NOUN
cuesj-900	19	9	by	by	ADP
cuesj-900	19	10	rebalancing	rebalancing	NOUN
cuesj-900	19	11	and	and	CCONJ
cuesj-900	19	12	decreasing	decrease	VERB
cuesj-900	19	13	its	its	PRON
cuesj-900	19	14	dimension	dimension	NOUN
cuesj-900	19	15	.	.	PUNCT
cuesj-900	20	1	the	the	DET
cuesj-900	20	2	weka	weka	PROPN
cuesj-900	20	3	project	project	NOUN
cuesj-900	20	4	seeks	seek	VERB
cuesj-900	20	5	to	to	PART
cuesj-900	20	6	offer	offer	VERB
cuesj-900	20	7	a	a	DET
cuesj-900	20	8	full	full	ADJ
cuesj-900	20	9	range	range	NOUN
cuesj-900	20	10	of	of	ADP
cuesj-900	20	11	machine	machine	NOUN
cuesj-900	20	12	learning	learning	NOUN
cuesj-900	20	13	tools	tool	NOUN
cuesj-900	20	14	and	and	CCONJ
cuesj-900	20	15	preprocessing	preprocesse	VERB
cuesj-900	20	16	methods	method	NOUN
cuesj-900	20	17	to	to	ADP
cuesj-900	20	18	academics	academic	NOUN
cuesj-900	20	19	.	.	PUNCT
cuesj-900	21	1	this	this	PRON
cuesj-900	21	2	allows	allow	VERB
cuesj-900	21	3	applications	application	NOUN
cuesj-900	21	4	to	to	PART
cuesj-900	21	5	rapidly	rapidly	ADV
cuesj-900	21	6	test	test	VERB
cuesj-900	21	7	and	and	CCONJ
cuesj-900	21	8	contrast	contrast	VERB
cuesj-900	21	9	various	various	ADJ
cuesj-900	21	10	machine	machine	NOUN
cuesj-900	21	11	learning	learning	NOUN
cuesj-900	21	12	process	process	NOUN
cuesj-900	21	13	strategies	strategy	NOUN
cuesj-900	21	14	on	on	ADP
cuesj-900	21	15	new	new	ADJ
cuesj-900	21	16	datasets.[2	datasets.[2	X
cuesj-900	21	17	]	]	PUNCT
cuesj-900	21	18	literature	literature	NOUN
cuesj-900	21	19	review	review	NOUN
cuesj-900	21	20	in	in	ADP
cuesj-900	21	21	soofi	soofi	ADJ
cuesj-900	21	22	and	and	CCONJ
cuesj-900	21	23	awan,[3	awan,[3	NUM
cuesj-900	21	24	]	]	PUNCT
cuesj-900	21	25	discussed	discuss	VERB
cuesj-900	21	26	the	the	DET
cuesj-900	21	27	fundamental	fundamental	ADJ
cuesj-900	21	28	classification	classification	NOUN
cuesj-900	21	29	strategies	strategy	NOUN
cuesj-900	21	30	in	in	ADP
cuesj-900	21	31	this	this	DET
cuesj-900	21	32	paper	paper	NOUN
cuesj-900	21	33	.	.	PUNCT
cuesj-900	22	1	later	later	ADV
cuesj-900	22	2	,	,	PUNCT
cuesj-900	22	3	he	he	PRON
cuesj-900	22	4	covered	cover	VERB
cuesj-900	22	5	some	some	PRON
cuesj-900	22	6	of	of	ADP
cuesj-900	22	7	the	the	DET
cuesj-900	22	8	main	main	ADJ
cuesj-900	22	9	classification	classification	NOUN
cuesj-900	22	10	technique	technique	NOUN
cuesj-900	22	11	subcategories	subcategorie	NOUN
cuesj-900	22	12	,	,	PUNCT
cuesj-900	22	13	including	include	VERB
cuesj-900	22	14	bayesian	bayesian	NOUN
cuesj-900	22	15	networks	network	NOUN
cuesj-900	22	16	,	,	PUNCT
cuesj-900	22	17	decision	decision	NOUN
cuesj-900	22	18	tree	tree	NOUN
cuesj-900	22	19	(	(	PUNCT
cuesj-900	22	20	dt	dt	PROPN
cuesj-900	22	21	)	)	PUNCT
cuesj-900	22	22	,	,	PUNCT
cuesj-900	22	23	k	k	X
cuesj-900	22	24	-	-	PUNCT
cuesj-900	22	25	nearest	near	ADJ
cuesj-900	22	26	neighbor	neighbor	NOUN
cuesj-900	22	27	(	(	PUNCT
cuesj-900	22	28	knn	knn	PROPN
cuesj-900	22	29	)	)	PUNCT
cuesj-900	22	30	,	,	PUNCT
cuesj-900	22	31	and	and	CCONJ
cuesj-900	22	32	support	support	VERB
cuesj-900	22	33	vector	vector	NOUN
cuesj-900	22	34	machine	machine	NOUN
cuesj-900	22	35	(	(	PUNCT
cuesj-900	22	36	svm	svm	PROPN
cuesj-900	22	37	)	)	PUNCT
cuesj-900	22	38	,	,	PUNCT
cuesj-900	22	39	along	along	ADP
cuesj-900	22	40	with	with	ADP
cuesj-900	22	41	their	their	PRON
cuesj-900	22	42	advantages	advantage	NOUN
cuesj-900	22	43	,	,	PUNCT
cuesj-900	22	44	disadvantages	disadvantage	NOUN
cuesj-900	22	45	,	,	PUNCT
cuesj-900	22	46	prospective	prospective	ADJ
cuesj-900	22	47	applications	application	NOUN
cuesj-900	22	48	,	,	PUNCT
cuesj-900	22	49	problems	problem	NOUN
cuesj-900	22	50	,	,	PUNCT
cuesj-900	22	51	and	and	CCONJ
cuesj-900	22	52	potential	potential	ADJ
cuesj-900	22	53	solutions	solution	NOUN
cuesj-900	22	54	.	.	PUNCT
cuesj-900	23	1	this	this	DET
cuesj-900	23	2	study	study	NOUN
cuesj-900	23	3	’s	’s	PART
cuesj-900	23	4	objective	objective	NOUN
cuesj-900	23	5	is	be	AUX
cuesj-900	23	6	to	to	PART
cuesj-900	23	7	offer	offer	VERB
cuesj-900	23	8	a	a	DET
cuesj-900	23	9	thorough	thorough	ADJ
cuesj-900	23	10	evaluation	evaluation	NOUN
cuesj-900	23	11	of	of	ADP
cuesj-900	23	12	several	several	ADJ
cuesj-900	23	13	machine	machine	NOUN
cuesj-900	23	14	learning	learn	VERB
cuesj-900	23	15	classification	classification	NOUN
cuesj-900	23	16	methods	method	NOUN
cuesj-900	23	17	.	.	PUNCT
cuesj-900	24	1	cihan	cihan	VERB
cuesj-900	24	2	university	university	NOUN
cuesj-900	24	3	-	-	PUNCT
cuesj-900	24	4	erbil	erbil	PROPN
cuesj-900	24	5	scientific	scientific	ADJ
cuesj-900	24	6	journal	journal	NOUN
cuesj-900	24	7	(	(	PUNCT
cuesj-900	24	8	cuesj	cuesj	PROPN
cuesj-900	24	9	)	)	PUNCT
cuesj-900	24	10	corresponding	correspond	VERB
cuesj-900	24	11	author	author	NOUN
cuesj-900	24	12	:	:	PUNCT
cuesj-900	24	13	guhdar	guhdar	PROPN
cuesj-900	24	14	a.	a.	PROPN
cuesj-900	24	15	ahmed	ahmed	PROPN
cuesj-900	24	16	,	,	PUNCT
cuesj-900	24	17	department	department	NOUN
cuesj-900	24	18	of	of	ADP
cuesj-900	24	19	economic	economic	ADJ
cuesj-900	24	20	,	,	PUNCT
cuesj-900	24	21	faculty	faculty	NOUN
cuesj-900	24	22	of	of	ADP
cuesj-900	24	23	economics	economic	NOUN
cuesj-900	24	24	and	and	CCONJ
cuesj-900	24	25	administrations	administration	NOUN
cuesj-900	24	26	,	,	PUNCT
cuesj-900	24	27	nawroz	nawroz	ADV
cuesj-900	24	28	university	university	NOUN
cuesj-900	24	29	,	,	PUNCT
cuesj-900	24	30	kurdistan	kurdistan	PROPN
cuesj-900	24	31	region	region	NOUN
cuesj-900	24	32	,	,	PUNCT
cuesj-900	24	33	iraq	iraq	PROPN
cuesj-900	24	34	.	.	PUNCT
cuesj-900	25	1	e	e	X
cuesj-900	25	2	-	-	NOUN
cuesj-900	25	3	mail	mail	NOUN
cuesj-900	25	4	:	:	PUNCT
cuesj-900	25	5	guhdar.abdulaziz@gmail.com	guhdar.abdulaziz@gmail.com	X
cuesj-900	25	6	received	receive	VERB
cuesj-900	25	7	:	:	PUNCT
cuesj-900	25	8	march	march	PROPN
cuesj-900	25	9	3	3	NUM
cuesj-900	25	10	,	,	PUNCT
cuesj-900	25	11	2023	2023	NUM
cuesj-900	25	12	accepted	accept	VERB
cuesj-900	25	13	:	:	PUNCT
cuesj-900	25	14	may	may	AUX
cuesj-900	25	15	19	19	NUM
cuesj-900	25	16	,	,	PUNCT
cuesj-900	25	17	2023	2023	NUM
cuesj-900	25	18	published	publish	VERB
cuesj-900	25	19	:	:	PUNCT
cuesj-900	25	20	june	june	PROPN
cuesj-900	25	21	10	10	NUM
cuesj-900	25	22	,	,	PUNCT
cuesj-900	25	23	2023	2023	NUM
cuesj-900	25	24	doi	doi	NOUN
cuesj-900	25	25	:	:	PUNCT
cuesj-900	25	26	10.24086	10.24086	NUM
cuesj-900	25	27	/	/	SYM
cuesj-900	25	28	cuesj.v7n1y2023.pp52	cuesj.v7n1y2023.pp52	PROPN
cuesj-900	25	29	-	-	PUNCT
cuesj-900	25	30	59	59	NUM
cuesj-900	25	31	copyright	copyright	NOUN
cuesj-900	25	32	©	©	PROPN
cuesj-900	25	33	2023	2023	NUM
cuesj-900	25	34	guhdar	guhdar	PROPN
cuesj-900	25	35	a.	a.	PROPN
cuesj-900	25	36	ahmed	ahmed	PROPN
cuesj-900	25	37	,	,	PUNCT
cuesj-900	25	38	yıldırım	yıldırım	PROPN
cuesj-900	25	39	demir	demir	PROPN
cuesj-900	25	40	.	.	PUNCT
cuesj-900	26	1	this	this	PRON
cuesj-900	26	2	is	be	AUX
cuesj-900	26	3	an	an	DET
cuesj-900	26	4	open	open	ADJ
cuesj-900	26	5	-	-	PUNCT
cuesj-900	26	6	access	access	NOUN
cuesj-900	26	7	article	article	NOUN
cuesj-900	26	8	distributed	distribute	VERB
cuesj-900	26	9	under	under	ADP
cuesj-900	26	10	the	the	DET
cuesj-900	26	11	creative	creative	ADJ
cuesj-900	26	12	commons	common	NOUN
cuesj-900	26	13	attribution	attribution	NOUN
cuesj-900	26	14	license	license	NOUN
cuesj-900	26	15	(	(	PUNCT
cuesj-900	26	16	cc	cc	NOUN
cuesj-900	26	17	by	by	ADP
cuesj-900	26	18	-	-	PUNCT
cuesj-900	26	19	nc	nc	VERB
cuesj-900	26	20	-	-	PROPN
cuesj-900	26	21	nd	nd	PRON
cuesj-900	26	22	4.0	4.0	NUM
cuesj-900	26	23	)	)	PUNCT
cuesj-900	26	24	cihan	cihan	VERB
cuesj-900	26	25	university	university	NOUN
cuesj-900	26	26	-	-	PUNCT
cuesj-900	26	27	erbil	erbil	PROPN
cuesj-900	26	28	scientific	scientific	ADJ
cuesj-900	26	29	journal	journal	NOUN
cuesj-900	26	30	(	(	PUNCT
cuesj-900	26	31	cuesj	cuesj	PROPN
cuesj-900	26	32	)	)	PUNCT
cuesj-900	26	33	mulla	mulla	NOUN
cuesj-900	26	34	and	and	CCONJ
cuesj-900	26	35	demir	demir	PROPN
cuesj-900	26	36	:	:	PUNCT
cuesj-900	26	37	the	the	DET
cuesj-900	26	38	use	use	NOUN
cuesj-900	26	39	of	of	ADP
cuesj-900	26	40	clustering	clustering	NOUN
cuesj-900	26	41	and	and	CCONJ
cuesj-900	26	42	classification	classification	NOUN
cuesj-900	26	43	methods	method	NOUN
cuesj-900	26	44	in	in	ADP
cuesj-900	26	45	machine	machine	NOUN
cuesj-900	26	46	learning	learn	VERB
cuesj-900	26	47	53	53	NUM
cuesj-900	26	48	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-900	26	49	cuesj	cuesj	NOUN
cuesj-900	26	50	2023	2023	NUM
cuesj-900	26	51	,	,	PUNCT
cuesj-900	26	52	7	7	NUM
cuesj-900	26	53	(	(	PUNCT
cuesj-900	26	54	1	1	NUM
cuesj-900	26	55	):	):	PUNCT
cuesj-900	26	56	52	52	NUM
cuesj-900	26	57	-	-	SYM
cuesj-900	26	58	59	59	NUM
cuesj-900	26	59	both	both	CCONJ
cuesj-900	26	60	experts	expert	NOUN
cuesj-900	26	61	and	and	CCONJ
cuesj-900	26	62	newbies	newbie	NOUN
cuesj-900	26	63	to	to	ADP
cuesj-900	26	64	the	the	DET
cuesj-900	26	65	field	field	NOUN
cuesj-900	26	66	of	of	ADP
cuesj-900	26	67	machine	machine	NOUN
cuesj-900	26	68	learning	learning	NOUN
cuesj-900	26	69	will	will	AUX
cuesj-900	26	70	benefit	benefit	VERB
cuesj-900	26	71	from	from	ADP
cuesj-900	26	72	this	this	DET
cuesj-900	26	73	research	research	NOUN
cuesj-900	26	74	’s	’s	PART
cuesj-900	26	75	efforts	effort	NOUN
cuesj-900	26	76	to	to	PART
cuesj-900	26	77	strengthen	strengthen	VERB
cuesj-900	26	78	the	the	DET
cuesj-900	26	79	conceptual	conceptual	ADJ
cuesj-900	26	80	underpinnings	underpinning	NOUN
cuesj-900	26	81	of	of	ADP
cuesj-900	26	82	classification	classification	NOUN
cuesj-900	26	83	techniques	technique	NOUN
cuesj-900	26	84	.	.	PUNCT
cuesj-900	27	1	in	in	ADP
cuesj-900	27	2	this	this	DET
cuesj-900	27	3	study	study	NOUN
cuesj-900	27	4	sharma,[4	sharma,[4	NUM
cuesj-900	27	5	]	]	PUNCT
cuesj-900	27	6	the	the	DET
cuesj-900	27	7	many	many	ADJ
cuesj-900	27	8	kinds	kind	NOUN
cuesj-900	27	9	of	of	ADP
cuesj-900	27	10	existing	exist	VERB
cuesj-900	27	11	categorization	categorization	NOUN
cuesj-900	27	12	algorithms	algorithm	NOUN
cuesj-900	27	13	were	be	AUX
cuesj-900	27	14	thoroughly	thoroughly	ADV
cuesj-900	27	15	explained	explain	VERB
cuesj-900	27	16	in	in	ADP
cuesj-900	27	17	this	this	DET
cuesj-900	27	18	work	work	NOUN
cuesj-900	27	19	.	.	PUNCT
cuesj-900	28	1	sharma	sharma	PROPN
cuesj-900	28	2	will	will	AUX
cuesj-900	28	3	mostly	mostly	ADV
cuesj-900	28	4	compare	compare	VERB
cuesj-900	28	5	and	and	CCONJ
cuesj-900	28	6	discuss	discuss	VERB
cuesj-900	28	7	in	in	ADP
cuesj-900	28	8	-	-	PUNCT
cuesj-900	28	9	depth	depth	NOUN
cuesj-900	28	10	the	the	DET
cuesj-900	28	11	key	key	ADJ
cuesj-900	28	12	7	7	NUM
cuesj-900	28	13	categories	category	NOUN
cuesj-900	28	14	of	of	ADP
cuesj-900	28	15	categorization	categorization	NOUN
cuesj-900	28	16	algorithms	algorithm	NOUN
cuesj-900	28	17	here	here	ADV
cuesj-900	28	18	.	.	PUNCT
cuesj-900	29	1	the	the	DET
cuesj-900	29	2	comparison	comparison	NOUN
cuesj-900	29	3	will	will	AUX
cuesj-900	29	4	mostly	mostly	ADV
cuesj-900	29	5	be	be	AUX
cuesj-900	29	6	focused	focus	VERB
cuesj-900	29	7	on	on	ADP
cuesj-900	29	8	each	each	DET
cuesj-900	29	9	system	system	NOUN
cuesj-900	29	10	’s	’s	PART
cuesj-900	29	11	assumptions	assumption	NOUN
cuesj-900	29	12	,	,	PUNCT
cuesj-900	29	13	benefits	benefit	NOUN
cuesj-900	29	14	,	,	PUNCT
cuesj-900	29	15	and	and	CCONJ
cuesj-900	29	16	disadvantages	disadvantage	NOUN
cuesj-900	29	17	.	.	PUNCT
cuesj-900	30	1	sharma	sharma	PROPN
cuesj-900	30	2	will	will	AUX
cuesj-900	30	3	address	address	VERB
cuesj-900	30	4	some	some	PRON
cuesj-900	30	5	of	of	ADP
cuesj-900	30	6	the	the	DET
cuesj-900	30	7	most	most	ADV
cuesj-900	30	8	popular	popular	ADJ
cuesj-900	30	9	classification	classification	NOUN
cuesj-900	30	10	algorithms	algorithm	NOUN
cuesj-900	30	11	used	use	VERB
cuesj-900	30	12	,	,	PUNCT
cuesj-900	30	13	different	different	ADJ
cuesj-900	30	14	scoring	scoring	NOUN
cuesj-900	30	15	schemes	scheme	NOUN
cuesj-900	30	16	to	to	PART
cuesj-900	30	17	gauge	gauge	VERB
cuesj-900	30	18	their	their	PRON
cuesj-900	30	19	effectiveness	effectiveness	NOUN
cuesj-900	30	20	,	,	PUNCT
cuesj-900	30	21	multiclass	multiclass	ADJ
cuesj-900	30	22	classification	classification	NOUN
cuesj-900	30	23	strategies	strategy	NOUN
cuesj-900	30	24	,	,	PUNCT
cuesj-900	30	25	and	and	CCONJ
cuesj-900	30	26	finally	finally	ADV
cuesj-900	30	27	model	model	VERB
cuesj-900	30	28	selection	selection	NOUN
cuesj-900	30	29	techniques	technique	NOUN
cuesj-900	30	30	in	in	ADP
cuesj-900	30	31	this	this	DET
cuesj-900	30	32	paper	paper	NOUN
cuesj-900	30	33	.	.	PUNCT
cuesj-900	31	1	the	the	DET
cuesj-900	31	2	study	study	NOUN
cuesj-900	31	3	sisodia	sisodia	NOUN
cuesj-900	31	4	and	and	CCONJ
cuesj-900	31	5	sisodia,[5	sisodia,[5	NUM
cuesj-900	31	6	]	]	PUNCT
cuesj-900	31	7	have	have	AUX
cuesj-900	31	8	covered	cover	VERB
cuesj-900	31	9	the	the	DET
cuesj-900	31	10	prediction	prediction	NOUN
cuesj-900	31	11	of	of	ADP
cuesj-900	31	12	diabetes	diabetes	NOUN
cuesj-900	31	13	using	use	VERB
cuesj-900	31	14	nave	nave	NOUN
cuesj-900	31	15	bayes	bayes	PROPN
cuesj-900	31	16	,	,	PUNCT
cuesj-900	31	17	dts	dts	NOUN
cuesj-900	31	18	,	,	PUNCT
cuesj-900	31	19	and	and	CCONJ
cuesj-900	31	20	svms	svms	NOUN
cuesj-900	31	21	as	as	ADP
cuesj-900	31	22	classification	classification	NOUN
cuesj-900	31	23	algorithms	algorithm	NOUN
cuesj-900	31	24	.	.	PUNCT
cuesj-900	32	1	the	the	DET
cuesj-900	32	2	major	major	ADJ
cuesj-900	32	3	goal	goal	NOUN
cuesj-900	32	4	of	of	ADP
cuesj-900	32	5	this	this	DET
cuesj-900	32	6	study	study	NOUN
cuesj-900	32	7	is	be	AUX
cuesj-900	32	8	to	to	PART
cuesj-900	32	9	create	create	VERB
cuesj-900	32	10	a	a	DET
cuesj-900	32	11	model	model	NOUN
cuesj-900	32	12	that	that	PRON
cuesj-900	32	13	can	can	AUX
cuesj-900	32	14	accurately	accurately	ADV
cuesj-900	32	15	predict	predict	VERB
cuesj-900	32	16	the	the	DET
cuesj-900	32	17	likelihood	likelihood	NOUN
cuesj-900	32	18	of	of	ADP
cuesj-900	32	19	developing	develop	VERB
cuesj-900	32	20	diabetes	diabete	NOUN
cuesj-900	32	21	.	.	PUNCT
cuesj-900	33	1	weka	weka	PROPN
cuesj-900	33	2	tool	tool	PROPN
cuesj-900	33	3	was	be	AUX
cuesj-900	33	4	utilized	utilize	VERB
cuesj-900	33	5	for	for	ADP
cuesj-900	33	6	data	data	NOUN
cuesj-900	33	7	classification	classification	NOUN
cuesj-900	33	8	,	,	PUNCT
cuesj-900	33	9	with	with	SCONJ
cuesj-900	33	10	the	the	DET
cuesj-900	33	11	pima	pima	PROPN
cuesj-900	33	12	indian	indian	PROPN
cuesj-900	33	13	diabetes	diabetes	NOUN
cuesj-900	33	14	dataset	dataset	VERB
cuesj-900	33	15	serving	serve	VERB
cuesj-900	33	16	as	as	ADP
cuesj-900	33	17	the	the	DET
cuesj-900	33	18	primary	primary	ADJ
cuesj-900	33	19	dataset	dataset	NOUN
cuesj-900	33	20	.	.	PUNCT
cuesj-900	34	1	this	this	DET
cuesj-900	34	2	research	research	NOUN
cuesj-900	34	3	work	work	NOUN
cuesj-900	34	4	by	by	ADP
cuesj-900	34	5	ambigavathi	ambigavathi	ADV
cuesj-900	34	6	and	and	CCONJ
cuesj-900	34	7	sridharan,[6	sridharan,[6	ADP
cuesj-900	34	8	]	]	X
cuesj-900	34	9	the	the	DET
cuesj-900	34	10	purpose	purpose	NOUN
cuesj-900	34	11	of	of	ADP
cuesj-900	34	12	this	this	DET
cuesj-900	34	13	work	work	NOUN
cuesj-900	34	14	was	be	AUX
cuesj-900	34	15	to	to	PART
cuesj-900	34	16	examine	examine	VERB
cuesj-900	34	17	several	several	ADJ
cuesj-900	34	18	clustering	clustering	ADJ
cuesj-900	34	19	techniques	technique	NOUN
cuesj-900	34	20	from	from	ADP
cuesj-900	34	21	both	both	CCONJ
cuesj-900	34	22	theoretical	theoretical	ADJ
cuesj-900	34	23	and	and	CCONJ
cuesj-900	34	24	experimental	experimental	ADJ
cuesj-900	34	25	angles	angle	NOUN
cuesj-900	34	26	.	.	PUNCT
cuesj-900	35	1	trial	trial	NOUN
cuesj-900	35	2	findings	finding	NOUN
cuesj-900	35	3	revealed	reveal	VERB
cuesj-900	35	4	which	which	DET
cuesj-900	35	5	category	category	NOUN
cuesj-900	35	6	had	have	VERB
cuesj-900	35	7	the	the	DET
cuesj-900	35	8	best	good	ADJ
cuesj-900	35	9	algorithm	algorithm	NOUN
cuesj-900	35	10	using	use	VERB
cuesj-900	35	11	a	a	DET
cuesj-900	35	12	set	set	NOUN
cuesj-900	35	13	of	of	ADP
cuesj-900	35	14	physiological	physiological	ADJ
cuesj-900	35	15	data	datum	NOUN
cuesj-900	35	16	.	.	PUNCT
cuesj-900	36	1	a	a	DET
cuesj-900	36	2	variety	variety	NOUN
cuesj-900	36	3	of	of	ADP
cuesj-900	36	4	internal	internal	ADJ
cuesj-900	36	5	as	as	ADV
cuesj-900	36	6	well	well	ADV
cuesj-900	36	7	as	as	ADP
cuesj-900	36	8	stability	stability	NOUN
cuesj-900	36	9	measures	measure	NOUN
cuesj-900	36	10	were	be	AUX
cuesj-900	36	11	used	use	VERB
cuesj-900	36	12	to	to	PART
cuesj-900	36	13	validate	validate	VERB
cuesj-900	36	14	the	the	DET
cuesj-900	36	15	effectiveness	effectiveness	NOUN
cuesj-900	36	16	of	of	ADP
cuesj-900	36	17	each	each	DET
cuesj-900	36	18	clustering	clustering	ADJ
cuesj-900	36	19	technique	technique	NOUN
cuesj-900	36	20	in	in	ADP
cuesj-900	36	21	machine	machine	NOUN
cuesj-900	36	22	learning	learning	NOUN
cuesj-900	36	23	.	.	PUNCT
cuesj-900	37	1	this	this	DET
cuesj-900	37	2	research	research	NOUN
cuesj-900	37	3	concluded	conclude	VERB
cuesj-900	37	4	by	by	ADP
cuesj-900	37	5	highlighting	highlight	VERB
cuesj-900	37	6	future	future	ADJ
cuesj-900	37	7	directions	direction	NOUN
cuesj-900	37	8	for	for	ADP
cuesj-900	37	9	highdimensional	highdimensional	ADJ
cuesj-900	37	10	health	health	NOUN
cuesj-900	37	11	-	-	PUNCT
cuesj-900	37	12	care	care	NOUN
cuesj-900	37	13	data	datum	NOUN
cuesj-900	37	14	sets	set	NOUN
cuesj-900	37	15	using	use	VERB
cuesj-900	37	16	a	a	DET
cuesj-900	37	17	suitable	suitable	ADJ
cuesj-900	37	18	clustering	clustering	ADJ
cuesj-900	37	19	technique	technique	NOUN
cuesj-900	37	20	.	.	PUNCT
cuesj-900	38	1	in	in	ADP
cuesj-900	38	2	this	this	DET
cuesj-900	38	3	study	study	NOUN
cuesj-900	38	4	lorena	lorena	PROPN
cuesj-900	38	5	et	et	PROPN
cuesj-900	38	6	al	al	PROPN
cuesj-900	38	7	.	.	PROPN
cuesj-900	38	8	,[7	,[7	PROPN
cuesj-900	38	9	]	]	PUNCT
cuesj-900	38	10	in	in	ADP
cuesj-900	38	11	this	this	DET
cuesj-900	38	12	research	research	NOUN
cuesj-900	38	13	,	,	PUNCT
cuesj-900	38	14	we	we	PRON
cuesj-900	38	15	investigated	investigate	VERB
cuesj-900	38	16	resampling	resample	VERB
cuesj-900	38	17	techniques	technique	NOUN
cuesj-900	38	18	and	and	CCONJ
cuesj-900	38	19	metrics	metric	NOUN
cuesj-900	38	20	that	that	PRON
cuesj-900	38	21	may	may	AUX
cuesj-900	38	22	be	be	AUX
cuesj-900	38	23	obtained	obtain	VERB
cuesj-900	38	24	from	from	ADP
cuesj-900	38	25	training	train	VERB
cuesj-900	38	26	datasets	dataset	NOUN
cuesj-900	38	27	to	to	PART
cuesj-900	38	28	characterize	characterize	VERB
cuesj-900	38	29	the	the	DET
cuesj-900	38	30	complexity	complexity	NOUN
cuesj-900	38	31	of	of	ADP
cuesj-900	38	32	the	the	DET
cuesj-900	38	33	classification	classification	NOUN
cuesj-900	38	34	issues	issue	NOUN
cuesj-900	38	35	.	.	PUNCT
cuesj-900	39	1	recent	recent	ADJ
cuesj-900	39	2	literature	literature	NOUN
cuesj-900	39	3	also	also	ADV
cuesj-900	39	4	looked	look	VERB
cuesj-900	39	5	at	at	ADP
cuesj-900	39	6	and	and	CCONJ
cuesj-900	39	7	addressed	address	VERB
cuesj-900	39	8	their	their	PRON
cuesj-900	39	9	approaches	approach	NOUN
cuesj-900	39	10	,	,	PUNCT
cuesj-900	39	11	opening	open	VERB
cuesj-900	39	12	up	up	ADP
cuesj-900	39	13	the	the	DET
cuesj-900	39	14	possibility	possibility	NOUN
cuesj-900	39	15	of	of	ADP
cuesj-900	39	16	new	new	ADJ
cuesj-900	39	17	areas	area	NOUN
cuesj-900	39	18	for	for	ADP
cuesj-900	39	19	study	study	NOUN
cuesj-900	39	20	.	.	PUNCT
cuesj-900	40	1	the	the	DET
cuesj-900	40	2	extended	extended	ADJ
cuesj-900	40	3	complexity	complexity	NOUN
cuesj-900	40	4	library	library	NOUN
cuesj-900	40	5	r	r	NOUN
cuesj-900	40	6	package	package	NOUN
cuesj-900	40	7	,	,	PUNCT
cuesj-900	40	8	which	which	PRON
cuesj-900	40	9	includes	include	VERB
cuesj-900	40	10	a	a	DET
cuesj-900	40	11	number	number	NOUN
cuesj-900	40	12	of	of	ADP
cuesj-900	40	13	complex	complex	ADJ
cuesj-900	40	14	measurements	measurement	NOUN
cuesj-900	40	15	and	and	CCONJ
cuesj-900	40	16	is	be	AUX
cuesj-900	40	17	finished	finish	VERB
cuesj-900	40	18	and	and	CCONJ
cuesj-900	40	19	publicly	publicly	ADV
cuesj-900	40	20	accessible	accessible	ADJ
cuesj-900	40	21	,	,	PUNCT
cuesj-900	40	22	was	be	AUX
cuesj-900	40	23	also	also	ADV
cuesj-900	40	24	defined	define	VERB
cuesj-900	40	25	.	.	PUNCT
cuesj-900	41	1	this	this	DET
cuesj-900	41	2	research	research	NOUN
cuesj-900	41	3	work	work	NOUN
cuesj-900	41	4	by	by	ADP
cuesj-900	41	5	liu	liu	PROPN
cuesj-900	41	6	et	et	PROPN
cuesj-900	41	7	al	al	PROPN
cuesj-900	41	8	.	.	PROPN
cuesj-900	41	9	,[8	,[8	PROPN
cuesj-900	41	10	]	]	PUNCT
cuesj-900	41	11	centered	center	VERB
cuesj-900	41	12	on	on	ADP
cuesj-900	41	13	addressing	address	VERB
cuesj-900	41	14	issues	issue	NOUN
cuesj-900	41	15	with	with	ADP
cuesj-900	41	16	the	the	DET
cuesj-900	41	17	class	class	NOUN
cuesj-900	41	18	imbalance	imbalance	NOUN
cuesj-900	41	19	and	and	CCONJ
cuesj-900	41	20	identifying	identify	VERB
cuesj-900	41	21	attribute	attribute	NOUN
cuesj-900	41	22	correlations	correlation	NOUN
cuesj-900	41	23	.	.	PUNCT
cuesj-900	42	1	kaggle	kaggle	NOUN
cuesj-900	42	2	offers	offer	VERB
cuesj-900	42	3	two	two	NUM
cuesj-900	42	4	datasets	dataset	NOUN
cuesj-900	42	5	for	for	ADP
cuesj-900	42	6	fraud	fraud	NOUN
cuesj-900	42	7	detection	detection	NOUN
cuesj-900	42	8	that	that	PRON
cuesj-900	42	9	may	may	AUX
cuesj-900	42	10	be	be	AUX
cuesj-900	42	11	used	use	VERB
cuesj-900	42	12	to	to	PART
cuesj-900	42	13	create	create	VERB
cuesj-900	42	14	classifiers	classifier	NOUN
cuesj-900	42	15	and	and	CCONJ
cuesj-900	42	16	assess	assess	VERB
cuesj-900	42	17	the	the	DET
cuesj-900	42	18	effects	effect	NOUN
cuesj-900	42	19	of	of	ADP
cuesj-900	42	20	various	various	ADJ
cuesj-900	42	21	data	datum	NOUN
cuesj-900	42	22	processing	processing	NOUN
cuesj-900	42	23	methods	method	NOUN
cuesj-900	42	24	.	.	PUNCT
cuesj-900	43	1	through	through	ADP
cuesj-900	43	2	their	their	PRON
cuesj-900	43	3	approach	approach	NOUN
cuesj-900	43	4	,	,	PUNCT
cuesj-900	43	5	they	they	PRON
cuesj-900	43	6	were	be	AUX
cuesj-900	43	7	aware	aware	ADJ
cuesj-900	43	8	of	of	ADP
cuesj-900	43	9	the	the	DET
cuesj-900	43	10	latest	late	ADJ
cuesj-900	43	11	fraud	fraud	NOUN
cuesj-900	43	12	detection	detection	NOUN
cuesj-900	43	13	discoveries	discovery	NOUN
cuesj-900	43	14	,	,	PUNCT
cuesj-900	43	15	learned	learn	VERB
cuesj-900	43	16	more	more	ADJ
cuesj-900	43	17	about	about	ADP
cuesj-900	43	18	various	various	ADJ
cuesj-900	43	19	data	datum	NOUN
cuesj-900	43	20	processing	processing	NOUN
cuesj-900	43	21	techniques	technique	NOUN
cuesj-900	43	22	,	,	PUNCT
cuesj-900	43	23	and	and	CCONJ
cuesj-900	43	24	created	create	VERB
cuesj-900	43	25	several	several	ADJ
cuesj-900	43	26	sorts	sort	NOUN
cuesj-900	43	27	of	of	ADP
cuesj-900	43	28	classifiers	classifier	NOUN
cuesj-900	43	29	.	.	PUNCT
cuesj-900	44	1	they	they	PRON
cuesj-900	44	2	supported	support	VERB
cuesj-900	44	3	the	the	DET
cuesj-900	44	4	need	need	NOUN
cuesj-900	44	5	of	of	ADP
cuesj-900	44	6	addressing	address	VERB
cuesj-900	44	7	class	class	NOUN
cuesj-900	44	8	imbalance	imbalance	NOUN
cuesj-900	44	9	and	and	CCONJ
cuesj-900	44	10	examining	examine	VERB
cuesj-900	44	11	attribute	attribute	NOUN
cuesj-900	44	12	connections	connection	NOUN
cuesj-900	44	13	.	.	PUNCT
cuesj-900	45	1	finally	finally	ADV
cuesj-900	45	2	,	,	PUNCT
cuesj-900	45	3	they	they	PRON
cuesj-900	45	4	looked	look	VERB
cuesj-900	45	5	at	at	ADP
cuesj-900	45	6	the	the	DET
cuesj-900	45	7	relationships	relationship	NOUN
cuesj-900	45	8	between	between	ADP
cuesj-900	45	9	each	each	DET
cuesj-900	45	10	attribute	attribute	NOUN
cuesj-900	45	11	and	and	CCONJ
cuesj-900	45	12	marginally	marginally	ADV
cuesj-900	45	13	better	well	ADJ
cuesj-900	45	14	performance	performance	NOUN
cuesj-900	45	15	.	.	PUNCT
cuesj-900	46	1	their	their	PRON
cuesj-900	46	2	experimental	experimental	ADJ
cuesj-900	46	3	findings	finding	NOUN
cuesj-900	46	4	clearly	clearly	ADV
cuesj-900	46	5	demonstrated	demonstrate	VERB
cuesj-900	46	6	that	that	SCONJ
cuesj-900	46	7	addressing	address	VERB
cuesj-900	46	8	class	class	NOUN
cuesj-900	46	9	imbalance	imbalance	NOUN
cuesj-900	46	10	had	have	VERB
cuesj-900	46	11	a	a	DET
cuesj-900	46	12	beneficial	beneficial	ADJ
cuesj-900	46	13	effect	effect	NOUN
cuesj-900	46	14	on	on	ADP
cuesj-900	46	15	unbalanced	unbalanced	ADJ
cuesj-900	46	16	data	datum	NOUN
cuesj-900	46	17	sets	set	NOUN
cuesj-900	46	18	and	and	CCONJ
cuesj-900	46	19	that	that	SCONJ
cuesj-900	46	20	examining	examine	VERB
cuesj-900	46	21	the	the	DET
cuesj-900	46	22	link	link	NOUN
cuesj-900	46	23	between	between	ADP
cuesj-900	46	24	qualities	quality	NOUN
cuesj-900	46	25	would	would	AUX
cuesj-900	46	26	also	also	ADV
cuesj-900	46	27	be	be	AUX
cuesj-900	46	28	helpful	helpful	ADJ
cuesj-900	46	29	.	.	PUNCT
cuesj-900	47	1	in	in	ADP
cuesj-900	47	2	zheng,[9	zheng,[9	NUM
cuesj-900	47	3	]	]	PUNCT
cuesj-900	47	4	investigation	investigation	NOUN
cuesj-900	47	5	on	on	ADP
cuesj-900	47	6	the	the	DET
cuesj-900	47	7	application	application	NOUN
cuesj-900	47	8	of	of	ADP
cuesj-900	47	9	synthetic	synthetic	ADJ
cuesj-900	47	10	minority	minority	NOUN
cuesj-900	47	11	oversampling	oversample	VERB
cuesj-900	47	12	technique	technique	NOUN
cuesj-900	47	13	(	(	PUNCT
cuesj-900	47	14	smote	smote	NOUN
cuesj-900	47	15	)	)	PUNCT
cuesj-900	47	16	with	with	ADP
cuesj-900	47	17	various	various	ADJ
cuesj-900	47	18	classifiers	classifier	NOUN
cuesj-900	47	19	.	.	PUNCT
cuesj-900	48	1	to	to	PART
cuesj-900	48	2	build	build	VERB
cuesj-900	48	3	predictive	predictive	ADJ
cuesj-900	48	4	models	model	NOUN
cuesj-900	48	5	on	on	ADP
cuesj-900	48	6	the	the	DET
cuesj-900	48	7	heart	heart	NOUN
cuesj-900	48	8	disease	disease	NOUN
cuesj-900	48	9	data	datum	NOUN
cuesj-900	48	10	,	,	PUNCT
cuesj-900	48	11	the	the	DET
cuesj-900	48	12	smote	smote	ADJ
cuesj-900	48	13	method	method	NOUN
cuesj-900	48	14	was	be	AUX
cuesj-900	48	15	used	use	VERB
cuesj-900	48	16	for	for	ADP
cuesj-900	48	17	comparison	comparison	NOUN
cuesj-900	48	18	.	.	PUNCT
cuesj-900	49	1	regular	regular	ADJ
cuesj-900	49	2	smote	smote	ADJ
cuesj-900	49	3	,	,	PUNCT
cuesj-900	49	4	borderline	borderline	NOUN
cuesj-900	49	5	-	-	PUNCT
cuesj-900	49	6	smote	smote	ADJ
cuesj-900	49	7	,	,	PUNCT
cuesj-900	49	8	svm	svm	ADJ
cuesj-900	49	9	-	-	ADJ
cuesj-900	49	10	smote	smote	ADJ
cuesj-900	49	11	,	,	PUNCT
cuesj-900	49	12	and	and	CCONJ
cuesj-900	49	13	k	k	X
cuesj-900	49	14	-	-	PUNCT
cuesj-900	49	15	means	means	NOUN
cuesj-900	49	16	smote	smote	NOUN
cuesj-900	49	17	were	be	AUX
cuesj-900	49	18	combined	combine	VERB
cuesj-900	49	19	with	with	ADP
cuesj-900	49	20	four	four	NUM
cuesj-900	49	21	classification	classification	NOUN
cuesj-900	49	22	algorithms	algorithm	NOUN
cuesj-900	49	23	under	under	ADP
cuesj-900	49	24	the	the	DET
cuesj-900	49	25	classical	classical	ADJ
cuesj-900	49	26	and	and	CCONJ
cuesj-900	49	27	neyman	neyman	PROPN
cuesj-900	49	28	pearson	pearson	PROPN
cuesj-900	49	29	(	(	PUNCT
cuesj-900	49	30	np	np	INTJ
cuesj-900	49	31	)	)	PUNCT
cuesj-900	49	32	paradigms	paradigm	VERB
cuesj-900	49	33	.	.	PUNCT
cuesj-900	50	1	the	the	DET
cuesj-900	50	2	performance	performance	NOUN
cuesj-900	50	3	of	of	ADP
cuesj-900	50	4	these	these	DET
cuesj-900	50	5	models	model	NOUN
cuesj-900	50	6	was	be	AUX
cuesj-900	50	7	compared	compare	VERB
cuesj-900	50	8	.	.	PUNCT
cuesj-900	51	1	according	accord	VERB
cuesj-900	51	2	to	to	ADP
cuesj-900	51	3	their	their	PRON
cuesj-900	51	4	findings	finding	NOUN
cuesj-900	51	5	,	,	PUNCT
cuesj-900	51	6	the	the	DET
cuesj-900	51	7	svm	svm	ADJ
cuesj-900	51	8	-	-	ADJ
cuesj-900	51	9	smote	smote	ADJ
cuesj-900	51	10	and	and	CCONJ
cuesj-900	51	11	borderline	borderline	NOUN
cuesj-900	51	12	-	-	PUNCT
cuesj-900	51	13	smote	smote	ADJ
cuesj-900	51	14	perform	perform	NOUN
cuesj-900	51	15	better	well	ADJ
cuesj-900	51	16	than	than	ADP
cuesj-900	51	17	other	other	ADJ
cuesj-900	51	18	smote	smote	ADJ
cuesj-900	51	19	variations	variation	NOUN
cuesj-900	51	20	,	,	PUNCT
cuesj-900	51	21	and	and	CCONJ
cuesj-900	51	22	the	the	DET
cuesj-900	51	23	np	np	PRON
cuesj-900	51	24	classification	classification	NOUN
cuesj-900	51	25	is	be	AUX
cuesj-900	51	26	better	well	ADJ
cuesj-900	51	27	at	at	ADP
cuesj-900	51	28	successfully	successfully	ADV
cuesj-900	51	29	controlling	control	VERB
cuesj-900	51	30	type	type	NOUN
cuesj-900	51	31	i	i	PRON
cuesj-900	51	32	error	error	NOUN
cuesj-900	51	33	.	.	PUNCT
cuesj-900	52	1	materials	material	NOUN
cuesj-900	52	2	and	and	CCONJ
cuesj-900	52	3	methods	method	NOUN
cuesj-900	52	4	weather	weather	PROPN
cuesj-900	52	5	forecasting	forecasting	NOUN
cuesj-900	52	6	:	:	PUNCT
cuesj-900	52	7	the	the	DET
cuesj-900	52	8	machine	machine	NOUN
cuesj-900	52	9	learning	learn	VERB
cuesj-900	52	10	approach	approach	NOUN
cuesj-900	52	11	before	before	ADP
cuesj-900	52	12	the	the	DET
cuesj-900	52	13	development	development	NOUN
cuesj-900	52	14	of	of	ADP
cuesj-900	52	15	models	model	NOUN
cuesj-900	52	16	,	,	PUNCT
cuesj-900	52	17	people	people	NOUN
cuesj-900	52	18	made	make	VERB
cuesj-900	52	19	predictions	prediction	NOUN
cuesj-900	52	20	for	for	ADP
cuesj-900	52	21	a	a	DET
cuesj-900	52	22	given	give	VERB
cuesj-900	52	23	location	location	NOUN
cuesj-900	52	24	based	base	VERB
cuesj-900	52	25	on	on	ADP
cuesj-900	52	26	observable	observable	ADJ
cuesj-900	52	27	data	datum	NOUN
cuesj-900	52	28	collected	collect	VERB
cuesj-900	52	29	over	over	ADP
cuesj-900	52	30	time	time	NOUN
cuesj-900	52	31	.	.	PUNCT
cuesj-900	53	1	for	for	ADP
cuesj-900	53	2	example	example	NOUN
cuesj-900	53	3	,	,	PUNCT
cuesj-900	53	4	they	they	PRON
cuesj-900	53	5	used	use	VERB
cuesj-900	53	6	a	a	DET
cuesj-900	53	7	basic	basic	ADJ
cuesj-900	53	8	approach	approach	NOUN
cuesj-900	53	9	known	know	VERB
cuesj-900	53	10	as	as	ADP
cuesj-900	53	11	climatology	climatology	NOUN
cuesj-900	53	12	to	to	PART
cuesj-900	53	13	predict	predict	VERB
cuesj-900	53	14	the	the	DET
cuesj-900	53	15	weather	weather	NOUN
cuesj-900	53	16	.	.	PUNCT
cuesj-900	54	1	rough	rough	ADJ
cuesj-900	54	2	estimations	estimation	NOUN
cuesj-900	54	3	of	of	ADP
cuesj-900	54	4	long	long	ADJ
cuesj-900	54	5	-	-	PUNCT
cuesj-900	54	6	term	term	NOUN
cuesj-900	54	7	trends	trend	NOUN
cuesj-900	54	8	and	and	CCONJ
cuesj-900	54	9	cumulative	cumulative	ADJ
cuesj-900	54	10	values	value	NOUN
cuesj-900	54	11	of	of	ADP
cuesj-900	54	12	variables	variable	NOUN
cuesj-900	54	13	such	such	ADJ
cuesj-900	54	14	as	as	ADP
cuesj-900	54	15	temperature	temperature	NOUN
cuesj-900	54	16	or	or	CCONJ
cuesj-900	54	17	precipitation	precipitation	NOUN
cuesj-900	54	18	may	may	AUX
cuesj-900	54	19	be	be	AUX
cuesj-900	54	20	generated	generate	VERB
cuesj-900	54	21	by	by	ADP
cuesj-900	54	22	applying	apply	VERB
cuesj-900	54	23	basic	basic	ADJ
cuesj-900	54	24	statistics	statistic	NOUN
cuesj-900	54	25	to	to	ADP
cuesj-900	54	26	recorded	record	VERB
cuesj-900	54	27	weather	weather	NOUN
cuesj-900	54	28	events	event	NOUN
cuesj-900	54	29	over	over	ADP
cuesj-900	54	30	long	long	ADJ
cuesj-900	54	31	enough	enough	ADJ
cuesj-900	54	32	periods	period	NOUN
cuesj-900	54	33	.	.	PUNCT
cuesj-900	55	1	with	with	ADP
cuesj-900	55	2	the	the	DET
cuesj-900	55	3	development	development	NOUN
cuesj-900	55	4	of	of	ADP
cuesj-900	55	5	tools	tool	NOUN
cuesj-900	55	6	to	to	PART
cuesj-900	55	7	accurately	accurately	ADV
cuesj-900	55	8	monitor	monitor	VERB
cuesj-900	55	9	the	the	DET
cuesj-900	55	10	atmosphere	atmosphere	NOUN
cuesj-900	55	11	and	and	CCONJ
cuesj-900	55	12	disseminate	disseminate	VERB
cuesj-900	55	13	these	these	DET
cuesj-900	55	14	readings	reading	NOUN
cuesj-900	55	15	across	across	ADP
cuesj-900	55	16	geographically	geographically	ADV
cuesj-900	55	17	far	far	ADJ
cuesj-900	55	18	places	place	NOUN
cuesj-900	55	19	,	,	PUNCT
cuesj-900	55	20	weather	weather	NOUN
cuesj-900	55	21	maps	map	NOUN
cuesj-900	55	22	became	become	VERB
cuesj-900	55	23	available	available	ADJ
cuesj-900	55	24	around	around	ADP
cuesj-900	55	25	the	the	DET
cuesj-900	55	26	start	start	NOUN
cuesj-900	55	27	of	of	ADP
cuesj-900	55	28	the	the	DET
cuesj-900	55	29	20th	20th	ADJ
cuesj-900	55	30	century	century	NOUN
cuesj-900	55	31	.	.	PUNCT
cuesj-900	56	1	with	with	ADP
cuesj-900	56	2	the	the	DET
cuesj-900	56	3	advent	advent	NOUN
cuesj-900	56	4	of	of	ADP
cuesj-900	56	5	weather	weather	NOUN
cuesj-900	56	6	forecasting	forecasting	NOUN
cuesj-900	56	7	techniques	technique	NOUN
cuesj-900	56	8	that	that	PRON
cuesj-900	56	9	anticipated	anticipate	VERB
cuesj-900	56	10	the	the	DET
cuesj-900	56	11	movement	movement	NOUN
cuesj-900	56	12	and	and	CCONJ
cuesj-900	56	13	effects	effect	NOUN
cuesj-900	56	14	of	of	ADP
cuesj-900	56	15	these	these	DET
cuesj-900	56	16	pressure	pressure	NOUN
cuesj-900	56	17	systems	system	NOUN
cuesj-900	56	18	in	in	ADP
cuesj-900	56	19	the	the	DET
cuesj-900	56	20	atmosphere	atmosphere	NOUN
cuesj-900	56	21	,	,	PUNCT
cuesj-900	56	22	these	these	DET
cuesj-900	56	23	maps	map	NOUN
cuesj-900	56	24	first	first	ADV
cuesj-900	56	25	started	start	VERB
cuesj-900	56	26	to	to	PART
cuesj-900	56	27	show	show	VERB
cuesj-900	56	28	the	the	DET
cuesj-900	56	29	position	position	NOUN
cuesj-900	56	30	and	and	CCONJ
cuesj-900	56	31	shape	shape	NOUN
cuesj-900	56	32	of	of	ADP
cuesj-900	56	33	lowand	lowand	ADJ
cuesj-900	56	34	high	high	ADJ
cuesj-900	56	35	-	-	PUNCT
cuesj-900	56	36	pressure	pressure	NOUN
cuesj-900	56	37	systems	system	NOUN
cuesj-900	56	38	.	.	PUNCT
cuesj-900	57	1	the	the	DET
cuesj-900	57	2	use	use	NOUN
cuesj-900	57	3	of	of	ADP
cuesj-900	57	4	statistical	statistical	ADJ
cuesj-900	57	5	models	model	NOUN
cuesj-900	57	6	in	in	ADP
cuesj-900	57	7	weather	weather	NOUN
cuesj-900	57	8	forecasting	forecasting	NOUN
cuesj-900	57	9	was	be	AUX
cuesj-900	57	10	first	first	ADV
cuesj-900	57	11	advocated	advocate	VERB
cuesj-900	57	12	in	in	ADP
cuesj-900	57	13	the	the	DET
cuesj-900	57	14	1950s.[10	1950s.[10	NOUN
cuesj-900	57	15	]	]	PUNCT
cuesj-900	57	16	malone	malone	PROPN
cuesj-900	57	17	claimed	claim	VERB
cuesj-900	57	18	that	that	SCONJ
cuesj-900	57	19	“	"	PUNCT
cuesj-900	57	20	statistics	statistic	NOUN
cuesj-900	57	21	must	must	AUX
cuesj-900	57	22	ultimately	ultimately	ADV
cuesj-900	57	23	play	play	VERB
cuesj-900	57	24	some	some	DET
cuesj-900	57	25	part	part	NOUN
cuesj-900	57	26	”	"	PUNCT
cuesj-900	57	27	in	in	ADP
cuesj-900	57	28	atmospheric	atmospheric	ADJ
cuesj-900	57	29	simulation	simulation	NOUN
cuesj-900	57	30	at	at	ADP
cuesj-900	57	31	the	the	DET
cuesj-900	57	32	same	same	ADJ
cuesj-900	57	33	time	time	NOUN
cuesj-900	57	34	that	that	PRON
cuesj-900	57	35	the	the	DET
cuesj-900	57	36	first	first	ADJ
cuesj-900	57	37	numerical	numerical	PROPN
cuesj-900	57	38	weather	weather	PROPN
cuesj-900	57	39	prediction	prediction	NOUN
cuesj-900	57	40	(	(	PUNCT
cuesj-900	57	41	nwp	nwp	NOUN
cuesj-900	57	42	)	)	PUNCT
cuesj-900	57	43	models	model	NOUN
cuesj-900	57	44	were	be	AUX
cuesj-900	57	45	being	be	AUX
cuesj-900	57	46	built	build	VERB
cuesj-900	57	47	.	.	PUNCT
cuesj-900	58	1	the	the	DET
cuesj-900	58	2	author	author	NOUN
cuesj-900	58	3	developed	develop	VERB
cuesj-900	58	4	a	a	DET
cuesj-900	58	5	method	method	NOUN
cuesj-900	58	6	for	for	ADP
cuesj-900	58	7	forecasting	forecast	VERB
cuesj-900	58	8	the	the	DET
cuesj-900	58	9	sea	sea	NOUN
cuesj-900	58	10	-	-	PUNCT
cuesj-900	58	11	level	level	NOUN
cuesj-900	58	12	pressure	pressure	NOUN
cuesj-900	58	13	field	field	NOUN
cuesj-900	58	14	using	use	VERB
cuesj-900	58	15	multiple	multiple	ADJ
cuesj-900	58	16	linear	linear	ADJ
cuesj-900	58	17	regression	regression	NOUN
cuesj-900	58	18	.	.	PUNCT
cuesj-900	59	1	despite	despite	SCONJ
cuesj-900	59	2	the	the	DET
cuesj-900	59	3	optimism	optimism	NOUN
cuesj-900	59	4	malone	malone	NOUN
cuesj-900	59	5	expressed	express	VERB
cuesj-900	59	6	in	in	ADP
cuesj-900	59	7	the	the	DET
cuesj-900	59	8	early	early	ADJ
cuesj-900	59	9	1950s	1950s	NUM
cuesj-900	59	10	,	,	PUNCT
cuesj-900	59	11	machine	machine	NOUN
cuesj-900	59	12	learning	learning	NOUN
cuesj-900	59	13	and	and	CCONJ
cuesj-900	59	14	statistical	statistical	ADJ
cuesj-900	59	15	methods	method	NOUN
cuesj-900	59	16	have	have	AUX
cuesj-900	59	17	actually	actually	ADV
cuesj-900	59	18	been	be	AUX
cuesj-900	59	19	applied	apply	VERB
cuesj-900	59	20	as	as	ADP
cuesj-900	59	21	a	a	DET
cuesj-900	59	22	complement	complement	NOUN
cuesj-900	59	23	to	to	ADP
cuesj-900	59	24	nwp	nwp	NOUN
cuesj-900	59	25	rather	rather	ADV
cuesj-900	59	26	than	than	ADP
cuesj-900	59	27	as	as	ADP
cuesj-900	59	28	a	a	DET
cuesj-900	59	29	replacement	replacement	NOUN
cuesj-900	59	30	for	for	ADP
cuesj-900	59	31	it	it	PRON
cuesj-900	59	32	.	.	PUNCT
cuesj-900	60	1	other	other	ADJ
cuesj-900	60	2	approaches	approach	NOUN
cuesj-900	60	3	have	have	AUX
cuesj-900	60	4	not	not	PART
cuesj-900	60	5	yet	yet	ADV
cuesj-900	60	6	been	be	AUX
cuesj-900	60	7	able	able	ADJ
cuesj-900	60	8	to	to	PART
cuesj-900	60	9	match	match	VERB
cuesj-900	60	10	the	the	DET
cuesj-900	60	11	power	power	NOUN
cuesj-900	60	12	of	of	ADP
cuesj-900	60	13	physical	physical	ADJ
cuesj-900	60	14	equations	equation	NOUN
cuesj-900	60	15	to	to	PART
cuesj-900	60	16	simulate	simulate	VERB
cuesj-900	60	17	the	the	DET
cuesj-900	60	18	evolution	evolution	NOUN
cuesj-900	60	19	of	of	ADP
cuesj-900	60	20	the	the	DET
cuesj-900	60	21	atmosphere	atmosphere	NOUN
cuesj-900	60	22	along	along	ADP
cuesj-900	60	23	the	the	DET
cuesj-900	60	24	spatial	spatial	ADJ
cuesj-900	60	25	and	and	CCONJ
cuesj-900	60	26	temporal	temporal	ADJ
cuesj-900	60	27	dimensions	dimension	NOUN
cuesj-900	60	28	.	.	PUNCT
cuesj-900	61	1	we	we	PRON
cuesj-900	61	2	discuss	discuss	VERB
cuesj-900	61	3	examples	example	NOUN
cuesj-900	61	4	of	of	ADP
cuesj-900	61	5	machine	machine	NOUN
cuesj-900	61	6	learning	learn	VERB
cuesj-900	61	7	approaches	approach	NOUN
cuesj-900	61	8	being	be	AUX
cuesj-900	61	9	used	use	VERB
cuesj-900	61	10	to	to	PART
cuesj-900	61	11	address	address	VERB
cuesj-900	61	12	various	various	ADJ
cuesj-900	61	13	issues	issue	NOUN
cuesj-900	61	14	in	in	ADP
cuesj-900	61	15	the	the	DET
cuesj-900	61	16	field	field	NOUN
cuesj-900	61	17	of	of	ADP
cuesj-900	61	18	weather	weather	NOUN
cuesj-900	61	19	forecasting	forecasting	NOUN
cuesj-900	61	20	in	in	ADP
cuesj-900	61	21	this	this	DET
cuesj-900	61	22	section	section	NOUN
cuesj-900	61	23	.	.	PUNCT
cuesj-900	62	1	the	the	DET
cuesj-900	62	2	following	follow	VERB
cuesj-900	62	3	categories	category	NOUN
cuesj-900	62	4	of	of	ADP
cuesj-900	62	5	challenges	challenge	NOUN
cuesj-900	62	6	,	,	PUNCT
cuesj-900	62	7	in	in	ADP
cuesj-900	62	8	which	which	PRON
cuesj-900	62	9	various	various	ADJ
cuesj-900	62	10	machine	machine	NOUN
cuesj-900	62	11	learning	learning	NOUN
cuesj-900	62	12	approaches	approach	NOUN
cuesj-900	62	13	have	have	AUX
cuesj-900	62	14	been	be	AUX
cuesj-900	62	15	effectively	effectively	ADV
cuesj-900	62	16	employed	employ	VERB
cuesj-900	62	17	to	to	PART
cuesj-900	62	18	enhance	enhance	VERB
cuesj-900	62	19	our	our	PRON
cuesj-900	62	20	understanding	understanding	NOUN
cuesj-900	62	21	of	of	ADP
cuesj-900	62	22	the	the	DET
cuesj-900	62	23	processes	process	NOUN
cuesj-900	62	24	in	in	ADP
cuesj-900	62	25	the	the	DET
cuesj-900	62	26	atmosphere	atmosphere	NOUN
cuesj-900	62	27	,	,	PUNCT
cuesj-900	62	28	can	can	AUX
cuesj-900	62	29	be	be	AUX
cuesj-900	62	30	identified	identify	VERB
cuesj-900	62	31	depending	depend	VERB
cuesj-900	62	32	on	on	ADP
cuesj-900	62	33	the	the	DET
cuesj-900	62	34	task	task	NOUN
cuesj-900	62	35	to	to	PART
cuesj-900	62	36	be	be	AUX
cuesj-900	62	37	solved	solve	VERB
cuesj-900	62	38	and	and	CCONJ
cuesj-900	62	39	the	the	DET
cuesj-900	62	40	nature	nature	NOUN
cuesj-900	62	41	of	of	ADP
cuesj-900	62	42	the	the	DET
cuesj-900	62	43	underlying	underlie	VERB
cuesj-900	62	44	data	datum	NOUN
cuesj-900	62	45	.	.	PUNCT
cuesj-900	63	1	kinds	kind	NOUN
cuesj-900	63	2	of	of	ADP
cuesj-900	63	3	machine	machine	NOUN
cuesj-900	63	4	learning	learning	NOUN
cuesj-900	63	5	supervised	supervise	VERB
cuesj-900	63	6	,	,	PUNCT
cuesj-900	63	7	unsupervised	unsupervised	ADJ
cuesj-900	63	8	,	,	PUNCT
cuesj-900	63	9	and	and	CCONJ
cuesj-900	63	10	reinforcement	reinforcement	NOUN
cuesj-900	63	11	learning	learning	NOUN
cuesj-900	63	12	methods	method	NOUN
cuesj-900	63	13	were	be	AUX
cuesj-900	63	14	used	use	VERB
cuesj-900	63	15	to	to	PART
cuesj-900	63	16	classify	classify	VERB
cuesj-900	63	17	machine	machine	NOUN
cuesj-900	63	18	learning	learning	NOUN
cuesj-900	63	19	algorithms	algorithm	NOUN
cuesj-900	63	20	:	:	PUNCT
cuesj-900	63	21	figure	figure	NOUN
cuesj-900	63	22	1	1	NUM
cuesj-900	63	23	depicts	depict	VERB
cuesj-900	63	24	the	the	DET
cuesj-900	63	25	categorization	categorization	NOUN
cuesj-900	63	26	in	in	ADP
cuesj-900	63	27	a	a	DET
cuesj-900	63	28	visual	visual	ADJ
cuesj-900	63	29	manner.[11	manner.[11	PROPN
cuesj-900	63	30	]	]	X
cuesj-900	63	31	supervised	supervise	VERB
cuesj-900	63	32	learning	learning	NOUN
cuesj-900	63	33	for	for	ADP
cuesj-900	63	34	machine	machine	NOUN
cuesj-900	63	35	learning	learning	NOUN
cuesj-900	63	36	,	,	PUNCT
cuesj-900	63	37	supervised	supervised	ADJ
cuesj-900	63	38	learning	learning	NOUN
cuesj-900	63	39	is	be	AUX
cuesj-900	63	40	crucial	crucial	ADJ
cuesj-900	63	41	.	.	PUNCT
cuesj-900	64	1	averaging	average	VERB
cuesj-900	64	2	input	input	NOUN
cuesj-900	64	3	and	and	CCONJ
cuesj-900	64	4	output	output	NOUN
cuesj-900	64	5	is	be	AUX
cuesj-900	64	6	the	the	DET
cuesj-900	64	7	aim	aim	NOUN
cuesj-900	64	8	of	of	ADP
cuesj-900	64	9	supervised	supervised	ADJ
cuesj-900	64	10	learning	learning	NOUN
cuesj-900	64	11	.	.	PUNCT
cuesj-900	65	1	the	the	DET
cuesj-900	65	2	input	input	NOUN
cuesj-900	65	3	data	datum	NOUN
cuesj-900	65	4	are	be	AUX
cuesj-900	65	5	a	a	DET
cuesj-900	65	6	list	list	NOUN
cuesj-900	65	7	of	of	ADP
cuesj-900	65	8	many	many	ADJ
cuesj-900	65	9	interesting	interesting	ADJ
cuesj-900	65	10	items	item	NOUN
cuesj-900	65	11	,	,	PUNCT
cuesj-900	65	12	each	each	PRON
cuesj-900	65	13	of	of	ADP
cuesj-900	65	14	which	which	PRON
cuesj-900	65	15	is	be	AUX
cuesj-900	65	16	referred	refer	VERB
cuesj-900	65	17	to	to	ADP
cuesj-900	65	18	as	as	ADP
cuesj-900	65	19	an	an	DET
cuesj-900	65	20	attribute	attribute	NOUN
cuesj-900	65	21	or	or	CCONJ
cuesj-900	65	22	an	an	DET
cuesj-900	65	23	example	example	NOUN
cuesj-900	65	24	.	.	PUNCT
cuesj-900	66	1	the	the	DET
cuesj-900	66	2	output	output	NOUN
cuesj-900	66	3	is	be	AUX
cuesj-900	66	4	a	a	DET
cuesj-900	66	5	manager	manager	NOUN
cuesj-900	66	6	’s	’s	PART
cuesj-900	66	7	assessment	assessment	NOUN
cuesj-900	66	8	or	or	CCONJ
cuesj-900	66	9	conclusion	conclusion	NOUN
cuesj-900	66	10	.	.	PUNCT
cuesj-900	67	1	in	in	ADP
cuesj-900	67	2	classification	classification	NOUN
cuesj-900	67	3	,	,	PUNCT
cuesj-900	67	4	a	a	DET
cuesj-900	67	5	form	form	NOUN
cuesj-900	67	6	of	of	ADP
cuesj-900	67	7	supervised	supervised	ADJ
cuesj-900	67	8	learning	learning	NOUN
cuesj-900	67	9	,	,	PUNCT
cuesj-900	67	10	averaging	average	VERB
cuesj-900	67	11	(	(	PUNCT
cuesj-900	67	12	or	or	CCONJ
cuesj-900	67	13	a	a	DET
cuesj-900	67	14	discriminant	discriminant	NOUN
cuesj-900	67	15	function	function	NOUN
cuesj-900	67	16	)	)	PUNCT
cuesj-900	67	17	is	be	AUX
cuesj-900	67	18	used	use	VERB
cuesj-900	67	19	to	to	PART
cuesj-900	67	20	distinguish	distinguish	VERB
cuesj-900	67	21	between	between	ADP
cuesj-900	67	22	several	several	ADJ
cuesj-900	67	23	groups	group	NOUN
cuesj-900	67	24	of	of	ADP
cuesj-900	67	25	occurrences	occurrence	NOUN
cuesj-900	67	26	.	.	PUNCT
cuesj-900	68	1	a	a	DET
cuesj-900	68	2	list	list	NOUN
cuesj-900	68	3	of	of	ADP
cuesj-900	68	4	the	the	DET
cuesj-900	68	5	different	different	ADJ
cuesj-900	68	6	classes	class	NOUN
cuesj-900	68	7	is	be	AUX
cuesj-900	68	8	included	include	VERB
cuesj-900	68	9	in	in	ADP
cuesj-900	68	10	the	the	DET
cuesj-900	68	11	output	output	NOUN
cuesj-900	68	12	,	,	PUNCT
cuesj-900	68	13	mulla	mulla	NOUN
cuesj-900	68	14	and	and	CCONJ
cuesj-900	68	15	demir	demir	PROPN
cuesj-900	68	16	:	:	PUNCT
cuesj-900	68	17	the	the	DET
cuesj-900	68	18	use	use	NOUN
cuesj-900	68	19	of	of	ADP
cuesj-900	68	20	clustering	clustering	NOUN
cuesj-900	68	21	and	and	CCONJ
cuesj-900	68	22	classification	classification	NOUN
cuesj-900	68	23	methods	method	NOUN
cuesj-900	68	24	in	in	ADP
cuesj-900	68	25	machine	machine	NOUN
cuesj-900	68	26	learning	learn	VERB
cuesj-900	68	27	54	54	NUM
cuesj-900	68	28	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-900	68	29	cuesj	cuesj	NOUN
cuesj-900	68	30	2023	2023	NUM
cuesj-900	68	31	,	,	PUNCT
cuesj-900	68	32	7	7	NUM
cuesj-900	68	33	(	(	PUNCT
cuesj-900	68	34	1	1	NUM
cuesj-900	68	35	):	):	PUNCT
cuesj-900	68	36	52	52	NUM
cuesj-900	68	37	-	-	SYM
cuesj-900	68	38	59	59	NUM
cuesj-900	68	39	sometimes	sometimes	ADV
cuesj-900	68	40	referred	refer	VERB
cuesj-900	68	41	to	to	ADP
cuesj-900	68	42	as	as	ADP
cuesj-900	68	43	the	the	DET
cuesj-900	68	44	class	class	NOUN
cuesj-900	68	45	label	label	NOUN
cuesj-900	68	46	in	in	ADP
cuesj-900	68	47	machine	machine	NOUN
cuesj-900	68	48	learning	learning	NOUN
cuesj-900	68	49	.	.	PUNCT
cuesj-900	69	1	the	the	DET
cuesj-900	69	2	term	term	NOUN
cuesj-900	69	3	used	use	VERB
cuesj-900	69	4	to	to	PART
cuesj-900	69	5	describe	describe	VERB
cuesj-900	69	6	the	the	DET
cuesj-900	69	7	discriminant	discriminant	NOUN
cuesj-900	69	8	function	function	NOUN
cuesj-900	69	9	is	be	AUX
cuesj-900	69	10	classifier	classifier	NOUN
cuesj-900	69	11	or	or	CCONJ
cuesj-900	69	12	model	model	NOUN
cuesj-900	69	13	.	.	PUNCT
cuesj-900	70	1	a	a	DET
cuesj-900	70	2	training	training	NOUN
cuesj-900	70	3	set	set	NOUN
cuesj-900	70	4	is	be	AUX
cuesj-900	70	5	a	a	DET
cuesj-900	70	6	set	set	NOUN
cuesj-900	70	7	of	of	ADP
cuesj-900	70	8	instances	instance	NOUN
cuesj-900	70	9	for	for	ADP
cuesj-900	70	10	which	which	PRON
cuesj-900	70	11	the	the	DET
cuesj-900	70	12	class	class	NOUN
cuesj-900	70	13	label	label	NOUN
cuesj-900	70	14	has	have	AUX
cuesj-900	70	15	been	be	AUX
cuesj-900	70	16	determined	determine	VERB
cuesj-900	70	17	.	.	PUNCT
cuesj-900	71	1	during	during	ADP
cuesj-900	71	2	classification	classification	NOUN
cuesj-900	71	3	,	,	PUNCT
cuesj-900	71	4	a	a	DET
cuesj-900	71	5	model	model	NOUN
cuesj-900	71	6	is	be	AUX
cuesj-900	71	7	developed	develop	VERB
cuesj-900	71	8	using	use	VERB
cuesj-900	71	9	a	a	DET
cuesj-900	71	10	set	set	NOUN
cuesj-900	71	11	of	of	ADP
cuesj-900	71	12	parameters	parameter	NOUN
cuesj-900	71	13	to	to	PART
cuesj-900	71	14	create	create	VERB
cuesj-900	71	15	a	a	DET
cuesj-900	71	16	mapping	mapping	NOUN
cuesj-900	71	17	between	between	ADP
cuesj-900	71	18	instances	instance	NOUN
cuesj-900	71	19	in	in	ADP
cuesj-900	71	20	the	the	DET
cuesj-900	71	21	training	training	NOUN
cuesj-900	71	22	set	set	NOUN
cuesj-900	71	23	and	and	CCONJ
cuesj-900	71	24	labels	label	NOUN
cuesj-900	71	25	in	in	ADP
cuesj-900	71	26	the	the	DET
cuesj-900	71	27	training	training	NOUN
cuesj-900	71	28	set	set	NOUN
cuesj-900	71	29	.	.	PUNCT
cuesj-900	72	1	to	to	PART
cuesj-900	72	2	identify	identify	VERB
cuesj-900	72	3	or	or	CCONJ
cuesj-900	72	4	recognize	recognize	VERB
cuesj-900	72	5	previously	previously	ADV
cuesj-900	72	6	undiscovered	undiscovered	ADJ
cuesj-900	72	7	scenarios	scenario	NOUN
cuesj-900	72	8	,	,	PUNCT
cuesj-900	72	9	the	the	DET
cuesj-900	72	10	trained	train	VERB
cuesj-900	72	11	model	model	NOUN
cuesj-900	72	12	may	may	AUX
cuesj-900	72	13	be	be	AUX
cuesj-900	72	14	used	use	VERB
cuesj-900	72	15	.	.	PUNCT
cuesj-900	73	1	supervised	supervised	ADJ
cuesj-900	73	2	learning	learning	NOUN
cuesj-900	73	3	is	be	AUX
cuesj-900	73	4	the	the	DET
cuesj-900	73	5	main	main	ADJ
cuesj-900	73	6	methodology	methodology	NOUN
cuesj-900	73	7	used	use	VERB
cuesj-900	73	8	in	in	ADP
cuesj-900	73	9	real	real	ADJ
cuesj-900	73	10	-	-	PUNCT
cuesj-900	73	11	world	world	NOUN
cuesj-900	73	12	machine	machine	NOUN
cuesj-900	73	13	learning	learning	NOUN
cuesj-900	73	14	.	.	PUNCT
cuesj-900	74	1	in	in	ADP
cuesj-900	74	2	supervised	supervised	ADJ
cuesj-900	74	3	learning	learning	NOUN
cuesj-900	74	4	,	,	PUNCT
cuesj-900	74	5	the	the	DET
cuesj-900	74	6	function	function	NOUN
cuesj-900	74	7	that	that	PRON
cuesj-900	74	8	converts	convert	VERB
cuesj-900	74	9	input	input	NOUN
cuesj-900	74	10	variables	variable	NOUN
cuesj-900	74	11	(	(	PUNCT
cuesj-900	74	12	x	x	X
cuesj-900	74	13	)	)	PUNCT
cuesj-900	74	14	to	to	PART
cuesj-900	74	15	output	output	NOUN
cuesj-900	74	16	variables	variable	NOUN
cuesj-900	74	17	are	be	AUX
cuesj-900	74	18	learned	learn	VERB
cuesj-900	74	19	using	use	VERB
cuesj-900	74	20	an	an	DET
cuesj-900	74	21	algorithm	algorithm	NOUN
cuesj-900	74	22	(	(	PUNCT
cuesj-900	74	23	y	y	NOUN
cuesj-900	74	24	)	)	PUNCT
cuesj-900	74	25	.	.	PUNCT
cuesj-900	75	1	y	y	PROPN
cuesj-900	75	2	=	=	SYM
cuesj-900	75	3	f(x	f(x	PROPN
cuesj-900	75	4	)	)	PUNCT
cuesj-900	75	5	the	the	DET
cuesj-900	75	6	objective	objective	NOUN
cuesj-900	75	7	is	be	AUX
cuesj-900	75	8	to	to	PART
cuesj-900	75	9	anticipate	anticipate	VERB
cuesj-900	75	10	the	the	DET
cuesj-900	75	11	output	output	NOUN
cuesj-900	75	12	variables	variable	NOUN
cuesj-900	75	13	(	(	PUNCT
cuesj-900	75	14	y	y	NOUN
cuesj-900	75	15	)	)	PUNCT
cuesj-900	75	16	using	use	VERB
cuesj-900	75	17	new	new	ADJ
cuesj-900	75	18	input	input	NOUN
cuesj-900	75	19	data	datum	NOUN
cuesj-900	75	20	by	by	ADP
cuesj-900	75	21	as	as	ADP
cuesj-900	75	22	nearly	nearly	ADV
cuesj-900	75	23	approximating	approximate	VERB
cuesj-900	75	24	the	the	DET
cuesj-900	75	25	mapping	mapping	NOUN
cuesj-900	75	26	function	function	NOUN
cuesj-900	75	27	as	as	SCONJ
cuesj-900	75	28	is	be	AUX
cuesj-900	75	29	practical	practical	ADJ
cuesj-900	75	30	(	(	PUNCT
cuesj-900	75	31	x	x	NOUN
cuesj-900	75	32	)	)	PUNCT
cuesj-900	75	33	.	.	PUNCT
cuesj-900	76	1	since	since	SCONJ
cuesj-900	76	2	an	an	DET
cuesj-900	76	3	algorithm	algorithm	NOUN
cuesj-900	76	4	learns	learn	VERB
cuesj-900	76	5	from	from	ADP
cuesj-900	76	6	the	the	DET
cuesj-900	76	7	training	training	NOUN
cuesj-900	76	8	dataset	dataset	NOUN
cuesj-900	76	9	is	be	AUX
cuesj-900	76	10	akin	akin	ADJ
cuesj-900	76	11	to	to	ADP
cuesj-900	76	12	a	a	DET
cuesj-900	76	13	teacher	teacher	NOUN
cuesj-900	76	14	guiding	guide	VERB
cuesj-900	76	15	the	the	DET
cuesj-900	76	16	learning	learning	NOUN
cuesj-900	76	17	process	process	NOUN
cuesj-900	76	18	,	,	PUNCT
cuesj-900	76	19	it	it	PRON
cuesj-900	76	20	is	be	AUX
cuesj-900	76	21	known	know	VERB
cuesj-900	76	22	as	as	ADP
cuesj-900	76	23	supervised	supervised	ADJ
cuesj-900	76	24	learning	learning	NOUN
cuesj-900	76	25	.	.	PUNCT
cuesj-900	77	1	when	when	SCONJ
cuesj-900	77	2	the	the	DET
cuesj-900	77	3	algorithm	algorithm	NOUN
cuesj-900	77	4	operates	operate	VERB
cuesj-900	77	5	satisfactorily	satisfactorily	ADV
cuesj-900	77	6	,	,	PUNCT
cuesj-900	77	7	learning	learning	NOUN
cuesj-900	77	8	comes	come	VERB
cuesj-900	77	9	to	to	ADP
cuesj-900	77	10	an	an	DET
cuesj-900	77	11	end	end	NOUN
cuesj-900	77	12	.	.	PUNCT
cuesj-900	78	1	the	the	DET
cuesj-900	78	2	algorithm	algorithm	NOUN
cuesj-900	78	3	iteratively	iteratively	ADV
cuesj-900	78	4	makes	make	VERB
cuesj-900	78	5	predictions	prediction	NOUN
cuesj-900	78	6	on	on	ADP
cuesj-900	78	7	the	the	DET
cuesj-900	78	8	training	training	NOUN
cuesj-900	78	9	data	datum	NOUN
cuesj-900	78	10	and	and	CCONJ
cuesj-900	78	11	is	be	AUX
cuesj-900	78	12	corrected	correct	VERB
cuesj-900	78	13	by	by	ADP
cuesj-900	78	14	the	the	DET
cuesj-900	78	15	teacher	teacher	NOUN
cuesj-900	78	16	as	as	SCONJ
cuesj-900	78	17	we	we	PRON
cuesj-900	78	18	are	be	AUX
cuesj-900	78	19	aware	aware	ADJ
cuesj-900	78	20	of	of	ADP
cuesj-900	78	21	the	the	DET
cuesj-900	78	22	appropriate	appropriate	ADJ
cuesj-900	78	23	responses	response	NOUN
cuesj-900	78	24	.	.	PUNCT
cuesj-900	79	1	learning	learning	NOUN
cuesj-900	79	2	comes	come	VERB
cuesj-900	79	3	to	to	ADP
cuesj-900	79	4	a	a	DET
cuesj-900	79	5	conclusion	conclusion	NOUN
cuesj-900	79	6	when	when	SCONJ
cuesj-900	79	7	the	the	DET
cuesj-900	79	8	algorithm	algorithm	NOUN
cuesj-900	79	9	works	work	VERB
cuesj-900	79	10	satisfactorily.[12	satisfactorily.[12	PROPN
cuesj-900	79	11	]	]	X
cuesj-900	79	12	unsupervised	unsupervised	ADJ
cuesj-900	79	13	learning	learning	NOUN
cuesj-900	79	14	it	it	PRON
cuesj-900	79	15	is	be	AUX
cuesj-900	79	16	a	a	DET
cuesj-900	79	17	machine	machine	NOUN
cuesj-900	79	18	learning	learning	NOUN
cuesj-900	79	19	technique	technique	NOUN
cuesj-900	79	20	used	use	VERB
cuesj-900	79	21	when	when	SCONJ
cuesj-900	79	22	training	training	NOUN
cuesj-900	79	23	data	data	NOUN
cuesj-900	79	24	is	be	AUX
cuesj-900	79	25	not	not	PART
cuesj-900	79	26	classified	classify	VERB
cuesj-900	79	27	or	or	CCONJ
cuesj-900	79	28	labeled	label	VERB
cuesj-900	79	29	.	.	PUNCT
cuesj-900	80	1	to	to	PART
cuesj-900	80	2	describe	describe	VERB
cuesj-900	80	3	a	a	DET
cuesj-900	80	4	hidden	hide	VERB
cuesj-900	80	5	structure	structure	NOUN
cuesj-900	80	6	,	,	PUNCT
cuesj-900	80	7	unsupervised	unsupervised	ADJ
cuesj-900	80	8	learning	learning	NOUN
cuesj-900	80	9	examines	examine	NOUN
cuesj-900	80	10	systems	system	NOUN
cuesj-900	80	11	’	'	PUNCT
cuesj-900	80	12	capacity	capacity	NOUN
cuesj-900	80	13	to	to	PART
cuesj-900	80	14	infer	infer	VERB
cuesj-900	80	15	a	a	DET
cuesj-900	80	16	function	function	NOUN
cuesj-900	80	17	from	from	ADP
cuesj-900	80	18	unlabeled	unlabeled	ADJ
cuesj-900	80	19	data	datum	NOUN
cuesj-900	80	20	.	.	PUNCT
cuesj-900	81	1	the	the	DET
cuesj-900	81	2	system	system	NOUN
cuesj-900	81	3	examines	examine	VERB
cuesj-900	81	4	the	the	DET
cuesj-900	81	5	data	datum	NOUN
cuesj-900	81	6	and	and	CCONJ
cuesj-900	81	7	may	may	AUX
cuesj-900	81	8	utilize	utilize	VERB
cuesj-900	81	9	datasets	dataset	NOUN
cuesj-900	81	10	to	to	PART
cuesj-900	81	11	infer	infer	VERB
cuesj-900	81	12	hidden	hidden	ADJ
cuesj-900	81	13	structures	structure	NOUN
cuesj-900	81	14	from	from	ADP
cuesj-900	81	15	unlabeled	unlabeled	ADJ
cuesj-900	81	16	data	datum	NOUN
cuesj-900	81	17	even	even	ADV
cuesj-900	81	18	when	when	SCONJ
cuesj-900	81	19	it	it	PRON
cuesj-900	81	20	is	be	AUX
cuesj-900	81	21	unable	unable	ADJ
cuesj-900	81	22	to	to	PART
cuesj-900	81	23	select	select	VERB
cuesj-900	81	24	the	the	DET
cuesj-900	81	25	ideal	ideal	ADJ
cuesj-900	81	26	output	output	NOUN
cuesj-900	81	27	.	.	PUNCT
cuesj-900	82	1	unsupervised	unsupervised	ADJ
cuesj-900	82	2	learning	learning	NOUN
cuesj-900	82	3	occurs	occur	VERB
cuesj-900	82	4	when	when	SCONJ
cuesj-900	82	5	there	there	PRON
cuesj-900	82	6	are	be	VERB
cuesj-900	82	7	just	just	ADV
cuesj-900	82	8	input	input	NOUN
cuesj-900	82	9	variables	variable	NOUN
cuesj-900	82	10	and	and	CCONJ
cuesj-900	82	11	no	no	DET
cuesj-900	82	12	matching	match	VERB
cuesj-900	82	13	output	output	NOUN
cuesj-900	82	14	variables	variable	NOUN
cuesj-900	82	15	(	(	PUNCT
cuesj-900	82	16	x	x	NOUN
cuesj-900	82	17	)	)	PUNCT
cuesj-900	82	18	.	.	PUNCT
cuesj-900	83	1	unsupervised	unsupervised	ADJ
cuesj-900	83	2	learning	learning	NOUN
cuesj-900	83	3	simulates	simulate	VERB
cuesj-900	83	4	the	the	DET
cuesj-900	83	5	underlying	underlie	VERB
cuesj-900	83	6	distribution	distribution	NOUN
cuesj-900	83	7	or	or	CCONJ
cuesj-900	83	8	structure	structure	NOUN
cuesj-900	83	9	of	of	ADP
cuesj-900	83	10	data	datum	NOUN
cuesj-900	83	11	to	to	PART
cuesj-900	83	12	understand	understand	VERB
cuesj-900	83	13	it	it	PRON
cuesj-900	83	14	better	well	ADV
cuesj-900	83	15	.	.	PUNCT
cuesj-900	84	1	these	these	PRON
cuesj-900	84	2	are	be	AUX
cuesj-900	84	3	referred	refer	VERB
cuesj-900	84	4	to	to	ADP
cuesj-900	84	5	as	as	ADP
cuesj-900	84	6	unsupervised	unsupervised	ADJ
cuesj-900	84	7	learning	learning	NOUN
cuesj-900	84	8	as	as	SCONJ
cuesj-900	84	9	there	there	PRON
cuesj-900	84	10	are	be	VERB
cuesj-900	84	11	no	no	DET
cuesj-900	84	12	correct	correct	ADJ
cuesj-900	84	13	answers	answer	NOUN
cuesj-900	84	14	and	and	CCONJ
cuesj-900	84	15	no	no	DET
cuesj-900	84	16	teachers	teacher	NOUN
cuesj-900	84	17	,	,	PUNCT
cuesj-900	84	18	in	in	ADP
cuesj-900	84	19	contrast	contrast	NOUN
cuesj-900	84	20	to	to	ADP
cuesj-900	84	21	the	the	DET
cuesj-900	84	22	supervised	supervised	ADJ
cuesj-900	84	23	learning	learning	NOUN
cuesj-900	84	24	that	that	PRON
cuesj-900	84	25	was	be	AUX
cuesj-900	84	26	previously	previously	ADV
cuesj-900	84	27	discussed	discuss	VERB
cuesj-900	84	28	.	.	PUNCT
cuesj-900	85	1	it	it	PRON
cuesj-900	85	2	is	be	AUX
cuesj-900	85	3	up	up	ADP
cuesj-900	85	4	to	to	ADP
cuesj-900	85	5	the	the	DET
cuesj-900	85	6	algorithms	algorithm	NOUN
cuesj-900	85	7	to	to	PART
cuesj-900	85	8	find	find	VERB
cuesj-900	85	9	and	and	CCONJ
cuesj-900	85	10	display	display	VERB
cuesj-900	85	11	the	the	DET
cuesj-900	85	12	fascinating	fascinating	ADJ
cuesj-900	85	13	structure	structure	NOUN
cuesj-900	85	14	of	of	ADP
cuesj-900	85	15	the	the	DET
cuesj-900	85	16	data	datum	NOUN
cuesj-900	85	17	.	.	PUNCT
cuesj-900	86	1	issues	issue	NOUN
cuesj-900	86	2	with	with	ADP
cuesj-900	86	3	grouping	grouping	NOUN
cuesj-900	86	4	and	and	CCONJ
cuesj-900	86	5	association	association	NOUN
cuesj-900	86	6	abound	abound	NOUN
cuesj-900	86	7	in	in	ADP
cuesj-900	86	8	unsupervised	unsupervised	ADJ
cuesj-900	86	9	learning	learning	NOUN
cuesj-900	86	10	.	.	PUNCT
cuesj-900	87	1	in	in	ADP
cuesj-900	87	2	contrast	contrast	NOUN
cuesj-900	87	3	to	to	ADP
cuesj-900	87	4	association	association	NOUN
cuesj-900	87	5	rule	rule	NOUN
cuesj-900	87	6	learning	learning	NOUN
cuesj-900	87	7	problems	problem	NOUN
cuesj-900	87	8	,	,	PUNCT
cuesj-900	87	9	where	where	SCONJ
cuesj-900	87	10	you	you	PRON
cuesj-900	87	11	are	be	AUX
cuesj-900	87	12	looking	look	VERB
cuesj-900	87	13	for	for	ADP
cuesj-900	87	14	rules	rule	NOUN
cuesj-900	87	15	that	that	PRON
cuesj-900	87	16	adequately	adequately	ADV
cuesj-900	87	17	describe	describe	VERB
cuesj-900	87	18	significant	significant	ADJ
cuesj-900	87	19	portions	portion	NOUN
cuesj-900	87	20	of	of	ADP
cuesj-900	87	21	your	your	PRON
cuesj-900	87	22	data	datum	NOUN
cuesj-900	87	23	,	,	PUNCT
cuesj-900	87	24	such	such	ADJ
cuesj-900	87	25	as	as	ADP
cuesj-900	87	26	people	people	NOUN
cuesj-900	87	27	who	who	PRON
cuesj-900	87	28	buy	buy	VERB
cuesj-900	87	29	x	x	PUNCT
cuesj-900	87	30	also	also	ADV
cuesj-900	87	31	tend	tend	VERB
cuesj-900	87	32	to	to	PART
cuesj-900	87	33	buy	buy	VERB
cuesj-900	87	34	y	y	PROPN
cuesj-900	87	35	,	,	PUNCT
cuesj-900	87	36	clustering	cluster	VERB
cuesj-900	87	37	problems	problem	NOUN
cuesj-900	87	38	are	be	AUX
cuesj-900	87	39	looking	look	VERB
cuesj-900	87	40	for	for	ADP
cuesj-900	87	41	the	the	DET
cuesj-900	87	42	inherent	inherent	ADJ
cuesj-900	87	43	groupings	grouping	NOUN
cuesj-900	87	44	in	in	ADP
cuesj-900	87	45	the	the	DET
cuesj-900	87	46	data	datum	NOUN
cuesj-900	87	47	,	,	PUNCT
cuesj-900	87	48	like	like	ADP
cuesj-900	87	49	grouping	group	VERB
cuesj-900	87	50	customers	customer	NOUN
cuesj-900	87	51	based	base	VERB
cuesj-900	87	52	on	on	ADP
cuesj-900	87	53	their	their	PRON
cuesj-900	87	54	purchasing	purchase	VERB
cuesj-900	87	55	behavior	behavior	NOUN
cuesj-900	87	56	.	.	PUNCT
cuesj-900	88	1	unsupervised	unsupervised	ADJ
cuesj-900	88	2	learning	learning	NOUN
cuesj-900	88	3	techniques	technique	NOUN
cuesj-900	88	4	include	include	VERB
cuesj-900	88	5	the	the	DET
cuesj-900	88	6	a	a	DET
cuesj-900	88	7	priori	priori	ADJ
cuesj-900	88	8	methodology	methodology	NOUN
cuesj-900	88	9	for	for	ADP
cuesj-900	88	10	learning	learn	VERB
cuesj-900	88	11	association	association	NOUN
cuesj-900	88	12	rules	rule	NOUN
cuesj-900	88	13	and	and	CCONJ
cuesj-900	88	14	k	k	NOUN
cuesj-900	88	15	-	-	PUNCT
cuesj-900	88	16	means	means	NOUN
cuesj-900	88	17	for	for	ADP
cuesj-900	88	18	classifying	classify	VERB
cuesj-900	88	19	problems.[12	problems.[12	NOUN
cuesj-900	88	20	]	]	PUNCT
cuesj-900	88	21	what	what	DET
cuesj-900	88	22	kinds	kind	NOUN
cuesj-900	88	23	of	of	ADP
cuesj-900	88	24	data	datum	NOUN
cuesj-900	88	25	can	can	AUX
cuesj-900	88	26	be	be	AUX
cuesj-900	88	27	mined	mine	VERB
cuesj-900	88	28	?	?	PUNCT
cuesj-900	89	1	any	any	DET
cuesj-900	89	2	sort	sort	NOUN
cuesj-900	89	3	of	of	ADP
cuesj-900	89	4	data	datum	NOUN
cuesj-900	89	5	may	may	AUX
cuesj-900	89	6	be	be	AUX
cuesj-900	89	7	used	use	VERB
cuesj-900	89	8	in	in	ADP
cuesj-900	89	9	data	datum	NOUN
cuesj-900	89	10	mining	mining	NOUN
cuesj-900	89	11	,	,	PUNCT
cuesj-900	89	12	as	as	ADV
cuesj-900	89	13	long	long	ADV
cuesj-900	89	14	as	as	SCONJ
cuesj-900	89	15	it	it	PRON
cuesj-900	89	16	is	be	AUX
cuesj-900	89	17	pertinent	pertinent	ADJ
cuesj-900	89	18	to	to	ADP
cuesj-900	89	19	the	the	DET
cuesj-900	89	20	application	application	NOUN
cuesj-900	89	21	for	for	ADP
cuesj-900	89	22	which	which	PRON
cuesj-900	89	23	it	it	PRON
cuesj-900	89	24	is	be	AUX
cuesj-900	89	25	intended	intend	VERB
cuesj-900	89	26	.	.	PUNCT
cuesj-900	90	1	the	the	DET
cuesj-900	90	2	most	most	ADV
cuesj-900	90	3	basic	basic	ADJ
cuesj-900	90	4	sorts	sort	NOUN
cuesj-900	90	5	of	of	ADP
cuesj-900	90	6	data	datum	NOUN
cuesj-900	90	7	for	for	ADP
cuesj-900	90	8	mining	mining	NOUN
cuesj-900	90	9	applications	application	NOUN
cuesj-900	90	10	are	be	AUX
cuesj-900	90	11	databases	database	NOUN
cuesj-900	90	12	.	.	PUNCT
cuesj-900	91	1	it	it	PRON
cuesj-900	91	2	is	be	AUX
cuesj-900	91	3	also	also	ADV
cuesj-900	91	4	possible	possible	ADJ
cuesj-900	91	5	to	to	AUX
cuesj-900	91	6	mine	mine	VERB
cuesj-900	91	7	other	other	ADJ
cuesj-900	91	8	forms	form	NOUN
cuesj-900	91	9	of	of	ADP
cuesj-900	91	10	data	datum	NOUN
cuesj-900	91	11	,	,	PUNCT
cuesj-900	91	12	including	include	VERB
cuesj-900	91	13	data	datum	NOUN
cuesj-900	91	14	streams	stream	NOUN
cuesj-900	91	15	,	,	PUNCT
cuesj-900	91	16	ordered	order	VERB
cuesj-900	91	17	/	/	SYM
cuesj-900	91	18	sequence	sequence	NOUN
cuesj-900	91	19	,	,	PUNCT
cuesj-900	91	20	graph	graph	NOUN
cuesj-900	91	21	or	or	CCONJ
cuesj-900	91	22	networked	networked	ADJ
cuesj-900	91	23	,	,	PUNCT
cuesj-900	91	24	geographical	geographical	ADJ
cuesj-900	91	25	,	,	PUNCT
cuesj-900	91	26	text	text	NOUN
cuesj-900	91	27	,	,	PUNCT
cuesj-900	91	28	and	and	CCONJ
cuesj-900	91	29	multimedia	multimedia	PROPN
cuesj-900	91	30	data	datum	NOUN
cuesj-900	91	31	.	.	PUNCT
cuesj-900	92	1	data	datum	NOUN
cuesj-900	92	2	mining	mining	NOUN
cuesj-900	92	3	will	will	AUX
cuesj-900	92	4	undoubtedly	undoubtedly	ADV
cuesj-900	92	5	continue	continue	VERB
cuesj-900	92	6	to	to	PART
cuesj-900	92	7	include	include	VERB
cuesj-900	92	8	new	new	ADJ
cuesj-900	92	9	data	datum	NOUN
cuesj-900	92	10	kinds	kind	NOUN
cuesj-900	92	11	as	as	SCONJ
cuesj-900	92	12	they	they	PRON
cuesj-900	92	13	emerge	emerge	VERB
cuesj-900	92	14	.	.	PUNCT
cuesj-900	93	1	machine	machine	NOUN
cuesj-900	93	2	learning	learning	NOUN
cuesj-900	93	3	models	model	NOUN
cuesj-900	93	4	in	in	ADP
cuesj-900	93	5	classification	classification	NOUN
cuesj-900	93	6	method	method	NOUN
cuesj-900	93	7	when	when	SCONJ
cuesj-900	93	8	semantic	semantic	ADJ
cuesj-900	93	9	classes	class	NOUN
cuesj-900	93	10	for	for	ADP
cuesj-900	93	11	particular	particular	ADJ
cuesj-900	93	12	items	item	NOUN
cuesj-900	93	13	are	be	AUX
cuesj-900	93	14	already	already	ADV
cuesj-900	93	15	known	know	VERB
cuesj-900	93	16	,	,	PUNCT
cuesj-900	93	17	classification	classification	NOUN
cuesj-900	93	18	refers	refer	VERB
cuesj-900	93	19	to	to	ADP
cuesj-900	93	20	the	the	DET
cuesj-900	93	21	process	process	NOUN
cuesj-900	93	22	of	of	ADP
cuesj-900	93	23	determining	determine	VERB
cuesj-900	93	24	those	those	DET
cuesj-900	93	25	classes	class	NOUN
cuesj-900	93	26	based	base	VERB
cuesj-900	93	27	on	on	ADP
cuesj-900	93	28	those	those	DET
cuesj-900	93	29	objects	object	NOUN
cuesj-900	93	30	’	'	PUNCT
cuesj-900	93	31	properties	property	NOUN
cuesj-900	93	32	.	.	PUNCT
cuesj-900	94	1	it	it	PRON
cuesj-900	94	2	involves	involve	VERB
cuesj-900	94	3	putting	put	VERB
cuesj-900	94	4	fresh	fresh	ADJ
cuesj-900	94	5	observed	observe	VERB
cuesj-900	94	6	samples	sample	NOUN
cuesj-900	94	7	into	into	ADP
cuesj-900	94	8	a	a	DET
cuesj-900	94	9	specific	specific	ADJ
cuesj-900	94	10	class	class	NOUN
cuesj-900	94	11	by	by	ADP
cuesj-900	94	12	comparing	compare	VERB
cuesj-900	94	13	the	the	DET
cuesj-900	94	14	sample	sample	NOUN
cuesj-900	94	15	’s	’s	PART
cuesj-900	94	16	characteristics	characteristic	NOUN
cuesj-900	94	17	to	to	ADP
cuesj-900	94	18	an	an	DET
cuesj-900	94	19	already	already	ADV
cuesj-900	94	20	existing	exist	VERB
cuesj-900	94	21	class	class	NOUN
cuesj-900	94	22	.	.	PUNCT
cuesj-900	95	1	by	by	ADP
cuesj-900	95	2	building	build	VERB
cuesj-900	95	3	a	a	DET
cuesj-900	95	4	model	model	NOUN
cuesj-900	95	5	using	use	VERB
cuesj-900	95	6	earlier	early	ADJ
cuesj-900	95	7	data	datum	NOUN
cuesj-900	95	8	,	,	PUNCT
cuesj-900	95	9	it	it	PRON
cuesj-900	95	10	may	may	AUX
cuesj-900	95	11	then	then	ADV
cuesj-900	95	12	decide	decide	VERB
cuesj-900	95	13	on	on	ADP
cuesj-900	95	14	the	the	DET
cuesj-900	95	15	unobserved	unobserved	ADJ
cuesj-900	95	16	cases	case	NOUN
cuesj-900	95	17	.	.	PUNCT
cuesj-900	96	1	therefore	therefore	ADV
cuesj-900	96	2	,	,	PUNCT
cuesj-900	96	3	it	it	PRON
cuesj-900	96	4	finds	find	VERB
cuesj-900	96	5	a	a	DET
cuesj-900	96	6	model	model	NOUN
cuesj-900	96	7	(	(	PUNCT
cuesj-900	96	8	or	or	CCONJ
cuesj-900	96	9	function	function	NOUN
cuesj-900	96	10	)	)	PUNCT
cuesj-900	96	11	that	that	PRON
cuesj-900	96	12	clarifies	clarify	VERB
cuesj-900	96	13	and	and	CCONJ
cuesj-900	96	14	divides	divide	VERB
cuesj-900	96	15	groups	group	NOUN
cuesj-900	96	16	of	of	ADP
cuesj-900	96	17	data	datum	NOUN
cuesj-900	96	18	or	or	CCONJ
cuesj-900	96	19	ideas	idea	NOUN
cuesj-900	96	20	.	.	PUNCT
cuesj-900	97	1	it	it	PRON
cuesj-900	97	2	looks	look	VERB
cuesj-900	97	3	at	at	ADP
cuesj-900	97	4	the	the	DET
cuesj-900	97	5	relationship	relationship	NOUN
cuesj-900	97	6	between	between	ADP
cuesj-900	97	7	the	the	DET
cuesj-900	97	8	values	value	NOUN
cuesj-900	97	9	of	of	ADP
cuesj-900	97	10	the	the	DET
cuesj-900	97	11	variables	variable	NOUN
cuesj-900	97	12	for	for	ADP
cuesj-900	97	13	each	each	DET
cuesj-900	97	14	row	row	NOUN
cuesj-900	97	15	and	and	CCONJ
cuesj-900	97	16	the	the	DET
cuesj-900	97	17	label	label	NOUN
cuesj-900	97	18	assigned	assign	VERB
cuesj-900	97	19	to	to	ADP
cuesj-900	97	20	that	that	DET
cuesj-900	97	21	row	row	NOUN
cuesj-900	97	22	using	use	VERB
cuesj-900	97	23	data	datum	NOUN
cuesj-900	97	24	mining	mining	NOUN
cuesj-900	97	25	techniques	technique	NOUN
cuesj-900	97	26	.	.	PUNCT
cuesj-900	98	1	various	various	ADJ
cuesj-900	98	2	classification	classification	NOUN
cuesj-900	98	3	methods	method	NOUN
cuesj-900	98	4	exist	exist	VERB
cuesj-900	98	5	,	,	PUNCT
cuesj-900	98	6	including	include	VERB
cuesj-900	98	7	naïve	naïve	ADJ
cuesj-900	98	8	bayes	bayes	NOUN
cuesj-900	98	9	(	(	PUNCT
cuesj-900	98	10	nb	nb	PROPN
cuesj-900	98	11	)	)	PUNCT
cuesj-900	98	12	,	,	PUNCT
cuesj-900	98	13	logistic	logistic	ADJ
cuesj-900	98	14	regression	regression	NOUN
cuesj-900	98	15	,	,	PUNCT
cuesj-900	98	16	svm	svm	PROPN
cuesj-900	98	17	,	,	PUNCT
cuesj-900	98	18	knn	knn	PROPN
cuesj-900	98	19	,	,	PUNCT
cuesj-900	98	20	dt	dt	X
cuesj-900	98	21	,	,	PUNCT
cuesj-900	98	22	and	and	CCONJ
cuesj-900	98	23	artificial	artificial	ADJ
cuesj-900	98	24	neural	neural	ADJ
cuesj-900	98	25	network	network	NOUN
cuesj-900	98	26	(	(	PUNCT
cuesj-900	98	27	ann	ann	PROPN
cuesj-900	98	28	)	)	PUNCT
cuesj-900	98	29	.	.	PUNCT
cuesj-900	99	1	as	as	ADP
cuesj-900	99	2	a	a	DET
cuesj-900	99	3	result	result	NOUN
cuesj-900	99	4	,	,	PUNCT
cuesj-900	99	5	the	the	DET
cuesj-900	99	6	extracted	extract	VERB
cuesj-900	99	7	data	datum	NOUN
cuesj-900	99	8	may	may	AUX
cuesj-900	99	9	be	be	AUX
cuesj-900	99	10	used	use	VERB
cuesj-900	99	11	to	to	PART
cuesj-900	99	12	predict	predict	VERB
cuesj-900	99	13	a	a	DET
cuesj-900	99	14	label	label	NOUN
cuesj-900	99	15	for	for	ADP
cuesj-900	99	16	a	a	DET
cuesj-900	99	17	new	new	ADJ
cuesj-900	99	18	row	row	NOUN
cuesj-900	99	19	when	when	SCONJ
cuesj-900	99	20	it	it	PRON
cuesj-900	99	21	is	be	AUX
cuesj-900	99	22	supplied	supply	VERB
cuesj-900	99	23	to	to	ADP
cuesj-900	99	24	the	the	DET
cuesj-900	99	25	classifier	classifier	NOUN
cuesj-900	99	26	based	base	VERB
cuesj-900	99	27	on	on	ADP
cuesj-900	99	28	the	the	DET
cuesj-900	99	29	variable	variable	ADJ
cuesj-900	99	30	values	value	NOUN
cuesj-900	99	31	that	that	PRON
cuesj-900	99	32	define	define	VERB
cuesj-900	99	33	it	it	PRON
cuesj-900	99	34	.	.	PUNCT
cuesj-900	100	1	these	these	DET
cuesj-900	100	2	methods	method	NOUN
cuesj-900	100	3	use	use	VERB
cuesj-900	100	4	different	different	ADJ
cuesj-900	100	5	ways	way	NOUN
cuesj-900	100	6	to	to	PART
cuesj-900	100	7	express	express	VERB
cuesj-900	100	8	these	these	DET
cuesj-900	100	9	interactions	interaction	NOUN
cuesj-900	100	10	.	.	PUNCT
cuesj-900	101	1	because	because	SCONJ
cuesj-900	101	2	these	these	DET
cuesj-900	101	3	correlations	correlation	NOUN
cuesj-900	101	4	change	change	VERB
cuesj-900	101	5	between	between	ADP
cuesj-900	101	6	datasets	dataset	NOUN
cuesj-900	101	7	,	,	PUNCT
cuesj-900	101	8	it	it	PRON
cuesj-900	101	9	is	be	AUX
cuesj-900	101	10	critical	critical	ADJ
cuesj-900	101	11	that	that	SCONJ
cuesj-900	101	12	the	the	DET
cuesj-900	101	13	classifiers	classifier	NOUN
cuesj-900	101	14	be	be	AUX
cuesj-900	101	15	trained	train	VERB
cuesj-900	101	16	using	use	VERB
cuesj-900	101	17	labeled	label	VERB
cuesj-900	101	18	training	training	NOUN
cuesj-900	101	19	data	datum	NOUN
cuesj-900	101	20	.	.	PUNCT
cuesj-900	102	1	classification	classification	NOUN
cuesj-900	102	2	is	be	AUX
cuesj-900	102	3	one	one	NUM
cuesj-900	102	4	of	of	ADP
cuesj-900	102	5	the	the	DET
cuesj-900	102	6	findings	finding	NOUN
cuesj-900	102	7	and	and	CCONJ
cuesj-900	102	8	predicting	predicting	NOUN
cuesj-900	102	9	process	process	NOUN
cuesj-900	102	10	a	a	DET
cuesj-900	102	11	result	result	NOUN
cuesj-900	102	12	from	from	ADP
cuesj-900	102	13	a	a	DET
cuesj-900	102	14	set	set	NOUN
cuesj-900	102	15	of	of	ADP
cuesj-900	102	16	data	datum	NOUN
cuesj-900	102	17	.	.	PUNCT
cuesj-900	103	1	the	the	DET
cuesj-900	103	2	algorithm	algorithm	NOUN
cuesj-900	103	3	uses	use	VERB
cuesj-900	103	4	a	a	DET
cuesj-900	103	5	collection	collection	NOUN
cuesj-900	103	6	of	of	ADP
cuesj-900	103	7	variables	variable	NOUN
cuesj-900	103	8	and	and	CCONJ
cuesj-900	103	9	a	a	DET
cuesj-900	103	10	training	training	NOUN
cuesj-900	103	11	set	set	VERB
cuesj-900	103	12	with	with	ADP
cuesj-900	103	13	the	the	DET
cuesj-900	103	14	property	property	NOUN
cuesj-900	103	15	of	of	ADP
cuesj-900	103	16	each	each	DET
cuesj-900	103	17	variable	variable	NOUN
cuesj-900	103	18	to	to	PART
cuesj-900	103	19	predict	predict	VERB
cuesj-900	103	20	the	the	DET
cuesj-900	103	21	outcome	outcome	NOUN
cuesj-900	103	22	.	.	PUNCT
cuesj-900	104	1	the	the	DET
cuesj-900	104	2	outcome	outcome	NOUN
cuesj-900	104	3	is	be	AUX
cuesj-900	104	4	often	often	ADV
cuesj-900	104	5	referred	refer	VERB
cuesj-900	104	6	to	to	ADP
cuesj-900	104	7	as	as	ADP
cuesj-900	104	8	a	a	DET
cuesj-900	104	9	target	target	NOUN
cuesj-900	104	10	or	or	CCONJ
cuesj-900	104	11	forecast	forecast	NOUN
cuesj-900	104	12	variable.[13	variable.[13	NOUN
cuesj-900	104	13	]	]	PUNCT
cuesj-900	104	14	this	this	DET
cuesj-900	104	15	article	article	NOUN
cuesj-900	104	16	discussed	discuss	VERB
cuesj-900	104	17	dt	dt	PROPN
cuesj-900	104	18	,	,	PUNCT
cuesj-900	104	19	ann	ann	PROPN
cuesj-900	104	20	,	,	PUNCT
cuesj-900	104	21	knn	knn	PROPN
cuesj-900	104	22	.	.	PUNCT
cuesj-900	105	1	dt	dt	X
cuesj-900	106	1	it	it	PRON
cuesj-900	106	2	is	be	AUX
cuesj-900	106	3	known	know	VERB
cuesj-900	106	4	that	that	SCONJ
cuesj-900	106	5	one	one	NUM
cuesj-900	106	6	of	of	ADP
cuesj-900	106	7	the	the	DET
cuesj-900	106	8	models	model	NOUN
cuesj-900	106	9	used	use	VERB
cuesj-900	106	10	in	in	ADP
cuesj-900	106	11	machine	machine	NOUN
cuesj-900	106	12	learning	learning	NOUN
cuesj-900	106	13	is	be	AUX
cuesj-900	106	14	dts.[14	dts.[14	ADJ
cuesj-900	106	15	]	]	PUNCT
cuesj-900	106	16	a	a	DET
cuesj-900	106	17	non	non	ADJ
cuesj-900	106	18	-	-	ADJ
cuesj-900	106	19	parametric	parametric	ADJ
cuesj-900	106	20	,	,	PUNCT
cuesj-900	106	21	intricate	intricate	ADJ
cuesj-900	106	22	,	,	PUNCT
cuesj-900	106	23	and	and	CCONJ
cuesj-900	106	24	computationally	computationally	ADV
cuesj-900	106	25	costly	costly	ADJ
cuesj-900	106	26	sorting	sorting	NOUN
cuesj-900	106	27	technique	technique	NOUN
cuesj-900	106	28	is	be	AUX
cuesj-900	106	29	the	the	DET
cuesj-900	106	30	dt	dt	PROPN
cuesj-900	106	31	approach	approach	NOUN
cuesj-900	106	32	,	,	PUNCT
cuesj-900	106	33	often	often	ADV
cuesj-900	106	34	known	know	VERB
cuesj-900	106	35	as	as	ADP
cuesj-900	106	36	the	the	DET
cuesj-900	106	37	recursive	recursive	ADJ
cuesj-900	106	38	partitioning	partitioning	ADJ
cuesj-900	106	39	algorithm	algorithm	NOUN
cuesj-900	106	40	.	.	PUNCT
cuesj-900	107	1	the	the	DET
cuesj-900	107	2	main	main	ADJ
cuesj-900	107	3	idea	idea	NOUN
cuesj-900	107	4	is	be	AUX
cuesj-900	107	5	to	to	PART
cuesj-900	107	6	keep	keep	VERB
cuesj-900	107	7	subdividing	subdivide	VERB
cuesj-900	107	8	the	the	DET
cuesj-900	107	9	subsample	subsample	NOUN
cuesj-900	107	10	into	into	ADP
cuesj-900	107	11	subgroups	subgroup	NOUN
cuesj-900	107	12	until	until	SCONJ
cuesj-900	107	13	it	it	PRON
cuesj-900	107	14	allows	allow	VERB
cuesj-900	107	15	for	for	ADP
cuesj-900	107	16	decision	decision	NOUN
cuesj-900	107	17	-	-	PUNCT
cuesj-900	107	18	making	making	NOUN
cuesj-900	107	19	,	,	PUNCT
cuesj-900	107	20	beginning	begin	VERB
cuesj-900	107	21	with	with	ADP
cuesj-900	107	22	dividing	divide	VERB
cuesj-900	107	23	the	the	DET
cuesj-900	107	24	sample	sample	NOUN
cuesj-900	107	25	responses	response	NOUN
cuesj-900	107	26	into	into	ADP
cuesj-900	107	27	fresh	fresh	ADJ
cuesj-900	107	28	subsamples	subsample	NOUN
cuesj-900	107	29	that	that	PRON
cuesj-900	107	30	are	be	AUX
cuesj-900	107	31	as	as	ADV
cuesj-900	107	32	similar	similar	ADJ
cuesj-900	107	33	to	to	ADP
cuesj-900	107	34	one	one	NUM
cuesj-900	107	35	another	another	DET
cuesj-900	107	36	and	and	CCONJ
cuesj-900	107	37	distinct	distinct	ADJ
cuesj-900	107	38	from	from	ADP
cuesj-900	107	39	them	they	PRON
cuesj-900	107	40	as	as	ADP
cuesj-900	107	41	practicable	practicable	ADJ
cuesj-900	107	42	.	.	PUNCT
cuesj-900	108	1	while	while	SCONJ
cuesj-900	108	2	the	the	DET
cuesj-900	108	3	nodes	node	NOUN
cuesj-900	108	4	reflect	reflect	VERB
cuesj-900	108	5	the	the	DET
cuesj-900	108	6	sub	sub	NOUN
cuesj-900	108	7	-	-	NOUN
cuesj-900	108	8	samples	sample	NOUN
cuesj-900	108	9	,	,	PUNCT
cuesj-900	108	10	the	the	DET
cuesj-900	108	11	root	root	NOUN
cuesj-900	108	12	node	node	NOUN
cuesj-900	108	13	represents	represent	VERB
cuesj-900	108	14	the	the	DET
cuesj-900	108	15	total	total	ADJ
cuesj-900	108	16	sample	sample	NOUN
cuesj-900	108	17	.	.	PUNCT
cuesj-900	109	1	figure	figure	NOUN
cuesj-900	109	2	2	2	NUM
cuesj-900	109	3	shows	show	VERB
cuesj-900	109	4	a	a	DET
cuesj-900	109	5	dt	dt	NOUN
cuesj-900	109	6	that	that	PRON
cuesj-900	109	7	categorizes	categorize	VERB
cuesj-900	109	8	weather	weather	NOUN
cuesj-900	109	9	conditions	condition	NOUN
cuesj-900	109	10	for	for	ADP
cuesj-900	109	11	those	those	PRON
cuesj-900	109	12	as	as	ADV
cuesj-900	109	13	good	good	ADJ
cuesj-900	109	14	and	and	CCONJ
cuesj-900	109	15	bad	bad	ADJ
cuesj-900	109	16	.	.	PUNCT
cuesj-900	110	1	the	the	DET
cuesj-900	110	2	dt	dt	PROPN
cuesj-900	110	3	models	model	NOUN
cuesj-900	110	4	are	be	AUX
cuesj-900	110	5	built	build	VERB
cuesj-900	110	6	employing	employ	VERB
cuesj-900	110	7	the	the	DET
cuesj-900	110	8	greedy	greedy	ADJ
cuesj-900	110	9	method	method	NOUN
cuesj-900	110	10	,	,	PUNCT
cuesj-900	110	11	and	and	CCONJ
cuesj-900	110	12	the	the	DET
cuesj-900	110	13	best	good	ADJ
cuesj-900	110	14	split	split	NOUN
cuesj-900	110	15	is	be	AUX
cuesj-900	110	16	that	that	PRON
cuesj-900	110	17	which	which	PRON
cuesj-900	110	18	minimizes	minimize	VERB
cuesj-900	110	19	the	the	DET
cuesj-900	110	20	weighted	weighted	ADJ
cuesj-900	110	21	drop	drop	NOUN
cuesj-900	110	22	in	in	ADP
cuesj-900	110	23	impurity	impurity	NOUN
cuesj-900	110	24	after	after	ADP
cuesj-900	110	25	evaluating	evaluate	VERB
cuesj-900	110	26	a	a	DET
cuesj-900	110	27	large	large	ADJ
cuesj-900	110	28	number	number	NOUN
cuesj-900	110	29	of	of	ADP
cuesj-900	110	30	variable	variable	ADJ
cuesj-900	110	31	splits	split	VERB
cuesj-900	110	32	at	at	ADP
cuesj-900	110	33	each	each	DET
cuesj-900	110	34	node	node	NOUN
cuesj-900	110	35	:	:	PUNCT
cuesj-900	110	36	�	�	PROPN
cuesj-900	110	37	i	i	NOUN
cuesj-900	111	1	l	l	NOUN
cuesj-900	111	2	l	l	NOUN
cuesj-900	111	3	r	r	NOUN
cuesj-900	111	4	rs	rs	NOUN
cuesj-900	111	5	t	t	NOUN
cuesj-900	112	1	i	i	PRON
cuesj-900	112	2	t	t	X
cuesj-900	112	3	p	p	X
cuesj-900	113	1	i	i	PRON
cuesj-900	113	2	t	t	X
cuesj-900	113	3	p	p	X
cuesj-900	114	1	i	i	PROPN
cuesj-900	114	2	t	t	PROPN
cuesj-900	114	3	,	,	PUNCT
cuesj-900	114	4	�	�	PROPN
cuesj-900	114	5	�	�	PROPN
cuesj-900	114	6	�	�	PROPN
cuesj-900	114	7	�	�	PROPN
cuesj-900	114	8	�	�	PROPN
cuesj-900	114	9	�	�	PROPN
cuesj-900	114	10	�	�	PROPN
cuesj-900	114	11	�	�	PROPN
cuesj-900	114	12	�	�	PROPN
cuesj-900	114	13	�	�	PROPN
cuesj-900	114	14	�	�	PROPN
cuesj-900	114	15	(	(	PUNCT
cuesj-900	114	16	1	1	NUM
cuesj-900	114	17	)	)	PUNCT
cuesj-900	114	18	figure	figure	NOUN
cuesj-900	114	19	1	1	NUM
cuesj-900	114	20	:	:	PUNCT
cuesj-900	114	21	overview	overview	NOUN
cuesj-900	114	22	of	of	ADP
cuesj-900	114	23	machine	machine	NOUN
cuesj-900	114	24	learning	learn	VERB
cuesj-900	114	25	techniques	technique	NOUN
cuesj-900	114	26	mulla	mulla	NOUN
cuesj-900	114	27	and	and	CCONJ
cuesj-900	114	28	demir	demir	PROPN
cuesj-900	114	29	:	:	PUNCT
cuesj-900	114	30	the	the	DET
cuesj-900	114	31	use	use	NOUN
cuesj-900	114	32	of	of	ADP
cuesj-900	114	33	clustering	clustering	NOUN
cuesj-900	114	34	and	and	CCONJ
cuesj-900	114	35	classification	classification	NOUN
cuesj-900	114	36	methods	method	NOUN
cuesj-900	114	37	in	in	ADP
cuesj-900	114	38	machine	machine	NOUN
cuesj-900	114	39	learning	learn	VERB
cuesj-900	114	40	55	55	NUM
cuesj-900	114	41	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-900	114	42	cuesj	cuesj	NOUN
cuesj-900	114	43	2023	2023	NUM
cuesj-900	114	44	,	,	PUNCT
cuesj-900	114	45	7	7	NUM
cuesj-900	114	46	(	(	PUNCT
cuesj-900	114	47	1	1	NUM
cuesj-900	114	48	):	):	PUNCT
cuesj-900	114	49	52	52	NUM
cuesj-900	114	50	-	-	SYM
cuesj-900	114	51	59	59	NUM
cuesj-900	114	52	where	where	SCONJ
cuesj-900	114	53	pl	pl	NOUN
cuesj-900	114	54	and	and	CCONJ
cuesj-900	114	55	pr	pr	NOUN
cuesj-900	114	56	indicate	indicate	VERB
cuesj-900	114	57	the	the	DET
cuesj-900	114	58	percentage	percentage	NOUN
cuesj-900	114	59	of	of	ADP
cuesj-900	114	60	observations	observation	NOUN
cuesj-900	114	61	connected	connect	VERB
cuesj-900	114	62	to	to	ADP
cuesj-900	114	63	the	the	DET
cuesj-900	114	64	node	node	PROPN
cuesj-900	114	65	t	t	PROPN
cuesj-900	114	66	are	be	AUX
cuesj-900	114	67	sent	send	VERB
cuesj-900	114	68	to	to	ADP
cuesj-900	114	69	the	the	DET
cuesj-900	114	70	left	left	ADJ
cuesj-900	114	71	child	child	NOUN
cuesj-900	114	72	node	node	PROPN
cuesj-900	114	73	tl	tl	PROPN
cuesj-900	114	74	or	or	CCONJ
cuesj-900	114	75	right	right	ADJ
cuesj-900	114	76	tr	tr	VERB
cuesj-900	114	77	he	he	PRON
cuesj-900	114	78	relevant	relevant	ADJ
cuesj-900	114	79	chide	chide	VERB
cuesj-900	114	80	node	node	PROPN
cuesj-900	114	81	.	.	PUNCT
cuesj-900	115	1	knn	knn	VERB
cuesj-900	116	1	a	a	DET
cuesj-900	116	2	machine	machine	NOUN
cuesj-900	116	3	learning	learning	NOUN
cuesj-900	116	4	method	method	NOUN
cuesj-900	116	5	for	for	ADP
cuesj-900	116	6	categorizing	categorize	VERB
cuesj-900	116	7	data	datum	NOUN
cuesj-900	116	8	is	be	AUX
cuesj-900	116	9	the	the	DET
cuesj-900	116	10	nearestneighbors	nearestneighbors	PROPN
cuesj-900	116	11	approach	approach	NOUN
cuesj-900	116	12	,	,	PUNCT
cuesj-900	116	13	also	also	ADV
cuesj-900	116	14	known	know	VERB
cuesj-900	116	15	as	as	ADP
cuesj-900	116	16	pattern	pattern	NOUN
cuesj-900	116	17	recognition.[15	recognition.[15	PROPN
cuesj-900	116	18	]	]	PUNCT
cuesj-900	116	19	this	this	DET
cuesj-900	116	20	classifier	classifier	NOUN
cuesj-900	116	21	’s	’s	PART
cuesj-900	116	22	objective	objective	NOUN
cuesj-900	116	23	is	be	AUX
cuesj-900	116	24	to	to	PART
cuesj-900	116	25	select	select	VERB
cuesj-900	116	26	a	a	DET
cuesj-900	116	27	metric	metric	NOUN
cuesj-900	116	28	to	to	PART
cuesj-900	116	29	compare	compare	VERB
cuesj-900	116	30	the	the	DET
cuesj-900	116	31	separation	separation	NOUN
cuesj-900	116	32	between	between	ADP
cuesj-900	116	33	any	any	DET
cuesj-900	116	34	two	two	NUM
cuesj-900	116	35	applicants	applicant	NOUN
cuesj-900	116	36	over	over	ADP
cuesj-900	116	37	the	the	DET
cuesj-900	116	38	whole	whole	ADJ
cuesj-900	116	39	set	set	NOUN
cuesj-900	116	40	of	of	ADP
cuesj-900	116	41	application	application	NOUN
cuesj-900	116	42	data.[16,17	data.[16,17	NOUN
cuesj-900	116	43	]	]	PUNCT
cuesj-900	116	44	proposed	propose	VERB
cuesj-900	116	45	using	use	VERB
cuesj-900	116	46	the	the	DET
cuesj-900	116	47	following	follow	VERB
cuesj-900	116	48	metric	metric	NOUN
cuesj-900	116	49	:	:	PUNCT
cuesj-900	116	50	d	d	X
cuesj-900	116	51	x	x	PUNCT
cuesj-900	116	52	y	y	NOUN
cuesj-900	116	53	x	x	PUNCT
cuesj-900	116	54	y	y	NOUN
cuesj-900	117	1	i	i	PRON
cuesj-900	117	2	dww	dww	PROPN
cuesj-900	117	3	x	x	PUNCT
cuesj-900	117	4	y	y	PROPN
cuesj-900	117	5	t	t	PROPN
cuesj-900	117	6	t	t	PROPN
cuesj-900	117	7	,	,	PUNCT
cuesj-900	117	8	�	�	PROPN
cuesj-900	117	9	�	�	PROPN
cuesj-900	117	10	{	{	PUNCT
cuesj-900	117	11	�	�	PROPN
cuesj-900	117	12	�	�	PROPN
cuesj-900	117	13	�	�	PROPN
cuesj-900	117	14	(	(	PUNCT
cuesj-900	117	15	�	�	PROPN
cuesj-900	117	16	�	�	PROPN
cuesj-900	117	17	�	�	PROPN
cuesj-900	117	18	(	(	PUNCT
cuesj-900	117	19	�	�	PROPN
cuesj-900	117	20	�	�	PROPN
cuesj-900	117	21	)	)	PUNCT
cuesj-900	117	22	}	}	PUNCT
cuesj-900	117	23	�	�	PROPN
cuesj-900	117	24	�	�	PROPN
cuesj-900	117	25	�	�	PROPN
cuesj-900	117	26	�	�	PROPN
cuesj-900	117	27	�	�	PROPN
cuesj-900	117	28	�	�	PROPN
cuesj-900	117	29	�	�	PROPN
cuesj-900	117	30	�	�	PROPN
cuesj-900	117	31	1	1	NUM
cuesj-900	117	32	2	2	NUM
cuesj-900	117	33	(	(	PUNCT
cuesj-900	117	34	2	2	NUM
cuesj-900	117	35	)	)	PUNCT
cuesj-900	117	36	where	where	SCONJ
cuesj-900	117	37	x	x	PRON
cuesj-900	117	38	and	and	CCONJ
cuesj-900	117	39	y	y	PROPN
cuesj-900	117	40	are	be	AUX
cuesj-900	117	41	in	in	ADP
cuesj-900	117	42	the	the	DET
cuesj-900	117	43	feature	feature	NOUN
cuesj-900	117	44	space	space	NOUN
cuesj-900	117	45	points	point	NOUN
cuesj-900	117	46	,	,	PUNCT
cuesj-900	117	47	i	i	PRON
cuesj-900	117	48	,	,	PUNCT
cuesj-900	117	49	d	d	X
cuesj-900	117	50	is	be	AUX
cuesj-900	117	51	a	a	DET
cuesj-900	117	52	distance	distance	NOUN
cuesj-900	117	53	between	between	ADP
cuesj-900	117	54	parameters	parameter	NOUN
cuesj-900	117	55	,	,	PUNCT
cuesj-900	117	56	w	w	PROPN
cuesj-900	117	57	is	be	AUX
cuesj-900	117	58	a	a	DET
cuesj-900	117	59	specific	specific	ADJ
cuesj-900	117	60	direction	direction	NOUN
cuesj-900	117	61	in	in	ADP
cuesj-900	117	62	the	the	DET
cuesj-900	117	63	measurement	measurement	NOUN
cuesj-900	117	64	space	space	NOUN
cuesj-900	117	65	and	and	CCONJ
cuesj-900	117	66	is	be	AUX
cuesj-900	117	67	the	the	DET
cuesj-900	117	68	identity	identity	NOUN
cuesj-900	117	69	matrix	matrix	NOUN
cuesj-900	117	70	.	.	PUNCT
cuesj-900	118	1	ann	ann	PROPN
cuesj-900	118	2	ann	ann	PROPN
cuesj-900	118	3	are	be	AUX
cuesj-900	118	4	mathematical	mathematical	ADJ
cuesj-900	118	5	representations	representation	NOUN
cuesj-900	118	6	of	of	ADP
cuesj-900	118	7	the	the	DET
cuesj-900	118	8	way	way	NOUN
cuesj-900	118	9	the	the	DET
cuesj-900	118	10	human	human	ADJ
cuesj-900	118	11	brain	brain	NOUN
cuesj-900	118	12	works.[18	works.[18	PROPN
cuesj-900	118	13	]	]	PUNCT
cuesj-900	118	14	with	with	ADP
cuesj-900	118	15	neural	neural	ADJ
cuesj-900	118	16	networks	network	NOUN
cuesj-900	118	17	,	,	PUNCT
cuesj-900	118	18	practically	practically	ADV
cuesj-900	118	19	any	any	DET
cuesj-900	118	20	nonlinear	nonlinear	ADJ
cuesj-900	118	21	relationship	relationship	NOUN
cuesj-900	118	22	between	between	ADP
cuesj-900	118	23	input	input	NOUN
cuesj-900	118	24	and	and	CCONJ
cuesj-900	118	25	objective	objective	ADJ
cuesj-900	118	26	variables	variable	NOUN
cuesj-900	118	27	may	may	AUX
cuesj-900	118	28	be	be	AUX
cuesj-900	118	29	simulated	simulate	VERB
cuesj-900	118	30	thanks	thank	NOUN
cuesj-900	118	31	to	to	ADP
cuesj-900	118	32	their	their	PRON
cuesj-900	118	33	adaptability	adaptability	NOUN
cuesj-900	118	34	.	.	PUNCT
cuesj-900	119	1	despite	despite	SCONJ
cuesj-900	119	2	the	the	DET
cuesj-900	119	3	numerous	numerous	ADJ
cuesj-900	119	4	proposed	propose	VERB
cuesj-900	119	5	architectures	architecture	NOUN
cuesj-900	119	6	,	,	PUNCT
cuesj-900	119	7	the	the	DET
cuesj-900	119	8	multilayer	multilayer	PROPN
cuesj-900	119	9	perceptron	perceptron	PROPN
cuesj-900	119	10	is	be	AUX
cuesj-900	119	11	the	the	DET
cuesj-900	119	12	one	one	NUM
cuesj-900	119	13	that	that	PRON
cuesj-900	119	14	this	this	DET
cuesj-900	119	15	article	article	NOUN
cuesj-900	119	16	concentrates	concentrate	VERB
cuesj-900	119	17	on	on	ADP
cuesj-900	119	18	(	(	PUNCT
cuesj-900	119	19	mlp	mlp	NOUN
cuesj-900	119	20	)	)	PUNCT
cuesj-900	119	21	.	.	PUNCT
cuesj-900	120	1	neurons	neuron	NOUN
cuesj-900	120	2	for	for	ADP
cuesj-900	120	3	each	each	DET
cuesj-900	120	4	input	input	NOUN
cuesj-900	120	5	variable	variable	NOUN
cuesj-900	120	6	are	be	AUX
cuesj-900	120	7	located	locate	VERB
cuesj-900	120	8	in	in	ADP
cuesj-900	120	9	the	the	DET
cuesj-900	120	10	hidden	hide	VERB
cuesj-900	120	11	layer	layer	NOUN
cuesj-900	120	12	of	of	ADP
cuesj-900	120	13	an	an	DET
cuesj-900	120	14	mlp	mlp	NOUN
cuesj-900	120	15	,	,	PUNCT
cuesj-900	120	16	which	which	PRON
cuesj-900	120	17	typically	typically	ADV
cuesj-900	120	18	comprises	comprise	VERB
cuesj-900	120	19	three	three	NUM
cuesj-900	120	20	layers	layer	NOUN
cuesj-900	120	21	(	(	PUNCT
cuesj-900	120	22	in	in	ADP
cuesj-900	120	23	our	our	PRON
cuesj-900	120	24	case	case	NOUN
cuesj-900	120	25	,	,	PUNCT
cuesj-900	120	26	one	one	NUM
cuesj-900	120	27	neuron	neuron	NOUN
cuesj-900	120	28	)	)	PUNCT
cuesj-900	120	29	.	.	PUNCT
cuesj-900	121	1	each	each	DET
cuesj-900	121	2	neuron	neuron	NOUN
cuesj-900	121	3	processes	process	VERB
cuesj-900	121	4	the	the	DET
cuesj-900	121	5	information	information	NOUN
cuesj-900	121	6	it	it	PRON
cuesj-900	121	7	receives	receive	VERB
cuesj-900	121	8	and	and	CCONJ
cuesj-900	121	9	transmits	transmit	VERB
cuesj-900	121	10	the	the	DET
cuesj-900	121	11	results	result	NOUN
cuesj-900	121	12	to	to	ADP
cuesj-900	121	13	the	the	DET
cuesj-900	121	14	neurons	neuron	NOUN
cuesj-900	121	15	in	in	ADP
cuesj-900	121	16	the	the	DET
cuesj-900	121	17	layer	layer	NOUN
cuesj-900	121	18	beneath	beneath	ADP
cuesj-900	121	19	it	it	PRON
cuesj-900	121	20	.	.	PUNCT
cuesj-900	122	1	each	each	PRON
cuesj-900	122	2	of	of	ADP
cuesj-900	122	3	these	these	DET
cuesj-900	122	4	neural	neural	ADJ
cuesj-900	122	5	connections	connection	NOUN
cuesj-900	122	6	receives	receive	VERB
cuesj-900	122	7	a	a	DET
cuesj-900	122	8	weight	weight	NOUN
cuesj-900	122	9	during	during	ADP
cuesj-900	122	10	training	training	NOUN
cuesj-900	122	11	.	.	PUNCT
cuesj-900	123	1	using	use	VERB
cuesj-900	123	2	the	the	DET
cuesj-900	123	3	weighted	weight	VERB
cuesj-900	123	4	inputs	input	NOUN
cuesj-900	123	5	and	and	CCONJ
cuesj-900	123	6	its	its	PRON
cuesj-900	123	7	bias	bias	NOUN
cuesj-900	123	8	term	term	NOUN
cuesj-900	123	9	as	as	ADP
cuesj-900	123	10	an	an	DET
cuesj-900	123	11	example	example	NOUN
cuesj-900	123	12	,	,	PUNCT
cuesj-900	123	13	the	the	DET
cuesj-900	123	14	logistic	logistic	ADJ
cuesj-900	123	15	function	function	NOUN
cuesj-900	123	16	,	,	PUNCT
cuesj-900	123	17	bi	bi	NOUN
cuesj-900	123	18	(	(	PUNCT
cuesj-900	123	19	1	1	NUM
cuesj-900	123	20	)	)	PUNCT
cuesj-900	123	21	results	result	NOUN
cuesj-900	123	22	in	in	ADP
cuesj-900	123	23	the	the	DET
cuesj-900	123	24	computation	computation	NOUN
cuesj-900	123	25	of	of	ADP
cuesj-900	123	26	the	the	DET
cuesj-900	123	27	hidden	hide	VERB
cuesj-900	123	28	neuron	neuron	NOUN
cuesj-900	123	29	’s	’s	PART
cuesj-900	123	30	output	output	NOUN
cuesj-900	123	31	:	:	PUNCT
cuesj-900	124	1	h	h	PROPN
cuesj-900	124	2	f	f	PROPN
cuesj-900	125	1	b	b	PROPN
cuesj-900	125	2	w	w	PROPN
cuesj-900	125	3	xi	xi	X
cuesj-900	125	4	j	j	PROPN
cuesj-900	126	1	i	i	PRON
cuesj-900	126	2	n	n	ADV
cuesj-900	126	3	ij	ij	INTJ
cuesj-900	127	1	i	i	PROPN
cuesj-900	127	2	�	�	PROPN
cuesj-900	127	3	�	�	PROPN
cuesj-900	127	4	�	�	PROPN
cuesj-900	127	5	�	�	PROPN
cuesj-900	127	6	(	(	PUNCT
cuesj-900	127	7	)	)	PUNCT
cuesj-900	127	8	�	�	PROPN
cuesj-900	127	9	�	�	PROPN
cuesj-900	127	10	�	�	PROPN
cuesj-900	127	11	�	�	PROPN
cuesj-900	127	12	�	�	PROPN
cuesj-900	127	13	�	�	PROPN
cuesj-900	127	14	�	�	PROPN
cuesj-900	127	15	�	�	PROPN
cuesj-900	127	16	�	�	PROPN
cuesj-900	127	17	�	�	PROPN
cuesj-900	127	18	�	�	PROPN
cuesj-900	127	19	�	�	PROPN
cuesj-900	127	20	�	�	PROPN
cuesj-900	127	21	1	1	NUM
cuesj-900	127	22	1	1	NUM
cuesj-900	127	23	(	(	PUNCT
cuesj-900	127	24	3	3	NUM
cuesj-900	127	25	)	)	PUNCT
cuesj-900	127	26	wij	wij	PROPN
cuesj-900	127	27	stands	stand	VERB
cuesj-900	127	28	for	for	ADP
cuesj-900	127	29	the	the	DET
cuesj-900	127	30	weight	weight	NOUN
cuesj-900	127	31	connecting	connect	VERB
cuesj-900	127	32	input	input	NOUN
cuesj-900	127	33	where	where	SCONJ
cuesj-900	127	34	w	w	NOUN
cuesj-900	127	35	is	be	AUX
cuesj-900	127	36	a	a	DET
cuesj-900	127	37	weight	weight	NOUN
cuesj-900	127	38	matrix	matrix	NOUN
cuesj-900	127	39	.	.	PUNCT
cuesj-900	128	1	the	the	DET
cuesj-900	128	2	weight	weight	NOUN
cuesj-900	128	3	j	j	PROPN
cuesj-900	128	4	represents	represent	VERB
cuesj-900	128	5	the	the	DET
cuesj-900	128	6	input	input	NOUN
cuesj-900	128	7	connection	connection	NOUN
cuesj-900	128	8	to	to	ADP
cuesj-900	128	9	hidden	hide	VERB
cuesj-900	128	10	neuron	neuron	PROPN
cuesj-900	128	11	i.	i.	PROPN
cuesj-900	128	12	machine	machine	PROPN
cuesj-900	128	13	learning	learning	NOUN
cuesj-900	128	14	models	model	NOUN
cuesj-900	128	15	in	in	ADP
cuesj-900	128	16	clustering	clustering	ADJ
cuesj-900	128	17	method	method	NOUN
cuesj-900	128	18	clustering	cluster	VERB
cuesj-900	128	19	is	be	AUX
cuesj-900	128	20	one	one	NUM
cuesj-900	128	21	of	of	ADP
cuesj-900	128	22	machine	machine	NOUN
cuesj-900	128	23	learning	learning	NOUN
cuesj-900	128	24	’s	’s	PART
cuesj-900	128	25	unsupervised	unsupervised	ADJ
cuesj-900	128	26	learning	learn	VERB
cuesj-900	128	27	strategies.[19	strategies.[19	NOUN
cuesj-900	128	28	]	]	PUNCT
cuesj-900	128	29	when	when	SCONJ
cuesj-900	128	30	no	no	DET
cuesj-900	128	31	class	class	NOUN
cuesj-900	128	32	labels	label	NOUN
cuesj-900	128	33	are	be	AUX
cuesj-900	128	34	available	available	ADJ
cuesj-900	128	35	to	to	PART
cuesj-900	128	36	analyze	analyze	VERB
cuesj-900	128	37	the	the	DET
cuesj-900	128	38	datasets	dataset	NOUN
cuesj-900	128	39	,	,	PUNCT
cuesj-900	128	40	the	the	DET
cuesj-900	128	41	clustering	clustering	ADJ
cuesj-900	128	42	strategy	strategy	NOUN
cuesj-900	128	43	is	be	AUX
cuesj-900	128	44	critical	critical	ADJ
cuesj-900	128	45	for	for	ADP
cuesj-900	128	46	breaking	break	VERB
cuesj-900	128	47	down	down	ADP
cuesj-900	128	48	the	the	DET
cuesj-900	128	49	massive	massive	ADJ
cuesj-900	128	50	volume	volume	NOUN
cuesj-900	128	51	of	of	ADP
cuesj-900	128	52	data	datum	NOUN
cuesj-900	128	53	into	into	ADP
cuesj-900	128	54	smaller	small	ADJ
cuesj-900	128	55	groupings	grouping	NOUN
cuesj-900	128	56	of	of	ADP
cuesj-900	128	57	data	datum	NOUN
cuesj-900	128	58	.	.	PUNCT
cuesj-900	129	1	each	each	DET
cuesj-900	129	2	data	datum	NOUN
cuesj-900	129	3	point	point	NOUN
cuesj-900	129	4	is	be	AUX
cuesj-900	129	5	classified	classify	VERB
cuesj-900	129	6	and	and	CCONJ
cuesj-900	129	7	grouped	group	VERB
cuesj-900	129	8	into	into	ADP
cuesj-900	129	9	a	a	DET
cuesj-900	129	10	distinct	distinct	ADJ
cuesj-900	129	11	cluster	cluster	NOUN
cuesj-900	129	12	using	use	VERB
cuesj-900	129	13	the	the	DET
cuesj-900	129	14	clustering	cluster	VERB
cuesj-900	129	15	approach	approach	NOUN
cuesj-900	129	16	,	,	PUNCT
cuesj-900	129	17	and	and	CCONJ
cuesj-900	129	18	each	each	DET
cuesj-900	129	19	cluster	cluster	NOUN
cuesj-900	129	20	comprises	comprise	VERB
cuesj-900	129	21	a	a	DET
cuesj-900	129	22	collection	collection	NOUN
cuesj-900	129	23	of	of	ADP
cuesj-900	129	24	data	datum	NOUN
cuesj-900	129	25	points	point	NOUN
cuesj-900	129	26	.	.	PUNCT
cuesj-900	130	1	moreover	moreover	ADV
cuesj-900	130	2	,	,	PUNCT
cuesj-900	130	3	data	datum	NOUN
cuesj-900	130	4	points	point	NOUN
cuesj-900	130	5	within	within	ADP
cuesj-900	130	6	the	the	DET
cuesj-900	130	7	same	same	ADJ
cuesj-900	130	8	cluster	cluster	NOUN
cuesj-900	130	9	should	should	AUX
cuesj-900	130	10	have	have	VERB
cuesj-900	130	11	comparable	comparable	ADJ
cuesj-900	130	12	attributes	attribute	NOUN
cuesj-900	130	13	,	,	PUNCT
cuesj-900	130	14	whereas	whereas	SCONJ
cuesj-900	130	15	those	those	PRON
cuesj-900	130	16	inside	inside	ADP
cuesj-900	130	17	a	a	DET
cuesj-900	130	18	separate	separate	ADJ
cuesj-900	130	19	cluster	cluster	NOUN
cuesj-900	130	20	should	should	AUX
cuesj-900	130	21	have	have	VERB
cuesj-900	130	22	significantly	significantly	ADV
cuesj-900	130	23	different	different	ADJ
cuesj-900	130	24	features.[20	features.[20	NOUN
cuesj-900	130	25	]	]	PUNCT
cuesj-900	130	26	partitioning	partition	VERB
cuesj-900	130	27	around	around	ADP
cuesj-900	130	28	medoids	medoid	NOUN
cuesj-900	130	29	,	,	PUNCT
cuesj-900	130	30	also	also	ADV
cuesj-900	130	31	referred	refer	VERB
cuesj-900	130	32	to	to	ADP
cuesj-900	130	33	as	as	ADP
cuesj-900	130	34	k	k	NOUN
cuesj-900	130	35	-	-	PUNCT
cuesj-900	130	36	medoids	medoid	NOUN
cuesj-900	130	37	and	and	CCONJ
cuesj-900	130	38	hierarchical	hierarchical	ADJ
cuesj-900	130	39	k	k	NOUN
cuesj-900	130	40	-	-	PUNCT
cuesj-900	130	41	means	means	NOUN
cuesj-900	130	42	.	.	PUNCT
cuesj-900	131	1	the	the	DET
cuesj-900	131	2	main	main	ADJ
cuesj-900	131	3	operations	operation	NOUN
cuesj-900	131	4	of	of	ADP
cuesj-900	131	5	these	these	DET
cuesj-900	131	6	algorithms	algorithm	NOUN
cuesj-900	131	7	are	be	AUX
cuesj-900	131	8	separated	separate	VERB
cuesj-900	131	9	into	into	ADP
cuesj-900	131	10	the	the	DET
cuesj-900	131	11	following	follow	VERB
cuesj-900	131	12	groups	group	NOUN
cuesj-900	131	13	.	.	PUNCT
cuesj-900	132	1	k	k	X
cuesj-900	132	2	-	-	PUNCT
cuesj-900	132	3	means	mean	VERB
cuesj-900	132	4	clustering	cluster	VERB
cuesj-900	132	5	algorithm	algorithm	NOUN
cuesj-900	132	6	the	the	DET
cuesj-900	132	7	primary	primary	ADJ
cuesj-900	132	8	function	function	NOUN
cuesj-900	132	9	of	of	ADP
cuesj-900	132	10	the	the	DET
cuesj-900	132	11	simple	simple	ADJ
cuesj-900	132	12	and	and	CCONJ
cuesj-900	132	13	frequently	frequently	ADV
cuesj-900	132	14	used	use	VERB
cuesj-900	132	15	clustering	clustering	NOUN
cuesj-900	132	16	method	method	NOUN
cuesj-900	132	17	k	k	NOUN
cuesj-900	132	18	-	-	PUNCT
cuesj-900	132	19	means	means	NOUN
cuesj-900	132	20	is	be	AUX
cuesj-900	132	21	to	to	PART
cuesj-900	132	22	classify	classify	VERB
cuesj-900	132	23	the	the	DET
cuesj-900	132	24	provided	provide	VERB
cuesj-900	132	25	unlabeled	unlabele	VERB
cuesj-900	132	26	dataset	dataset	NOUN
cuesj-900	132	27	.	.	PUNCT
cuesj-900	133	1	the	the	DET
cuesj-900	133	2	main	main	ADJ
cuesj-900	133	3	goal	goal	NOUN
cuesj-900	133	4	of	of	ADP
cuesj-900	133	5	this	this	DET
cuesj-900	133	6	method	method	NOUN
cuesj-900	133	7	is	be	AUX
cuesj-900	133	8	to	to	PART
cuesj-900	133	9	identify	identify	VERB
cuesj-900	133	10	clusters	cluster	NOUN
cuesj-900	133	11	that	that	PRON
cuesj-900	133	12	are	be	AUX
cuesj-900	133	13	comparable	comparable	ADJ
cuesj-900	133	14	to	to	ADP
cuesj-900	133	15	each	each	DET
cuesj-900	133	16	other	other	ADJ
cuesj-900	133	17	,	,	PUNCT
cuesj-900	133	18	as	as	SCONJ
cuesj-900	133	19	indicated	indicate	VERB
cuesj-900	133	20	by	by	ADP
cuesj-900	133	21	the	the	DET
cuesj-900	133	22	variable	variable	PROPN
cuesj-900	133	23	k.	k.	PROPN
cuesj-900	133	24	in	in	ADP
cuesj-900	133	25	this	this	DET
cuesj-900	133	26	approach	approach	NOUN
cuesj-900	133	27	,	,	PUNCT
cuesj-900	133	28	the	the	DET
cuesj-900	133	29	cluster	cluster	NOUN
cuesj-900	133	30	is	be	AUX
cuesj-900	133	31	described	describe	VERB
cuesj-900	133	32	statistically	statistically	ADV
cuesj-900	133	33	using	use	VERB
cuesj-900	133	34	the	the	DET
cuesj-900	133	35	mean	mean	NOUN
cuesj-900	133	36	or	or	CCONJ
cuesj-900	133	37	centroid	centroid	NOUN
cuesj-900	133	38	.	.	PUNCT
cuesj-900	134	1	not	not	PART
cuesj-900	134	2	usually	usually	ADV
cuesj-900	134	3	part	part	NOUN
cuesj-900	134	4	of	of	ADP
cuesj-900	134	5	the	the	DET
cuesj-900	134	6	collection	collection	NOUN
cuesj-900	134	7	,	,	PUNCT
cuesj-900	134	8	a	a	DET
cuesj-900	134	9	centroid	centroid	NOUN
cuesj-900	134	10	is	be	AUX
cuesj-900	134	11	a	a	DET
cuesj-900	134	12	data	data	NOUN
cuesj-900	134	13	point	point	NOUN
cuesj-900	134	14	that	that	PRON
cuesj-900	134	15	symbolizes	symbolize	VERB
cuesj-900	134	16	the	the	DET
cuesj-900	134	17	center	center	NOUN
cuesj-900	134	18	of	of	ADP
cuesj-900	134	19	a	a	DET
cuesj-900	134	20	cluster	cluster	NOUN
cuesj-900	134	21	.	.	PUNCT
cuesj-900	135	1	after	after	ADP
cuesj-900	135	2	that	that	PRON
cuesj-900	135	3	,	,	PUNCT
cuesj-900	135	4	the	the	DET
cuesj-900	135	5	k	k	PROPN
cuesj-900	135	6	clusters	cluster	NOUN
cuesj-900	135	7	are	be	AUX
cuesj-900	135	8	constructed	construct	VERB
cuesj-900	135	9	by	by	ADP
cuesj-900	135	10	grouping	group	VERB
cuesj-900	135	11	the	the	DET
cuesj-900	135	12	n	n	PROPN
cuesj-900	135	13	data	data	NOUN
cuesj-900	135	14	points	point	NOUN
cuesj-900	135	15	into	into	ADP
cuesj-900	135	16	them	they	PRON
cuesj-900	135	17	,	,	PUNCT
cuesj-900	135	18	with	with	ADP
cuesj-900	135	19	each	each	DET
cuesj-900	135	20	data	data	NOUN
cuesj-900	135	21	point	point	NOUN
cuesj-900	135	22	joining	join	VERB
cuesj-900	135	23	the	the	DET
cuesj-900	135	24	cluster	cluster	NOUN
cuesj-900	135	25	with	with	ADP
cuesj-900	135	26	the	the	DET
cuesj-900	135	27	closest	close	ADJ
cuesj-900	135	28	centroid	centroid	NOUN
cuesj-900	135	29	.	.	PUNCT
cuesj-900	136	1	the	the	DET
cuesj-900	136	2	euclidean	euclidean	ADJ
cuesj-900	136	3	distance	distance	NOUN
cuesj-900	136	4	between	between	ADP
cuesj-900	136	5	each	each	DET
cuesj-900	136	6	data	datum	NOUN
cuesj-900	136	7	point	point	NOUN
cuesj-900	136	8	n	n	NOUN
cuesj-900	136	9	and	and	CCONJ
cuesj-900	136	10	the	the	DET
cuesj-900	136	11	cluster	cluster	NOUN
cuesj-900	136	12	centroid	centroid	NOUN
cuesj-900	136	13	is	be	AUX
cuesj-900	136	14	then	then	ADV
cuesj-900	136	15	calculated	calculate	VERB
cuesj-900	136	16	precisely	precisely	ADV
cuesj-900	136	17	.	.	PUNCT
cuesj-900	137	1	a	a	DET
cuesj-900	137	2	cluster	cluster	NOUN
cuesj-900	137	3	’s	’s	PART
cuesj-900	137	4	data	datum	NOUN
cuesj-900	137	5	points	point	NOUN
cuesj-900	137	6	are	be	AUX
cuesj-900	137	7	always	always	ADV
cuesj-900	137	8	assigned	assign	VERB
cuesj-900	137	9	to	to	ADP
cuesj-900	137	10	the	the	DET
cuesj-900	137	11	place	place	NOUN
cuesj-900	137	12	with	with	ADP
cuesj-900	137	13	the	the	DET
cuesj-900	137	14	shortest	short	ADJ
cuesj-900	137	15	euclidean	euclidean	ADJ
cuesj-900	137	16	distance	distance	NOUN
cuesj-900	137	17	from	from	ADP
cuesj-900	137	18	the	the	DET
cuesj-900	137	19	centroid	centroid	NOUN
cuesj-900	137	20	point	point	NOUN
cuesj-900	137	21	.	.	PUNCT
cuesj-900	138	1	when	when	SCONJ
cuesj-900	138	2	there	there	PRON
cuesj-900	138	3	are	be	VERB
cuesj-900	138	4	no	no	DET
cuesj-900	138	5	available	available	ADJ
cuesj-900	138	6	data	datum	NOUN
cuesj-900	138	7	points	point	NOUN
cuesj-900	138	8	to	to	PART
cuesj-900	138	9	assign	assign	VERB
cuesj-900	138	10	,	,	PUNCT
cuesj-900	138	11	an	an	DET
cuesj-900	138	12	early	early	ADJ
cuesj-900	138	13	grouping	grouping	NOUN
cuesj-900	138	14	is	be	AUX
cuesj-900	138	15	considered	consider	VERB
cuesj-900	138	16	.	.	PUNCT
cuesj-900	139	1	the	the	DET
cuesj-900	139	2	technique	technique	NOUN
cuesj-900	139	3	is	be	AUX
cuesj-900	139	4	then	then	ADV
cuesj-900	139	5	repeated	repeat	VERB
cuesj-900	139	6	until	until	SCONJ
cuesj-900	139	7	the	the	DET
cuesj-900	139	8	“	"	PUNCT
cuesj-900	139	9	c	c	NOUN
cuesj-900	139	10	”	"	PUNCT
cuesj-900	139	11	centroids	centroid	NOUN
cuesj-900	139	12	stop	stop	VERB
cuesj-900	139	13	migrating	migrating	NOUN
cuesj-900	139	14	and	and	CCONJ
cuesj-900	139	15	the	the	DET
cuesj-900	139	16	new	new	ADJ
cuesj-900	139	17	“	"	PUNCT
cuesj-900	139	18	c	c	NOUN
cuesj-900	139	19	”	"	PUNCT
cuesj-900	139	20	centroids	centroid	NOUN
cuesj-900	139	21	are	be	AUX
cuesj-900	139	22	identified.[21	identified.[21	NOUN
cuesj-900	139	23	]	]	PUNCT
cuesj-900	140	1	hierarchical	hierarchical	ADJ
cuesj-900	140	2	clustering	cluster	VERB
cuesj-900	140	3	hierarchical	hierarchical	ADJ
cuesj-900	140	4	,	,	PUNCT
cuesj-900	140	5	also	also	ADV
cuesj-900	140	6	known	know	VERB
cuesj-900	140	7	as	as	ADP
cuesj-900	140	8	hierarchical	hierarchical	ADJ
cuesj-900	140	9	cluster	cluster	NOUN
cuesj-900	140	10	analysis	analysis	NOUN
cuesj-900	140	11	,	,	PUNCT
cuesj-900	140	12	is	be	AUX
cuesj-900	140	13	a	a	DET
cuesj-900	140	14	particular	particular	ADJ
cuesj-900	140	15	kind	kind	NOUN
cuesj-900	140	16	of	of	ADV
cuesj-900	140	17	unsupervised	unsupervised	ADJ
cuesj-900	140	18	.	.	PUNCT
cuesj-900	141	1	with	with	ADP
cuesj-900	141	2	the	the	DET
cuesj-900	141	3	use	use	NOUN
cuesj-900	141	4	of	of	ADP
cuesj-900	141	5	a	a	DET
cuesj-900	141	6	tree	tree	NOUN
cuesj-900	141	7	-	-	PUNCT
cuesj-900	141	8	based	base	VERB
cuesj-900	141	9	framework	framework	NOUN
cuesj-900	141	10	,	,	PUNCT
cuesj-900	141	11	hierarchical	hierarchical	ADJ
cuesj-900	141	12	cluster	cluster	NOUN
cuesj-900	141	13	analysis	analysis	NOUN
cuesj-900	141	14	aims	aim	VERB
cuesj-900	141	15	to	to	PART
cuesj-900	141	16	merge	merge	VERB
cuesj-900	141	17	multiple	multiple	ADJ
cuesj-900	141	18	clusters	cluster	NOUN
cuesj-900	141	19	made	make	VERB
cuesj-900	141	20	up	up	ADP
cuesj-900	141	21	of	of	ADP
cuesj-900	141	22	comparable	comparable	ADJ
cuesj-900	141	23	unlabeled	unlabeled	ADJ
cuesj-900	141	24	data	datum	NOUN
cuesj-900	141	25	points	point	NOUN
cuesj-900	141	26	.	.	PUNCT
cuesj-900	142	1	the	the	DET
cuesj-900	142	2	clusters	cluster	NOUN
cuesj-900	142	3	of	of	ADP
cuesj-900	142	4	data	datum	NOUN
cuesj-900	142	5	points	point	NOUN
cuesj-900	142	6	formed	form	VERB
cuesj-900	142	7	by	by	ADP
cuesj-900	142	8	the	the	DET
cuesj-900	142	9	last	last	ADJ
cuesj-900	142	10	branch	branch	NOUN
cuesj-900	142	11	of	of	ADP
cuesj-900	142	12	the	the	DET
cuesj-900	142	13	tree	tree	NOUN
cuesj-900	142	14	are	be	AUX
cuesj-900	142	15	not	not	PART
cuesj-900	142	16	alike	alike	ADV
cuesj-900	142	17	.	.	PUNCT
cuesj-900	143	1	in	in	ADP
cuesj-900	143	2	addition	addition	NOUN
cuesj-900	143	3	,	,	PUNCT
cuesj-900	143	4	the	the	DET
cuesj-900	143	5	majority	majority	NOUN
cuesj-900	143	6	of	of	ADP
cuesj-900	143	7	the	the	DET
cuesj-900	143	8	data	data	NOUN
cuesj-900	143	9	points	point	NOUN
cuesj-900	143	10	inside	inside	ADP
cuesj-900	143	11	a	a	DET
cuesj-900	143	12	particular	particular	ADJ
cuesj-900	143	13	cluster	cluster	NOUN
cuesj-900	143	14	coincide	coincide	NOUN
cuesj-900	143	15	with	with	ADP
cuesj-900	143	16	those	those	PRON
cuesj-900	143	17	within	within	ADP
cuesj-900	143	18	other	other	ADJ
cuesj-900	143	19	clusters	cluster	NOUN
cuesj-900	143	20	in	in	ADP
cuesj-900	143	21	the	the	DET
cuesj-900	143	22	data	datum	NOUN
cuesj-900	143	23	set	set	VERB
cuesj-900	143	24	.	.	PUNCT
cuesj-900	144	1	this	this	DET
cuesj-900	144	2	approach	approach	NOUN
cuesj-900	144	3	uses	use	VERB
cuesj-900	144	4	a	a	DET
cuesj-900	144	5	diagram	diagram	NOUN
cuesj-900	144	6	,	,	PUNCT
cuesj-900	144	7	which	which	PRON
cuesj-900	144	8	is	be	AUX
cuesj-900	144	9	a	a	DET
cuesj-900	144	10	diagram	diagram	NOUN
cuesj-900	144	11	that	that	PRON
cuesj-900	144	12	resembles	resemble	VERB
cuesj-900	144	13	a	a	DET
cuesj-900	144	14	tree	tree	NOUN
cuesj-900	144	15	and	and	CCONJ
cuesj-900	144	16	is	be	AUX
cuesj-900	144	17	based	base	VERB
cuesj-900	144	18	on	on	ADP
cuesj-900	144	19	the	the	DET
cuesj-900	144	20	hierarchy	hierarchy	NOUN
cuesj-900	144	21	.	.	PUNCT
cuesj-900	145	1	agglomerative	agglomerative	ADJ
cuesj-900	145	2	hierarchical	hierarchical	ADJ
cuesj-900	145	3	clustering	clustering	NOUN
cuesj-900	145	4	,	,	PUNCT
cuesj-900	145	5	also	also	ADV
cuesj-900	145	6	known	know	VERB
cuesj-900	145	7	as	as	ADP
cuesj-900	145	8	agnes	agnes	PROPN
cuesj-900	145	9	(	(	PUNCT
cuesj-900	145	10	agglomerative	agglomerative	ADJ
cuesj-900	145	11	nesting	nesting	NOUN
cuesj-900	145	12	)	)	PUNCT
cuesj-900	145	13	,	,	PUNCT
cuesj-900	145	14	and	and	CCONJ
cuesj-900	145	15	divisive	divisive	ADJ
cuesj-900	145	16	hierarchical	hierarchical	ADJ
cuesj-900	145	17	clustering	clustering	NOUN
cuesj-900	145	18	,	,	PUNCT
cuesj-900	145	19	also	also	ADV
cuesj-900	145	20	known	know	VERB
cuesj-900	145	21	as	as	ADP
cuesj-900	145	22	diana	diana	PROPN
cuesj-900	145	23	,	,	PUNCT
cuesj-900	145	24	are	be	AUX
cuesj-900	145	25	two	two	NUM
cuesj-900	145	26	categories	category	NOUN
cuesj-900	145	27	of	of	ADP
cuesj-900	145	28	hierarchical	hierarchical	ADJ
cuesj-900	145	29	clustering	clustering	NOUN
cuesj-900	145	30	techniques	technique	NOUN
cuesj-900	145	31	(	(	PUNCT
cuesj-900	145	32	divisive	divisive	ADJ
cuesj-900	145	33	analysis	analysis	NOUN
cuesj-900	145	34	)	)	PUNCT
cuesj-900	145	35	.	.	PUNCT
cuesj-900	146	1	this	this	DET
cuesj-900	146	2	algorithm	algorithm	NOUN
cuesj-900	146	3	is	be	AUX
cuesj-900	146	4	an	an	DET
cuesj-900	146	5	identical	identical	ADJ
cuesj-900	146	6	duplicate	duplicate	NOUN
cuesj-900	146	7	of	of	ADP
cuesj-900	146	8	itself	itself	PRON
cuesj-900	146	9	.	.	PUNCT
cuesj-900	147	1	table	table	NOUN
cuesj-900	147	2	1	1	NUM
cuesj-900	147	3	compiles	compile	VERB
cuesj-900	147	4	several	several	ADJ
cuesj-900	147	5	strategies	strategy	NOUN
cuesj-900	147	6	based	base	VERB
cuesj-900	147	7	on	on	ADP
cuesj-900	147	8	various	various	ADJ
cuesj-900	147	9	criteria.[21	criteria.[21	NOUN
cuesj-900	147	10	]	]	PUNCT
cuesj-900	147	11	machine	machine	NOUN
cuesj-900	147	12	learning	learn	VERB
cuesj-900	147	13	application	application	NOUN
cuesj-900	147	14	software	software	NOUN
cuesj-900	147	15	using	use	VERB
cuesj-900	147	16	the	the	DET
cuesj-900	147	17	software	software	NOUN
cuesj-900	147	18	is	be	AUX
cuesj-900	147	19	a	a	DET
cuesj-900	147	20	collection	collection	NOUN
cuesj-900	147	21	of	of	ADP
cuesj-900	147	22	one	one	NUM
cuesj-900	147	23	or	or	CCONJ
cuesj-900	147	24	more	more	ADJ
cuesj-900	147	25	apps	app	NOUN
cuesj-900	147	26	created	create	VERB
cuesj-900	147	27	for	for	ADP
cuesj-900	147	28	end	end	NOUN
cuesj-900	147	29	users	user	NOUN
cuesj-900	147	30	.	.	PUNCT
cuesj-900	148	1	applications	application	NOUN
cuesj-900	148	2	can	can	AUX
cuesj-900	148	3	be	be	AUX
cuesj-900	148	4	found	find	VERB
cuesj-900	148	5	in	in	ADP
cuesj-900	148	6	web	web	NOUN
cuesj-900	148	7	browsers	browser	NOUN
cuesj-900	148	8	,	,	PUNCT
cuesj-900	148	9	email	email	NOUN
cuesj-900	148	10	clients	client	NOUN
cuesj-900	148	11	,	,	PUNCT
cuesj-900	148	12	word	word	NOUN
cuesj-900	148	13	processors	processor	NOUN
cuesj-900	148	14	,	,	PUNCT
cuesj-900	148	15	spreadsheets	spreadsheet	NOUN
cuesj-900	148	16	,	,	PUNCT
cuesj-900	148	17	accounting	accounting	NOUN
cuesj-900	148	18	software	software	NOUN
cuesj-900	148	19	,	,	PUNCT
cuesj-900	148	20	music	music	NOUN
cuesj-900	148	21	players	player	NOUN
cuesj-900	148	22	,	,	PUNCT
cuesj-900	148	23	file	file	NOUN
cuesj-900	148	24	viewers	viewer	NOUN
cuesj-900	148	25	,	,	PUNCT
cuesj-900	148	26	simulators	simulator	NOUN
cuesj-900	148	27	,	,	PUNCT
cuesj-900	148	28	console	console	NOUN
cuesj-900	148	29	games	game	NOUN
cuesj-900	148	30	,	,	PUNCT
cuesj-900	148	31	and	and	CCONJ
cuesj-900	148	32	picture	picture	NOUN
cuesj-900	148	33	editors	editor	NOUN
cuesj-900	148	34	.	.	PUNCT
cuesj-900	149	1	machine	machine	NOUN
cuesj-900	149	2	learning	learn	VERB
cuesj-900	149	3	software	software	NOUN
cuesj-900	149	4	designed	design	VERB
cuesj-900	149	5	by	by	ADP
cuesj-900	149	6	professionals	professional	NOUN
cuesj-900	149	7	is	be	AUX
cuesj-900	149	8	generally	generally	ADV
cuesj-900	149	9	available	available	ADJ
cuesj-900	149	10	.	.	PUNCT
cuesj-900	150	1	the	the	DET
cuesj-900	150	2	following	follow	VERB
cuesj-900	150	3	are	be	AUX
cuesj-900	150	4	a	a	DET
cuesj-900	150	5	few	few	ADJ
cuesj-900	150	6	of	of	ADP
cuesj-900	150	7	the	the	DET
cuesj-900	150	8	few	few	ADJ
cuesj-900	150	9	notable	notable	ADJ
cuesj-900	150	10	pieces	piece	NOUN
cuesj-900	150	11	of	of	ADP
cuesj-900	150	12	software	software	NOUN
cuesj-900	150	13	:	:	PUNCT
cuesj-900	150	14	to	to	PART
cuesj-900	150	15	conduct	conduct	VERB
cuesj-900	150	16	data	datum	NOUN
cuesj-900	150	17	mining	mining	NOUN
cuesj-900	150	18	tasks	task	NOUN
cuesj-900	150	19	,	,	PUNCT
cuesj-900	150	20	the	the	DET
cuesj-900	150	21	program	program	NOUN
cuesj-900	150	22	employs	employ	VERB
cuesj-900	150	23	a	a	DET
cuesj-900	150	24	variety	variety	NOUN
cuesj-900	150	25	of	of	ADP
cuesj-900	150	26	machine	machine	NOUN
cuesj-900	150	27	learning	learn	VERB
cuesj-900	150	28	algorithms	algorithm	NOUN
cuesj-900	150	29	.	.	PUNCT
cuesj-900	151	1	manually	manually	ADV
cuesj-900	151	2	apply	apply	VERB
cuesj-900	151	3	the	the	DET
cuesj-900	151	4	algorithms	algorithm	NOUN
cuesj-900	151	5	to	to	ADP
cuesj-900	151	6	a	a	DET
cuesj-900	151	7	dataset	dataset	NOUN
cuesj-900	151	8	or	or	CCONJ
cuesj-900	151	9	simply	simply	ADV
cuesj-900	151	10	invoke	invoke	VERB
cuesj-900	151	11	them	they	PRON
cuesj-900	151	12	in	in	ADP
cuesj-900	151	13	your	your	PRON
cuesj-900	151	14	figure	figure	NOUN
cuesj-900	151	15	2	2	NUM
cuesj-900	151	16	:	:	PUNCT
cuesj-900	151	17	diagram	diagram	NOUN
cuesj-900	151	18	showing	show	VERB
cuesj-900	151	19	decision	decision	NOUN
cuesj-900	151	20	tree	tree	NOUN
cuesj-900	151	21	mulla	mulla	NOUN
cuesj-900	151	22	and	and	CCONJ
cuesj-900	151	23	demir	demir	PROPN
cuesj-900	151	24	:	:	PUNCT
cuesj-900	151	25	the	the	DET
cuesj-900	151	26	use	use	NOUN
cuesj-900	151	27	of	of	ADP
cuesj-900	151	28	clustering	clustering	NOUN
cuesj-900	151	29	and	and	CCONJ
cuesj-900	151	30	classification	classification	NOUN
cuesj-900	151	31	methods	method	NOUN
cuesj-900	151	32	in	in	ADP
cuesj-900	151	33	machine	machine	NOUN
cuesj-900	151	34	learning	learn	VERB
cuesj-900	151	35	56	56	NUM
cuesj-900	151	36	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-900	151	37	cuesj	cuesj	NOUN
cuesj-900	151	38	2023	2023	NUM
cuesj-900	151	39	,	,	PUNCT
cuesj-900	151	40	7	7	NUM
cuesj-900	151	41	(	(	PUNCT
cuesj-900	151	42	1	1	NUM
cuesj-900	151	43	):	):	PUNCT
cuesj-900	151	44	52	52	NUM
cuesj-900	151	45	-	-	SYM
cuesj-900	151	46	59	59	NUM
cuesj-900	151	47	java	java	PROPN
cuesj-900	151	48	code	code	PROPN
cuesj-900	151	49	.	.	PUNCT
cuesj-900	152	1	data	datum	NOUN
cuesj-900	152	2	pre	pre	ADJ
cuesj-900	152	3	-	-	ADJ
cuesj-900	152	4	processing	processing	ADJ
cuesj-900	152	5	,	,	PUNCT
cuesj-900	152	6	classification	classification	NOUN
cuesj-900	152	7	,	,	PUNCT
cuesj-900	152	8	regression	regression	NOUN
cuesj-900	152	9	,	,	PUNCT
cuesj-900	152	10	clustering	clustering	NOUN
cuesj-900	152	11	,	,	PUNCT
cuesj-900	152	12	association	association	NOUN
cuesj-900	152	13	rules	rule	NOUN
cuesj-900	152	14	,	,	PUNCT
cuesj-900	152	15	and	and	CCONJ
cuesj-900	152	16	visualization	visualization	NOUN
cuesj-900	152	17	are	be	AUX
cuesj-900	152	18	among	among	ADP
cuesj-900	152	19	weka	weka	PROPN
cuesj-900	152	20	’s	’s	PART
cuesj-900	152	21	capabilities	capability	NOUN
cuesj-900	152	22	.	.	PUNCT
cuesj-900	153	1	at	at	ADP
cuesj-900	153	2	the	the	DET
cuesj-900	153	3	university	university	NOUN
cuesj-900	153	4	of	of	ADP
cuesj-900	153	5	waikato	waikato	NOUN
cuesj-900	153	6	in	in	ADP
cuesj-900	153	7	new	new	PROPN
cuesj-900	153	8	zealand	zealand	PROPN
cuesj-900	153	9	,	,	PUNCT
cuesj-900	153	10	weka	weka	PROPN
cuesj-900	153	11	was	be	AUX
cuesj-900	153	12	upgraded	upgrade	VERB
cuesj-900	153	13	.	.	PUNCT
cuesj-900	154	1	this	this	DET
cuesj-900	154	2	tool	tool	NOUN
cuesj-900	154	3	is	be	AUX
cuesj-900	154	4	an	an	DET
cuesj-900	154	5	addition	addition	NOUN
cuesj-900	154	6	to	to	ADP
cuesj-900	154	7	the	the	DET
cuesj-900	154	8	gnu	gnu	PROPN
cuesj-900	154	9	general	general	ADJ
cuesj-900	154	10	public	public	ADJ
cuesj-900	154	11	license	license	NOUN
cuesj-900	154	12	-	-	PUNCT
cuesj-900	154	13	licensed	license	VERB
cuesj-900	154	14	free	free	ADJ
cuesj-900	154	15	program	program	NOUN
cuesj-900	154	16	“	"	PUNCT
cuesj-900	154	17	data	data	NOUN
cuesj-900	154	18	mining	mining	NOUN
cuesj-900	154	19	:	:	PUNCT
cuesj-900	154	20	practical	practical	ADJ
cuesj-900	154	21	machine	machine	NOUN
cuesj-900	154	22	learning	learning	NOUN
cuesj-900	154	23	tools	tool	NOUN
cuesj-900	154	24	and	and	CCONJ
cuesj-900	154	25	methods”.[22	methods”.[22	NOUN
cuesj-900	154	26	]	]	PUNCT
cuesj-900	154	27	the	the	DET
cuesj-900	154	28	measure	measure	NOUN
cuesj-900	154	29	that	that	PRON
cuesj-900	154	30	used	use	VERB
cuesj-900	154	31	in	in	ADP
cuesj-900	154	32	weka	weka	PROPN
cuesj-900	154	33	confusion	confusion	NOUN
cuesj-900	154	34	matrix	matrix	NOUN
cuesj-900	154	35	a	a	DET
cuesj-900	154	36	representative	representative	ADJ
cuesj-900	154	37	representation	representation	NOUN
cuesj-900	154	38	of	of	ADP
cuesj-900	154	39	the	the	DET
cuesj-900	154	40	above	above	ADV
cuesj-900	154	41	-	-	PUNCT
cuesj-900	154	42	mentioned	mention	VERB
cuesj-900	154	43	parameters	parameter	NOUN
cuesj-900	154	44	in	in	ADP
cuesj-900	154	45	matrix	matrix	NOUN
cuesj-900	154	46	form	form	NOUN
cuesj-900	154	47	is	be	AUX
cuesj-900	154	48	given	give	VERB
cuesj-900	154	49	in	in	ADP
cuesj-900	154	50	table	table	NOUN
cuesj-900	154	51	2	2	NUM
cuesj-900	154	52	,	,	PUNCT
cuesj-900	154	53	considering	consider	VERB
cuesj-900	154	54	that	that	SCONJ
cuesj-900	154	55	advanced	advanced	ADJ
cuesj-900	154	56	visualizations	visualization	NOUN
cuesj-900	154	57	would	would	AUX
cuesj-900	154	58	be	be	AUX
cuesj-900	154	59	helpful	helpful	ADJ
cuesj-900	154	60	accuracy	accuracy	NOUN
cuesj-900	154	61	to	to	PART
cuesj-900	154	62	evaluate	evaluate	VERB
cuesj-900	154	63	each	each	DET
cuesj-900	154	64	created	create	VERB
cuesj-900	154	65	subset	subset	NOUN
cuesj-900	154	66	in	in	ADP
cuesj-900	154	67	this	this	DET
cuesj-900	154	68	measurement	measurement	NOUN
cuesj-900	154	69	,	,	PUNCT
cuesj-900	154	70	classification	classification	NOUN
cuesj-900	154	71	data	datum	NOUN
cuesj-900	154	72	mining	mining	NOUN
cuesj-900	154	73	methods	method	NOUN
cuesj-900	154	74	have	have	AUX
cuesj-900	154	75	been	be	AUX
cuesj-900	154	76	offered	offer	VERB
cuesj-900	154	77	as	as	ADP
cuesj-900	154	78	a	a	DET
cuesj-900	154	79	fitness	fitness	NOUN
cuesj-900	154	80	function	function	NOUN
cuesj-900	154	81	.	.	PUNCT
cuesj-900	155	1	the	the	DET
cuesj-900	155	2	formulation	formulation	NOUN
cuesj-900	155	3	of	of	ADP
cuesj-900	155	4	measuring	measure	VERB
cuesj-900	155	5	the	the	DET
cuesj-900	155	6	accuracy	accuracy	NOUN
cuesj-900	155	7	of	of	ADP
cuesj-900	155	8	each	each	DET
cuesj-900	155	9	subset	subset	NOUN
cuesj-900	155	10	is	be	AUX
cuesj-900	155	11	described	describe	VERB
cuesj-900	155	12	as	as	SCONJ
cuesj-900	155	13	follows	follow	VERB
cuesj-900	155	14	:	:	PUNCT
cuesj-900	155	15	accuracy=	accuracy=	NUM
cuesj-900	156	1	tp+tn	tp+tn	PRON
cuesj-900	156	2	tp+tn+fp+fn	tp+tn+fp+fn	PROPN
cuesj-900	156	3	*	*	NUM
cuesj-900	156	4	100	100	NUM
cuesj-900	156	5	false	false	ADJ
cuesj-900	156	6	positive	positive	ADJ
cuesj-900	156	7	(	(	PUNCT
cuesj-900	156	8	fp	fp	NOUN
cuesj-900	156	9	)	)	PUNCT
cuesj-900	156	10	reflects	reflect	VERB
cuesj-900	156	11	the	the	DET
cuesj-900	156	12	improper	improper	ADJ
cuesj-900	156	13	placement	placement	NOUN
cuesj-900	156	14	of	of	ADP
cuesj-900	156	15	the	the	DET
cuesj-900	156	16	negative	negative	ADJ
cuesj-900	156	17	example	example	NOUN
cuesj-900	156	18	in	in	ADP
cuesj-900	156	19	the	the	DET
cuesj-900	156	20	positive	positive	ADJ
cuesj-900	156	21	class	class	NOUN
cuesj-900	156	22	,	,	PUNCT
cuesj-900	156	23	whereas	whereas	SCONJ
cuesj-900	156	24	true	true	ADJ
cuesj-900	156	25	negative	negative	ADJ
cuesj-900	156	26	(	(	PUNCT
cuesj-900	156	27	tn	tn	NOUN
cuesj-900	156	28	)	)	PUNCT
cuesj-900	156	29	represents	represent	VERB
cuesj-900	156	30	the	the	DET
cuesj-900	156	31	proper	proper	ADJ
cuesj-900	156	32	placement	placement	NOUN
cuesj-900	156	33	of	of	ADP
cuesj-900	156	34	the	the	DET
cuesj-900	156	35	negative	negative	ADJ
cuesj-900	156	36	example	example	NOUN
cuesj-900	156	37	.	.	PUNCT
cuesj-900	157	1	true	true	ADJ
cuesj-900	157	2	positive	positive	ADJ
cuesj-900	157	3	(	(	PUNCT
cuesj-900	157	4	tp	tp	NOUN
cuesj-900	157	5	)	)	PUNCT
cuesj-900	157	6	represents	represent	VERB
cuesj-900	157	7	the	the	DET
cuesj-900	157	8	proper	proper	ADJ
cuesj-900	157	9	classifications	classification	NOUN
cuesj-900	157	10	of	of	ADP
cuesj-900	157	11	the	the	DET
cuesj-900	157	12	positive	positive	ADJ
cuesj-900	157	13	example	example	NOUN
cuesj-900	157	14	.	.	PUNCT
cuesj-900	158	1	false	false	ADJ
cuesj-900	158	2	negative	negative	ADJ
cuesj-900	158	3	(	(	PUNCT
cuesj-900	158	4	fn	fn	NOUN
cuesj-900	158	5	)	)	PUNCT
cuesj-900	158	6	is	be	AUX
cuesj-900	158	7	the	the	DET
cuesj-900	158	8	term	term	NOUN
cuesj-900	158	9	used	use	VERB
cuesj-900	158	10	to	to	PART
cuesj-900	158	11	describe	describe	VERB
cuesj-900	158	12	a	a	DET
cuesj-900	158	13	positive	positive	ADJ
cuesj-900	158	14	sample	sample	NOUN
cuesj-900	158	15	that	that	PRON
cuesj-900	158	16	was	be	AUX
cuesj-900	158	17	mistakenly	mistakenly	ADV
cuesj-900	158	18	placed	place	VERB
cuesj-900	158	19	in	in	ADP
cuesj-900	158	20	the	the	DET
cuesj-900	158	21	negative	negative	ADJ
cuesj-900	158	22	category	category	NOUN
cuesj-900	158	23	.	.	PUNCT
cuesj-900	159	1	precision	precision	VERB
cuesj-900	159	2	the	the	DET
cuesj-900	159	3	precision	precision	NOUN
cuesj-900	159	4	is	be	AUX
cuesj-900	159	5	calculated	calculate	VERB
cuesj-900	159	6	with	with	ADP
cuesj-900	159	7	the	the	DET
cuesj-900	159	8	following	follow	VERB
cuesj-900	159	9	equation	equation	NOUN
cuesj-900	159	10	and	and	CCONJ
cuesj-900	159	11	the	the	DET
cuesj-900	159	12	denominator	denominator	NOUN
cuesj-900	159	13	consists	consist	VERB
cuesj-900	159	14	of	of	ADP
cuesj-900	159	15	the	the	DET
cuesj-900	159	16	total	total	NOUN
cuesj-900	159	17	predicted	predict	VERB
cuesj-900	159	18	positive	positive	ADJ
cuesj-900	159	19	.	.	PUNCT
cuesj-900	160	1	precision=	precision=	NUM
cuesj-900	160	2	tp	tp	NOUN
cuesj-900	160	3	tp+fp	tp+fp	INTJ
cuesj-900	161	1	it	it	PRON
cuesj-900	161	2	is	be	AUX
cuesj-900	161	3	immediately	immediately	ADV
cuesj-900	161	4	apparent	apparent	ADJ
cuesj-900	161	5	that	that	SCONJ
cuesj-900	161	6	precision	precision	NOUN
cuesj-900	161	7	relates	relate	VERB
cuesj-900	161	8	to	to	ADP
cuesj-900	161	9	how	how	SCONJ
cuesj-900	161	10	precise	precise	ADJ
cuesj-900	161	11	/	/	SYM
cuesj-900	161	12	accurate	accurate	ADJ
cuesj-900	161	13	your	your	PRON
cuesj-900	161	14	model	model	NOUN
cuesj-900	161	15	is	be	AUX
cuesj-900	161	16	in	in	ADP
cuesj-900	161	17	terms	term	NOUN
cuesj-900	161	18	of	of	ADP
cuesj-900	161	19	the	the	DET
cuesj-900	161	20	proportion	proportion	NOUN
cuesj-900	161	21	of	of	ADP
cuesj-900	161	22	successful	successful	ADJ
cuesj-900	161	23	predictions	prediction	NOUN
cuesj-900	161	24	.	.	PUNCT
cuesj-900	162	1	precision	precision	NOUN
cuesj-900	162	2	is	be	AUX
cuesj-900	162	3	a	a	DET
cuesj-900	162	4	helpful	helpful	ADJ
cuesj-900	162	5	statistic	statistic	NOUN
cuesj-900	162	6	to	to	PART
cuesj-900	162	7	judge	judge	VERB
cuesj-900	162	8	whether	whether	SCONJ
cuesj-900	162	9	there	there	PRON
cuesj-900	162	10	are	be	VERB
cuesj-900	162	11	high	high	ADJ
cuesj-900	162	12	costs	cost	NOUN
cuesj-900	162	13	associated	associate	VERB
cuesj-900	162	14	with	with	ADP
cuesj-900	162	15	false	false	ADJ
cuesj-900	162	16	positives	positive	NOUN
cuesj-900	162	17	.	.	PUNCT
cuesj-900	163	1	as	as	ADP
cuesj-900	163	2	an	an	DET
cuesj-900	163	3	example	example	NOUN
cuesj-900	163	4	,	,	PUNCT
cuesj-900	163	5	email	email	NOUN
cuesj-900	163	6	spam	spam	NOUN
cuesj-900	163	7	detection	detection	NOUN
cuesj-900	163	8	.	.	PUNCT
cuesj-900	164	1	when	when	SCONJ
cuesj-900	164	2	an	an	DET
cuesj-900	164	3	email	email	NOUN
cuesj-900	164	4	that	that	PRON
cuesj-900	164	5	is	be	AUX
cuesj-900	164	6	not	not	PART
cuesj-900	164	7	spam	spam	NOUN
cuesj-900	164	8	(	(	PUNCT
cuesj-900	164	9	actual	actual	ADJ
cuesj-900	164	10	negative	negative	NOUN
cuesj-900	164	11	)	)	PUNCT
cuesj-900	164	12	is	be	AUX
cuesj-900	164	13	wrongly	wrongly	ADV
cuesj-900	164	14	classified	classify	VERB
cuesj-900	164	15	as	as	ADP
cuesj-900	164	16	spam	spam	NOUN
cuesj-900	164	17	,	,	PUNCT
cuesj-900	164	18	it	it	PRON
cuesj-900	164	19	is	be	AUX
cuesj-900	164	20	known	know	VERB
cuesj-900	164	21	as	as	ADP
cuesj-900	164	22	a	a	DET
cuesj-900	164	23	false	false	ADJ
cuesj-900	164	24	positive	positive	ADJ
cuesj-900	164	25	in	in	ADP
cuesj-900	164	26	email	email	NOUN
cuesj-900	164	27	spam	spam	NOUN
cuesj-900	164	28	detection	detection	NOUN
cuesj-900	164	29	(	(	PUNCT
cuesj-900	164	30	predicted	predict	VERB
cuesj-900	164	31	spam	spam	NOUN
cuesj-900	164	32	)	)	PUNCT
cuesj-900	164	33	.	.	PUNCT
cuesj-900	165	1	the	the	DET
cuesj-900	165	2	email	email	NOUN
cuesj-900	165	3	user	user	NOUN
cuesj-900	165	4	may	may	AUX
cuesj-900	165	5	overlook	overlook	VERB
cuesj-900	165	6	important	important	ADJ
cuesj-900	165	7	emails	email	NOUN
cuesj-900	165	8	if	if	SCONJ
cuesj-900	165	9	the	the	DET
cuesj-900	165	10	spam	spam	NOUN
cuesj-900	165	11	detection	detection	NOUN
cuesj-900	165	12	model	model	NOUN
cuesj-900	165	13	’s	’s	PART
cuesj-900	165	14	precision	precision	NOUN
cuesj-900	165	15	is	be	AUX
cuesj-900	165	16	poor	poor	ADJ
cuesj-900	165	17	.	.	PUNCT
cuesj-900	166	1	f	f	X
cuesj-900	166	2	-	-	PUNCT
cuesj-900	166	3	measure	measure	NOUN
cuesj-900	166	4	in	in	ADP
cuesj-900	166	5	statistical	statistical	ADJ
cuesj-900	166	6	analysis	analysis	NOUN
cuesj-900	166	7	of	of	ADP
cuesj-900	166	8	binary	binary	ADJ
cuesj-900	166	9	classification	classification	NOUN
cuesj-900	166	10	,	,	PUNCT
cuesj-900	166	11	the	the	DET
cuesj-900	166	12	f	f	NOUN
cuesj-900	166	13	-	-	PUNCT
cuesj-900	166	14	score	score	NOUN
cuesj-900	166	15	or	or	CCONJ
cuesj-900	166	16	f	f	X
cuesj-900	166	17	-	-	PUNCT
cuesj-900	166	18	measure	measure	NOUN
cuesj-900	166	19	is	be	AUX
cuesj-900	166	20	a	a	DET
cuesj-900	166	21	measure	measure	NOUN
cuesj-900	166	22	of	of	ADP
cuesj-900	166	23	a	a	DET
cuesj-900	166	24	test	test	NOUN
cuesj-900	166	25	’s	’s	PART
cuesj-900	166	26	accuracy	accuracy	NOUN
cuesj-900	166	27	.	.	PUNCT
cuesj-900	167	1	even	even	ADV
cuesj-900	167	2	if	if	SCONJ
cuesj-900	167	3	you	you	PRON
cuesj-900	167	4	read	read	VERB
cuesj-900	167	5	a	a	DET
cuesj-900	167	6	lot	lot	NOUN
cuesj-900	167	7	of	of	ADP
cuesj-900	167	8	other	other	ADJ
cuesj-900	167	9	literature	literature	NOUN
cuesj-900	167	10	on	on	ADP
cuesj-900	167	11	precision	precision	NOUN
cuesj-900	167	12	and	and	CCONJ
cuesj-900	167	13	recall	recall	NOUN
cuesj-900	167	14	,	,	PUNCT
cuesj-900	167	15	you	you	PRON
cuesj-900	167	16	can	can	AUX
cuesj-900	167	17	not	not	PART
cuesj-900	167	18	ignore	ignore	VERB
cuesj-900	167	19	the	the	DET
cuesj-900	167	20	other	other	ADJ
cuesj-900	167	21	measure	measure	NOUN
cuesj-900	167	22	,	,	PUNCT
cuesj-900	167	23	f	f	X
cuesj-900	167	24	-	-	PUNCT
cuesj-900	167	25	measure	measure	NOUN
cuesj-900	167	26	,	,	PUNCT
cuesj-900	167	27	which	which	PRON
cuesj-900	167	28	is	be	AUX
cuesj-900	167	29	a	a	DET
cuesj-900	167	30	function	function	NOUN
cuesj-900	167	31	of	of	ADP
cuesj-900	167	32	those	those	DET
cuesj-900	167	33	two	two	NUM
cuesj-900	167	34	variables	variable	NOUN
cuesj-900	167	35	.	.	PUNCT
cuesj-900	168	1	the	the	DET
cuesj-900	168	2	formula	formula	NOUN
cuesj-900	168	3	is	be	AUX
cuesj-900	168	4	as	as	SCONJ
cuesj-900	168	5	follows	follow	VERB
cuesj-900	168	6	:	:	PUNCT
cuesj-900	168	7	f	f	X
cuesj-900	168	8	-	-	PUNCT
cuesj-900	168	9	measure=2	measure=2	PROPN
cuesj-900	168	10	*	*	PUNCT
cuesj-900	168	11	�	�	PROPN
cuesj-900	168	12	precision*recall	precision*recall	PROPN
cuesj-900	168	13	precision+recall	precision+recall	PROPN
cuesj-900	168	14	�	�	PROPN
cuesj-900	168	15	f	f	NOUN
cuesj-900	168	16	-	-	PUNCT
cuesj-900	168	17	measure	measure	NOUN
cuesj-900	168	18	is	be	AUX
cuesj-900	168	19	necessary	necessary	ADJ
cuesj-900	168	20	when	when	SCONJ
cuesj-900	168	21	seeking	seek	VERB
cuesj-900	168	22	to	to	PART
cuesj-900	168	23	balance	balance	VERB
cuesj-900	168	24	precision	precision	NOUN
cuesj-900	168	25	and	and	CCONJ
cuesj-900	168	26	recall	recall	NOUN
cuesj-900	168	27	.	.	PUNCT
cuesj-900	169	1	what	what	PRON
cuesj-900	169	2	distinguishes	distinguish	VERB
cuesj-900	169	3	f	f	NOUN
cuesj-900	169	4	-	-	PUNCT
cuesj-900	169	5	measure	measure	NOUN
cuesj-900	169	6	and	and	CCONJ
cuesj-900	169	7	accuracy	accuracy	NOUN
cuesj-900	169	8	from	from	ADP
cuesj-900	169	9	one	one	NUM
cuesj-900	169	10	another	another	DET
cuesj-900	169	11	then	then	ADV
cuesj-900	169	12	?	?	PUNCT
cuesj-900	170	1	if	if	SCONJ
cuesj-900	170	2	we	we	PRON
cuesj-900	170	3	need	need	VERB
cuesj-900	170	4	to	to	PART
cuesj-900	170	5	find	find	VERB
cuesj-900	170	6	a	a	DET
cuesj-900	170	7	balance	balance	NOUN
cuesj-900	170	8	between	between	ADP
cuesj-900	170	9	precision	precision	NOUN
cuesj-900	170	10	and	and	CCONJ
cuesj-900	170	11	recall	recall	NOUN
cuesj-900	170	12	and	and	CCONJ
cuesj-900	170	13	there	there	PRON
cuesj-900	170	14	is	be	VERB
cuesj-900	170	15	an	an	DET
cuesj-900	170	16	uneven	uneven	ADJ
cuesj-900	170	17	class	class	NOUN
cuesj-900	170	18	distribution	distribution	NOUN
cuesj-900	170	19	,	,	PUNCT
cuesj-900	170	20	f	f	X
cuesj-900	170	21	-	-	PUNCT
cuesj-900	170	22	measure	measure	NOUN
cuesj-900	170	23	would	would	AUX
cuesj-900	170	24	be	be	AUX
cuesj-900	170	25	a	a	DET
cuesj-900	170	26	better	well	ADJ
cuesj-900	170	27	statistic	statistic	NOUN
cuesj-900	170	28	to	to	PART
cuesj-900	170	29	utilize	utilize	VERB
cuesj-900	170	30	because	because	SCONJ
cuesj-900	170	31	false	false	ADJ
cuesj-900	170	32	negatives	negative	NOUN
cuesj-900	170	33	and	and	CCONJ
cuesj-900	170	34	false	false	ADJ
cuesj-900	170	35	positives	positive	NOUN
cuesj-900	170	36	often	often	ADV
cuesj-900	170	37	have	have	VERB
cuesj-900	170	38	business	business	NOUN
cuesj-900	170	39	expenses	expense	NOUN
cuesj-900	170	40	(	(	PUNCT
cuesj-900	170	41	physical	physical	ADJ
cuesj-900	170	42	and	and	CCONJ
cuesj-900	170	43	intangible	intangible	ADJ
cuesj-900	170	44	)	)	PUNCT
cuesj-900	170	45	.	.	PUNCT
cuesj-900	171	1	as	as	SCONJ
cuesj-900	171	2	we	we	PRON
cuesj-900	171	3	have	have	AUX
cuesj-900	171	4	previously	previously	ADV
cuesj-900	171	5	seen	see	VERB
cuesj-900	171	6	,	,	PUNCT
cuesj-900	171	7	a	a	DET
cuesj-900	171	8	huge	huge	ADJ
cuesj-900	171	9	number	number	NOUN
cuesj-900	171	10	of	of	ADP
cuesj-900	171	11	true	true	ADJ
cuesj-900	171	12	negatives	negative	NOUN
cuesj-900	171	13	,	,	PUNCT
cuesj-900	171	14	which	which	PRON
cuesj-900	171	15	are	be	AUX
cuesj-900	171	16	typically	typically	ADV
cuesj-900	171	17	overlooked	overlook	VERB
cuesj-900	171	18	in	in	ADP
cuesj-900	171	19	corporate	corporate	ADJ
cuesj-900	171	20	settings	setting	NOUN
cuesj-900	171	21	,	,	PUNCT
cuesj-900	171	22	can	can	AUX
cuesj-900	171	23	significantly	significantly	ADV
cuesj-900	171	24	contribute	contribute	VERB
cuesj-900	171	25	to	to	ADP
cuesj-900	171	26	accuracy	accuracy	NOUN
cuesj-900	171	27	(	(	PUNCT
cuesj-900	171	28	a	a	DET
cuesj-900	171	29	large	large	ADJ
cuesj-900	171	30	number	number	NOUN
cuesj-900	171	31	of	of	ADP
cuesj-900	171	32	actual	actual	ADJ
cuesj-900	171	33	negatives	negative	NOUN
cuesj-900	171	34	)	)	PUNCT
cuesj-900	171	35	.	.	PUNCT
cuesj-900	172	1	receiver	receiver	ADJ
cuesj-900	172	2	operating	operate	VERB
cuesj-900	172	3	characteristic	characteristic	NOUN
cuesj-900	172	4	(	(	PUNCT
cuesj-900	172	5	roc	roc	PROPN
cuesj-900	172	6	)	)	PUNCT
cuesj-900	172	7	area	area	NOUN
cuesj-900	172	8	a	a	DET
cuesj-900	172	9	graph	graph	NOUN
cuesj-900	172	10	that	that	PRON
cuesj-900	172	11	shows	show	VERB
cuesj-900	172	12	how	how	SCONJ
cuesj-900	172	13	well	well	ADV
cuesj-900	172	14	a	a	DET
cuesj-900	172	15	classification	classification	NOUN
cuesj-900	172	16	model	model	NOUN
cuesj-900	172	17	works	work	VERB
cuesj-900	172	18	at	at	ADP
cuesj-900	172	19	each	each	DET
cuesj-900	172	20	classification	classification	NOUN
cuesj-900	172	21	threshold	threshold	NOUN
cuesj-900	172	22	is	be	AUX
cuesj-900	172	23	called	call	VERB
cuesj-900	172	24	a	a	DET
cuesj-900	172	25	receiver	receiver	ADJ
cuesj-900	172	26	operating	operate	VERB
cuesj-900	172	27	characteristic	characteristic	ADJ
cuesj-900	172	28	curve	curve	NOUN
cuesj-900	172	29	,	,	PUNCT
cuesj-900	172	30	or	or	CCONJ
cuesj-900	172	31	roc	roc	PROPN
cuesj-900	172	32	area	area	NOUN
cuesj-900	172	33	.	.	PUNCT
cuesj-900	173	1	on	on	ADP
cuesj-900	173	2	this	this	DET
cuesj-900	173	3	curve	curve	NOUN
cuesj-900	173	4	,	,	PUNCT
cuesj-900	173	5	two	two	NUM
cuesj-900	173	6	parameters	parameter	NOUN
cuesj-900	173	7	are	be	AUX
cuesj-900	173	8	plotted	plot	VERB
cuesj-900	173	9	:	:	PUNCT
cuesj-900	173	10	•	•	NUM
cuesj-900	173	11	true	true	ADJ
cuesj-900	173	12	positive	positive	ADJ
cuesj-900	173	13	rate	rate	NOUN
cuesj-900	173	14	(	(	PUNCT
cuesj-900	173	15	tpr	tpr	NOUN
cuesj-900	173	16	)	)	PUNCT
cuesj-900	173	17	•	•	NUM
cuesj-900	173	18	false	false	ADJ
cuesj-900	173	19	positive	positive	ADJ
cuesj-900	173	20	rate	rate	NOUN
cuesj-900	173	21	(	(	PUNCT
cuesj-900	173	22	fpr	fpr	NOUN
cuesj-900	173	23	)	)	PUNCT
cuesj-900	173	24	tpr	tpr	NOUN
cuesj-900	173	25	is	be	AUX
cuesj-900	173	26	a	a	DET
cuesj-900	173	27	synonym	synonym	NOUN
cuesj-900	173	28	for	for	ADP
cuesj-900	173	29	recall	recall	NOUN
cuesj-900	173	30	and	and	CCONJ
cuesj-900	173	31	is	be	AUX
cuesj-900	173	32	therefore	therefore	ADV
cuesj-900	173	33	defined	define	VERB
cuesj-900	173	34	as	as	SCONJ
cuesj-900	173	35	follows	follow	VERB
cuesj-900	173	36	:	:	PUNCT
cuesj-900	173	37	tpr=	tpr=	PROPN
cuesj-900	173	38	�	�	PROPN
cuesj-900	173	39	tp	tp	PART
cuesj-900	173	40	tp+fn	tp+fn	VERB
cuesj-900	173	41	fpr	fpr	NOUN
cuesj-900	173	42	is	be	AUX
cuesj-900	173	43	defined	define	VERB
cuesj-900	173	44	as	as	SCONJ
cuesj-900	173	45	follows	follow	VERB
cuesj-900	173	46	:	:	PUNCT
cuesj-900	173	47	fpr=	fpr=	PROPN
cuesj-900	173	48	�	�	PROPN
cuesj-900	173	49	fp	fp	X
cuesj-900	173	50	fp+fn	fp+fn	ADP
cuesj-900	173	51	in	in	ADP
cuesj-900	173	52	a	a	DET
cuesj-900	173	53	roc	roc	PROPN
cuesj-900	173	54	curve	curve	NOUN
cuesj-900	173	55	,	,	PUNCT
cuesj-900	173	56	tpr	tpr	PROPN
cuesj-900	173	57	vs.	vs.	X
cuesj-900	173	58	fpr	fpr	NOUN
cuesj-900	173	59	for	for	ADP
cuesj-900	173	60	various	various	ADJ
cuesj-900	173	61	classification	classification	NOUN
cuesj-900	173	62	criteria	criterion	NOUN
cuesj-900	173	63	are	be	AUX
cuesj-900	173	64	displayed	display	VERB
cuesj-900	173	65	.	.	PUNCT
cuesj-900	174	1	as	as	SCONJ
cuesj-900	174	2	the	the	DET
cuesj-900	174	3	classification	classification	NOUN
cuesj-900	174	4	threshold	threshold	NOUN
cuesj-900	174	5	is	be	AUX
cuesj-900	174	6	lowered	lower	VERB
cuesj-900	174	7	,	,	PUNCT
cuesj-900	174	8	more	more	ADJ
cuesj-900	174	9	items	item	NOUN
cuesj-900	174	10	are	be	AUX
cuesj-900	174	11	categorized	categorize	VERB
cuesj-900	174	12	as	as	ADP
cuesj-900	174	13	positive	positive	ADJ
cuesj-900	174	14	,	,	PUNCT
cuesj-900	174	15	which	which	PRON
cuesj-900	174	16	increases	increase	VERB
cuesj-900	174	17	the	the	DET
cuesj-900	174	18	quantity	quantity	NOUN
cuesj-900	174	19	of	of	ADP
cuesj-900	174	20	both	both	DET
cuesj-900	174	21	false	false	ADJ
cuesj-900	174	22	positives	positive	NOUN
cuesj-900	174	23	and	and	CCONJ
cuesj-900	174	24	true	true	ADJ
cuesj-900	174	25	positives	positive	NOUN
cuesj-900	174	26	.	.	PUNCT
cuesj-900	175	1	data	datum	NOUN
cuesj-900	175	2	we	we	PRON
cuesj-900	175	3	used	use	VERB
cuesj-900	175	4	a	a	DET
cuesj-900	175	5	dataset	dataset	NOUN
cuesj-900	175	6	about	about	ADP
cuesj-900	175	7	the	the	DET
cuesj-900	175	8	predicting	predict	VERB
cuesj-900	175	9	whether	whether	SCONJ
cuesj-900	175	10	it	it	PRON
cuesj-900	175	11	will	will	AUX
cuesj-900	175	12	rain	rain	VERB
cuesj-900	175	13	or	or	CCONJ
cuesj-900	175	14	not	not	PART
cuesj-900	175	15	using	use	VERB
cuesj-900	175	16	some	some	DET
cuesj-900	175	17	weather	weather	NOUN
cuesj-900	175	18	conditions	condition	NOUN
cuesj-900	175	19	.	.	PUNCT
cuesj-900	176	1	and	and	CCONJ
cuesj-900	176	2	we	we	PRON
cuesj-900	176	3	obtained	obtain	VERB
cuesj-900	176	4	this	this	DET
cuesj-900	176	5	table	table	NOUN
cuesj-900	176	6	1	1	NUM
cuesj-900	176	7	:	:	PUNCT
cuesj-900	176	8	summary	summary	NOUN
cuesj-900	176	9	of	of	ADP
cuesj-900	176	10	clustering	clustering	ADJ
cuesj-900	176	11	algorithms	algorithms	NOUN
cuesj-900	176	12	algorithm	algorithm	NOUN
cuesj-900	176	13	size	size	NOUN
cuesj-900	176	14	of	of	ADP
cuesj-900	176	15	dataset	dataset	NOUN
cuesj-900	176	16	type	type	NOUN
cuesj-900	176	17	of	of	ADP
cuesj-900	176	18	data	datum	NOUN
cuesj-900	176	19	complexity	complexity	NOUN
cuesj-900	176	20	computation	computation	NOUN
cuesj-900	176	21	speed	speed	NOUN
cuesj-900	176	22	modifications	modification	NOUN
cuesj-900	176	23	corrections	correction	NOUN
cuesj-900	176	24	cluster	cluster	NOUN
cuesj-900	176	25	shape	shape	NOUN
cuesj-900	176	26	result	result	NOUN
cuesj-900	176	27	interpretation	interpretation	NOUN
cuesj-900	176	28	k	k	NOUN
cuesj-900	176	29	-	-	PUNCT
cuesj-900	176	30	means	mean	VERB
cuesj-900	176	31	large	large	ADJ
cuesj-900	176	32	numerical	numerical	ADJ
cuesj-900	176	33	o	o	PROPN
cuesj-900	176	34	(	(	PUNCT
cuesj-900	176	35	nkd	nkd	PROPN
cuesj-900	176	36	)	)	PUNCT
cuesj-900	176	37	fast	fast	ADJ
cuesj-900	176	38	flexible	flexible	ADJ
cuesj-900	176	39	none	none	NOUN
cuesj-900	176	40	convex	convex	VERB
cuesj-900	176	41	easy	easy	ADJ
cuesj-900	176	42	k	k	ADJ
cuesj-900	176	43	-	-	PUNCT
cuesj-900	176	44	medoids	medoids	PRON
cuesj-900	176	45	small	small	ADJ
cuesj-900	176	46	categorical	categorical	ADJ
cuesj-900	176	47	o	o	NOUN
cuesj-900	176	48	(	(	PUNCT
cuesj-900	176	49	n^2	n^2	PROPN
cuesj-900	176	50	dt	dt	PROPN
cuesj-900	176	51	)	)	PUNCT
cuesj-900	176	52	moderate	moderate	ADJ
cuesj-900	176	53	difficult	difficult	ADJ
cuesj-900	176	54	none	none	NOUN
cuesj-900	176	55	convex	convex	VERB
cuesj-900	176	56	difficult	difficult	ADJ
cuesj-900	176	57	hierarchical	hierarchical	ADJ
cuesj-900	176	58	large	large	ADJ
cuesj-900	176	59	numerical	numerical	ADJ
cuesj-900	176	60	o	o	NOUN
cuesj-900	176	61	(	(	PUNCT
cuesj-900	176	62	n	n	CCONJ
cuesj-900	176	63	)	)	PUNCT
cuesj-900	176	64	slow	slow	ADJ
cuesj-900	176	65	flexible	flexible	ADJ
cuesj-900	176	66	none	none	NOUN
cuesj-900	176	67	convex	convex	VERB
cuesj-900	176	68	easy	easy	ADJ
cuesj-900	176	69	table	table	NOUN
cuesj-900	176	70	2	2	NUM
cuesj-900	176	71	:	:	PUNCT
cuesj-900	176	72	confusion	confusion	NOUN
cuesj-900	176	73	matrix	matrix	NOUN
cuesj-900	176	74	predicted	predict	VERB
cuesj-900	176	75	true	true	ADJ
cuesj-900	176	76	class	class	NOUN
cuesj-900	176	77	true	true	ADJ
cuesj-900	176	78	positive	positive	ADJ
cuesj-900	176	79	false	false	ADJ
cuesj-900	176	80	negative	negative	ADJ
cuesj-900	176	81	false	false	ADJ
cuesj-900	176	82	positive	positive	ADJ
cuesj-900	176	83	true	true	ADJ
cuesj-900	176	84	negative	negative	ADJ
cuesj-900	176	85	mulla	mulla	NOUN
cuesj-900	176	86	and	and	CCONJ
cuesj-900	176	87	demir	demir	PROPN
cuesj-900	176	88	:	:	PUNCT
cuesj-900	176	89	the	the	DET
cuesj-900	176	90	use	use	NOUN
cuesj-900	176	91	of	of	ADP
cuesj-900	176	92	clustering	clustering	NOUN
cuesj-900	176	93	and	and	CCONJ
cuesj-900	176	94	classification	classification	NOUN
cuesj-900	176	95	methods	method	NOUN
cuesj-900	176	96	in	in	ADP
cuesj-900	176	97	machine	machine	NOUN
cuesj-900	176	98	learning	learn	VERB
cuesj-900	176	99	57	57	NUM
cuesj-900	176	100	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-900	176	101	cuesj	cuesj	NOUN
cuesj-900	176	102	2023	2023	NUM
cuesj-900	176	103	,	,	PUNCT
cuesj-900	176	104	7	7	NUM
cuesj-900	176	105	(	(	PUNCT
cuesj-900	176	106	1	1	NUM
cuesj-900	176	107	):	):	PUNCT
cuesj-900	176	108	52	52	NUM
cuesj-900	176	109	-	-	SYM
cuesj-900	176	110	59	59	NUM
cuesj-900	176	111	dataset	dataset	NOUN
cuesj-900	176	112	from	from	ADP
cuesj-900	176	113	the	the	DET
cuesj-900	176	114	kaggle	kaggle	ADJ
cuesj-900	176	115	side	side	NOUN
cuesj-900	176	116	it	it	PRON
cuesj-900	176	117	was	be	AUX
cuesj-900	176	118	shown	show	VERB
cuesj-900	176	119	in	in	ADP
cuesj-900	176	120	this	this	DET
cuesj-900	176	121	link	link	NOUN
cuesj-900	176	122	.	.	PUNCT
cuesj-900	177	1	https://	https://	PROPN
cuesj-900	177	2	www.kaggle.com/datasets/ananthr1/weather-prediction	www.kaggle.com/datasets/ananthr1/weather-prediction	PROPN
cuesj-900	177	3	.	.	PUNCT
cuesj-900	178	1	about	about	ADP
cuesj-900	178	2	dataset	dataset	NOUN
cuesj-900	178	3	:	:	PUNCT
cuesj-900	178	4	using	use	VERB
cuesj-900	178	5	coloms	colom	NOUN
cuesj-900	178	6	:	:	PUNCT
cuesj-900	178	7	•	•	NUM
cuesj-900	178	8	precipitation	precipitation	NOUN
cuesj-900	178	9	•	•	NUM
cuesj-900	178	10	tempreture	tempreture	NOUN
cuesj-900	178	11	min	min	PROPN
cuesj-900	178	12	•	•	PROPN
cuesj-900	178	13	tempreture	tempreture	NOUN
cuesj-900	178	14	max	max	PROPN
cuesj-900	178	15	•	•	NUM
cuesj-900	178	16	wind	wind	NOUN
cuesj-900	178	17	we	we	PRON
cuesj-900	178	18	will	will	AUX
cuesj-900	178	19	forecast	forecast	VERB
cuesj-900	178	20	the	the	DET
cuesj-900	178	21	weather	weather	NOUN
cuesj-900	178	22	as	as	SCONJ
cuesj-900	178	23	follows	follow	VERB
cuesj-900	178	24	:	:	PUNCT
cuesj-900	178	25	•	•	NUM
cuesj-900	178	26	drizzle	drizzle	NOUN
cuesj-900	178	27	•	•	NUM
cuesj-900	178	28	rain	rain	NOUN
cuesj-900	178	29	•	•	NUM
cuesj-900	178	30	snow	snow	NOUN
cuesj-900	178	31	•	•	ADP
cuesj-900	178	32	sun	sun	PROPN
cuesj-900	178	33	•	•	PROPN
cuesj-900	178	34	fog	fog	NOUN
cuesj-900	178	35	results	result	NOUN
cuesj-900	178	36	and	and	CCONJ
cuesj-900	178	37	discussion	discussion	NOUN
cuesj-900	178	38	three	three	NUM
cuesj-900	178	39	distinct	distinct	ADJ
cuesj-900	178	40	categorization	categorization	NOUN
cuesj-900	178	41	methods	method	NOUN
cuesj-900	178	42	were	be	AUX
cuesj-900	178	43	employed	employ	VERB
cuesj-900	178	44	for	for	SCONJ
cuesj-900	178	45	the	the	DET
cuesj-900	178	46	data	datum	NOUN
cuesj-900	178	47	being	be	AUX
cuesj-900	178	48	studied	study	VERB
cuesj-900	178	49	during	during	ADP
cuesj-900	178	50	this	this	DET
cuesj-900	178	51	study	study	NOUN
cuesj-900	178	52	.	.	PUNCT
cuesj-900	179	1	all	all	DET
cuesj-900	179	2	three	three	NUM
cuesj-900	179	3	algorithms	algorithm	NOUN
cuesj-900	179	4	dt	dt	PROPN
cuesj-900	179	5	,	,	PUNCT
cuesj-900	179	6	ann	ann	PROPN
cuesj-900	179	7	,	,	PUNCT
cuesj-900	179	8	and	and	CCONJ
cuesj-900	179	9	knn	knn	PROPN
cuesj-900	179	10	were	be	AUX
cuesj-900	179	11	used	use	VERB
cuesj-900	179	12	in	in	ADP
cuesj-900	179	13	the	the	DET
cuesj-900	179	14	classification	classification	NOUN
cuesj-900	179	15	process	process	NOUN
cuesj-900	179	16	.	.	PUNCT
cuesj-900	180	1	we	we	PRON
cuesj-900	180	2	utilized	utilize	VERB
cuesj-900	180	3	k=1	k=1	PROPN
cuesj-900	180	4	and	and	CCONJ
cuesj-900	180	5	divided	divide	VERB
cuesj-900	180	6	the	the	DET
cuesj-900	180	7	dataset	dataset	NOUN
cuesj-900	180	8	into	into	ADP
cuesj-900	180	9	10	10	NUM
cuesj-900	180	10	cross	cros	NOUN
cuesj-900	180	11	-	-	NOUN
cuesj-900	180	12	validations	validation	NOUN
cuesj-900	180	13	for	for	ADP
cuesj-900	180	14	the	the	DET
cuesj-900	180	15	knn	knn	PROPN
cuesj-900	180	16	algorithms	algorithms	PROPN
cuesj-900	180	17	.	.	PUNCT
cuesj-900	181	1	the	the	DET
cuesj-900	181	2	quantity	quantity	NOUN
cuesj-900	181	3	of	of	ADP
cuesj-900	181	4	data	datum	NOUN
cuesj-900	181	5	used	use	VERB
cuesj-900	181	6	for	for	ADP
cuesj-900	181	7	reducederror	reducederror	NOUN
cuesj-900	181	8	pruning	prune	VERB
cuesj-900	181	9	when	when	SCONJ
cuesj-900	181	10	we	we	PRON
cuesj-900	181	11	used	use	VERB
cuesj-900	181	12	dt	dt	PROPN
cuesj-900	181	13	methods	method	NOUN
cuesj-900	181	14	was	be	AUX
cuesj-900	181	15	defined	define	VERB
cuesj-900	181	16	according	accord	VERB
cuesj-900	181	17	to	to	ADP
cuesj-900	181	18	the	the	DET
cuesj-900	181	19	trimming	trim	VERB
cuesj-900	181	20	confidence	confidence	NOUN
cuesj-900	181	21	factor	factor	NOUN
cuesj-900	181	22	,	,	PUNCT
cuesj-900	181	23	which	which	PRON
cuesj-900	181	24	was	be	AUX
cuesj-900	181	25	0.25	0.25	NUM
cuesj-900	181	26	.	.	PUNCT
cuesj-900	182	1	all	all	DET
cuesj-900	182	2	three	three	NUM
cuesj-900	182	3	classifications	classification	NOUN
cuesj-900	182	4	and	and	CCONJ
cuesj-900	182	5	clustering	cluster	VERB
cuesj-900	182	6	algorithms	algorithm	NOUN
cuesj-900	182	7	were	be	AUX
cuesj-900	182	8	employed	employ	VERB
cuesj-900	182	9	,	,	PUNCT
cuesj-900	182	10	and	and	CCONJ
cuesj-900	182	11	the	the	DET
cuesj-900	182	12	training	training	NOUN
cuesj-900	182	13	rate	rate	NOUN
cuesj-900	182	14	was	be	AUX
cuesj-900	182	15	66	66	NUM
cuesj-900	182	16	%	%	NOUN
cuesj-900	182	17	and	and	CCONJ
cuesj-900	182	18	the	the	DET
cuesj-900	182	19	testing	testing	NOUN
cuesj-900	182	20	rate	rate	NOUN
cuesj-900	182	21	was	be	AUX
cuesj-900	182	22	34	34	NUM
cuesj-900	182	23	%	%	NOUN
cuesj-900	182	24	.	.	PUNCT
cuesj-900	183	1	we	we	PRON
cuesj-900	183	2	separated	separate	VERB
cuesj-900	183	3	into	into	ADP
cuesj-900	183	4	five	five	NUM
cuesj-900	183	5	groups	group	NOUN
cuesj-900	183	6	for	for	ADP
cuesj-900	183	7	k	k	ADJ
cuesj-900	183	8	-	-	PUNCT
cuesj-900	183	9	mean	mean	ADJ
cuesj-900	183	10	methods	method	NOUN
cuesj-900	183	11	,	,	PUNCT
cuesj-900	183	12	used	use	VERB
cuesj-900	183	13	10	10	NUM
cuesj-900	183	14	seeds	seed	NOUN
cuesj-900	183	15	,	,	PUNCT
cuesj-900	183	16	and	and	CCONJ
cuesj-900	183	17	decided	decide	VERB
cuesj-900	183	18	to	to	PART
cuesj-900	183	19	use	use	VERB
cuesj-900	183	20	false	false	ADJ
cuesj-900	183	21	for	for	ADP
cuesj-900	183	22	debug	debug	NOUN
cuesj-900	183	23	and	and	CCONJ
cuesj-900	183	24	false	false	ADJ
cuesj-900	183	25	for	for	ADP
cuesj-900	183	26	display	display	NOUN
cuesj-900	183	27	stander	stander	NOUN
cuesj-900	183	28	division	division	NOUN
cuesj-900	183	29	.	.	PUNCT
cuesj-900	184	1	the	the	DET
cuesj-900	184	2	effectiveness	effectiveness	NOUN
cuesj-900	184	3	of	of	ADP
cuesj-900	184	4	the	the	DET
cuesj-900	184	5	three	three	NUM
cuesj-900	184	6	classification	classification	NOUN
cuesj-900	184	7	methods	method	NOUN
cuesj-900	184	8	in	in	ADP
cuesj-900	184	9	table	table	NOUN
cuesj-900	184	10	3	3	NUM
cuesj-900	184	11	was	be	AUX
cuesj-900	184	12	evaluated	evaluate	VERB
cuesj-900	184	13	using	use	VERB
cuesj-900	184	14	the	the	DET
cuesj-900	184	15	accuracy	accuracy	NOUN
cuesj-900	184	16	precision	precision	NOUN
cuesj-900	184	17	,	,	PUNCT
cuesj-900	184	18	f	f	X
cuesj-900	184	19	-	-	PUNCT
cuesj-900	184	20	measure	measure	NOUN
cuesj-900	184	21	,	,	PUNCT
cuesj-900	184	22	and	and	CCONJ
cuesj-900	184	23	roc	roc	PROPN
cuesj-900	184	24	region	region	NOUN
cuesj-900	184	25	metrics	metric	NOUN
cuesj-900	184	26	.	.	PUNCT
cuesj-900	185	1	as	as	SCONJ
cuesj-900	185	2	shown	show	VERB
cuesj-900	185	3	in	in	ADP
cuesj-900	185	4	table	table	NOUN
cuesj-900	185	5	3	3	NUM
cuesj-900	185	6	for	for	ADP
cuesj-900	185	7	the	the	DET
cuesj-900	185	8	classification	classification	NOUN
cuesj-900	185	9	method	method	NOUN
cuesj-900	185	10	,	,	PUNCT
cuesj-900	185	11	the	the	DET
cuesj-900	185	12	highest	high	ADJ
cuesj-900	185	13	performance	performance	NOUN
cuesj-900	185	14	depending	depend	VERB
cuesj-900	185	15	on	on	ADP
cuesj-900	185	16	accuracy	accuracy	NOUN
cuesj-900	185	17	and	and	CCONJ
cuesj-900	185	18	precision	precision	NOUN
cuesj-900	185	19	was	be	AUX
cuesj-900	185	20	84.8	84.8	NUM
cuesj-900	185	21	,	,	PUNCT
cuesj-900	185	22	78.7	78.7	NUM
cuesj-900	185	23	obtained	obtain	VERB
cuesj-900	185	24	in	in	ADP
cuesj-900	185	25	dt	dt	PROPN
cuesj-900	185	26	,	,	PUNCT
cuesj-900	185	27	but	but	CCONJ
cuesj-900	185	28	the	the	DET
cuesj-900	185	29	higher	high	ADJ
cuesj-900	185	30	performance	performance	NOUN
cuesj-900	185	31	according	accord	VERB
cuesj-900	185	32	to	to	ADP
cuesj-900	185	33	the	the	DET
cuesj-900	185	34	f	f	NOUN
cuesj-900	185	35	-	-	PUNCT
cuesj-900	185	36	measure	measure	NOUN
cuesj-900	185	37	and	and	CCONJ
cuesj-900	185	38	roc	roc	PROPN
cuesj-900	185	39	area	area	NOUN
cuesj-900	185	40	was	be	AUX
cuesj-900	185	41	obtained	obtain	VERB
cuesj-900	185	42	in	in	ADP
cuesj-900	185	43	ann	ann	PROPN
cuesj-900	185	44	.	.	PUNCT
cuesj-900	186	1	on	on	ADP
cuesj-900	186	2	the	the	DET
cuesj-900	186	3	other	other	ADJ
cuesj-900	186	4	hand	hand	NOUN
cuesj-900	186	5	,	,	PUNCT
cuesj-900	186	6	the	the	DET
cuesj-900	186	7	best	good	ADJ
cuesj-900	186	8	model	model	NOUN
cuesj-900	186	9	for	for	ADP
cuesj-900	186	10	dt	dt	PROPN
cuesj-900	186	11	,	,	PUNCT
cuesj-900	186	12	ann	ann	PROPN
cuesj-900	186	13	,	,	PUNCT
cuesj-900	186	14	and	and	CCONJ
cuesj-900	186	15	knn	knn	PROPN
cuesj-900	186	16	algorithm	algorithm	PROPN
cuesj-900	186	17	in	in	ADP
cuesj-900	186	18	this	this	DET
cuesj-900	186	19	study	study	NOUN
cuesj-900	186	20	was	be	AUX
cuesj-900	186	21	obtained	obtain	VERB
cuesj-900	186	22	in	in	ADP
cuesj-900	186	23	roc	roc	PROPN
cuesj-900	186	24	area	area	NOUN
cuesj-900	186	25	.	.	PUNCT
cuesj-900	187	1	figure	figure	NOUN
cuesj-900	187	2	3	3	NUM
cuesj-900	187	3	shows	show	VERB
cuesj-900	187	4	one	one	NUM
cuesj-900	187	5	of	of	ADP
cuesj-900	187	6	the	the	DET
cuesj-900	187	7	classification	classification	NOUN
cuesj-900	187	8	methods	method	NOUN
cuesj-900	187	9	,	,	PUNCT
cuesj-900	187	10	so	so	SCONJ
cuesj-900	187	11	it	it	PRON
cuesj-900	187	12	can	can	AUX
cuesj-900	187	13	be	be	AUX
cuesj-900	187	14	decided	decide	VERB
cuesj-900	187	15	based	base	VERB
cuesj-900	187	16	on	on	ADP
cuesj-900	187	17	figure	figure	NOUN
cuesj-900	187	18	3	3	NUM
cuesj-900	187	19	.	.	PUNCT
cuesj-900	188	1	there	there	PRON
cuesj-900	188	2	are	be	VERB
cuesj-900	188	3	5	5	NUM
cuesj-900	188	4	decisions	decision	NOUN
cuesj-900	188	5	from	from	ADP
cuesj-900	188	6	figure	figure	NOUN
cuesj-900	188	7	3	3	NUM
cuesj-900	188	8	.	.	PUNCT
cuesj-900	189	1	the	the	DET
cuesj-900	189	2	dt	dt	PROPN
cuesj-900	189	3	region	region	PROPN
cuesj-900	189	4	measurement	measurement	NOUN
cuesj-900	189	5	was	be	AUX
cuesj-900	189	6	measured	measure	VERB
cuesj-900	189	7	for	for	ADP
cuesj-900	189	8	testing	test	VERB
cuesj-900	189	9	the	the	DET
cuesj-900	189	10	efficiency	efficiency	NOUN
cuesj-900	189	11	of	of	ADP
cuesj-900	189	12	the	the	DET
cuesj-900	189	13	dt	dt	PROPN
cuesj-900	189	14	algorithm	algorithm	NOUN
cuesj-900	189	15	as	as	SCONJ
cuesj-900	189	16	shown	show	VERB
cuesj-900	189	17	in	in	ADP
cuesj-900	189	18	figure	figure	NOUN
cuesj-900	189	19	3	3	NUM
cuesj-900	189	20	.	.	NOUN
cuesj-900	189	21	•	•	NOUN
cuesj-900	189	22	the	the	DET
cuesj-900	189	23	decision	decision	NOUN
cuesj-900	189	24	1	1	NUM
cuesj-900	189	25	:	:	PUNCT
cuesj-900	189	26	precipitation	precipitation	NOUN
cuesj-900	189	27	to	to	ADP
cuesj-900	189	28	sun	sun	PROPN
cuesj-900	189	29	.	.	PROPN
cuesj-900	190	1	•	•	NUM
cuesj-900	190	2	the	the	DET
cuesj-900	190	3	decision	decision	NOUN
cuesj-900	190	4	2	2	NUM
cuesj-900	190	5	:	:	PUNCT
cuesj-900	190	6	precipitation	precipitation	NOUN
cuesj-900	190	7	to	to	AUX
cuesj-900	190	8	temperature	temperature	NOUN
cuesj-900	190	9	min	min	NOUN
cuesj-900	190	10	to	to	PART
cuesj-900	190	11	rain	rain	VERB
cuesj-900	190	12	.	.	PUNCT
cuesj-900	191	1	•	•	NUM
cuesj-900	191	2	the	the	DET
cuesj-900	191	3	decision	decision	NOUN
cuesj-900	191	4	3	3	NUM
cuesj-900	191	5	:	:	PUNCT
cuesj-900	191	6	precipitation	precipitation	NOUN
cuesj-900	191	7	to	to	AUX
cuesj-900	191	8	temperature	temperature	NOUN
cuesj-900	191	9	min	min	NOUN
cuesj-900	191	10	to	to	PART
cuesj-900	191	11	wind	wind	VERB
cuesj-900	191	12	to	to	ADP
cuesj-900	191	13	snow	snow	NOUN
cuesj-900	191	14	.	.	PUNCT
cuesj-900	192	1	•	•	NUM
cuesj-900	192	2	the	the	DET
cuesj-900	192	3	decision	decision	NOUN
cuesj-900	192	4	4	4	NUM
cuesj-900	192	5	:	:	PUNCT
cuesj-900	192	6	precipitation	precipitation	NOUN
cuesj-900	192	7	to	to	AUX
cuesj-900	192	8	temperature	temperature	NOUN
cuesj-900	192	9	min	min	NOUN
cuesj-900	192	10	to	to	PART
cuesj-900	192	11	wind	wind	VERB
cuesj-900	192	12	to	to	ADP
cuesj-900	192	13	temperature	temperature	NOUN
cuesj-900	192	14	max	max	PROPN
cuesj-900	192	15	to	to	PART
cuesj-900	192	16	rain	rain	VERB
cuesj-900	192	17	.	.	PUNCT
cuesj-900	193	1	•	•	NUM
cuesj-900	193	2	the	the	DET
cuesj-900	193	3	decision	decision	NOUN
cuesj-900	193	4	5	5	NUM
cuesj-900	193	5	:	:	PUNCT
cuesj-900	193	6	precipitation	precipitation	NOUN
cuesj-900	193	7	to	to	AUX
cuesj-900	193	8	temperature	temperature	NOUN
cuesj-900	193	9	min	min	NOUN
cuesj-900	193	10	to	to	PART
cuesj-900	193	11	wind	wind	VERB
cuesj-900	193	12	to	to	ADP
cuesj-900	193	13	temperature	temperature	NOUN
cuesj-900	193	14	max	max	PROPN
cuesj-900	193	15	to	to	ADP
cuesj-900	193	16	snow	snow	NOUN
cuesj-900	193	17	.	.	PUNCT
cuesj-900	194	1	the	the	DET
cuesj-900	194	2	k	k	ADJ
cuesj-900	194	3	-	-	PUNCT
cuesj-900	194	4	means	means	PROPN
cuesj-900	194	5	region	region	NOUN
cuesj-900	194	6	measurement	measurement	NOUN
cuesj-900	194	7	was	be	AUX
cuesj-900	194	8	measured	measure	VERB
cuesj-900	194	9	for	for	ADP
cuesj-900	194	10	testing	test	VERB
cuesj-900	194	11	the	the	DET
cuesj-900	194	12	efficiency	efficiency	NOUN
cuesj-900	194	13	of	of	ADP
cuesj-900	194	14	the	the	DET
cuesj-900	194	15	clustering	cluster	VERB
cuesj-900	194	16	algorithm	algorithm	NOUN
cuesj-900	194	17	as	as	SCONJ
cuesj-900	194	18	shown	show	VERB
cuesj-900	194	19	in	in	ADP
cuesj-900	194	20	table	table	NOUN
cuesj-900	194	21	4	4	NUM
cuesj-900	194	22	.	.	PUNCT
cuesj-900	195	1	while	while	SCONJ
cuesj-900	195	2	we	we	PRON
cuesj-900	195	3	study	study	VERB
cuesj-900	195	4	the	the	DET
cuesj-900	195	5	output	output	NOUN
cuesj-900	195	6	report	report	NOUN
cuesj-900	195	7	,	,	PUNCT
cuesj-900	195	8	we	we	PRON
cuesj-900	195	9	can	can	AUX
cuesj-900	195	10	see	see	VERB
cuesj-900	195	11	5	5	NUM
cuesj-900	195	12	classes	class	NOUN
cuesj-900	195	13	(	(	PUNCT
cuesj-900	195	14	class	class	NOUN
cuesj-900	195	15	0	0	NUM
cuesj-900	195	16	,	,	PUNCT
cuesj-900	195	17	class	class	NOUN
cuesj-900	195	18	1	1	NUM
cuesj-900	195	19	,	,	PUNCT
cuesj-900	195	20	class	class	NOUN
cuesj-900	195	21	2	2	NUM
cuesj-900	195	22	,	,	PUNCT
cuesj-900	195	23	class	class	NOUN
cuesj-900	195	24	3	3	NUM
cuesj-900	195	25	,	,	PUNCT
cuesj-900	195	26	class	class	NOUN
cuesj-900	195	27	4	4	NUM
cuesj-900	195	28	)	)	PUNCT
cuesj-900	195	29	.	.	PUNCT
cuesj-900	196	1	in	in	ADP
cuesj-900	196	2	the	the	DET
cuesj-900	196	3	report	report	NOUN
cuesj-900	196	4	appear	appear	VERB
cuesj-900	196	5	as	as	ADP
cuesj-900	196	6	number	number	NOUN
cuesj-900	196	7	of	of	ADP
cuesj-900	196	8	clusters	cluster	NOUN
cuesj-900	196	9	selected	select	VERB
cuesj-900	196	10	by	by	ADP
cuesj-900	196	11	cross	cross	NOUN
cuesj-900	196	12	-	-	NOUN
cuesj-900	196	13	validation	validation	ADJ
cuesj-900	196	14	:	:	PUNCT
cuesj-900	196	15	5	5	NUM
cuesj-900	196	16	you	you	PRON
cuesj-900	196	17	can	can	AUX
cuesj-900	196	18	find	find	VERB
cuesj-900	196	19	the	the	DET
cuesj-900	196	20	centroid	centroid	NOUN
cuesj-900	196	21	of	of	ADP
cuesj-900	196	22	the	the	DET
cuesj-900	196	23	entire	entire	ADJ
cuesj-900	196	24	population	population	NOUN
cuesj-900	196	25	in	in	ADP
cuesj-900	196	26	the	the	DET
cuesj-900	196	27	first	first	ADJ
cuesj-900	196	28	column	column	NOUN
cuesj-900	196	29	.	.	PUNCT
cuesj-900	197	1	the	the	DET
cuesj-900	197	2	centroids	centroid	NOUN
cuesj-900	197	3	for	for	ADP
cuesj-900	197	4	clusters	cluster	NOUN
cuesj-900	197	5	0	0	NUM
cuesj-900	197	6	,	,	PUNCT
cuesj-900	197	7	1	1	NUM
cuesj-900	197	8	,	,	PUNCT
cuesj-900	197	9	2	2	NUM
cuesj-900	197	10	,	,	PUNCT
cuesj-900	197	11	3	3	NUM
cuesj-900	197	12	,	,	PUNCT
cuesj-900	197	13	and	and	CCONJ
cuesj-900	197	14	4	4	NUM
cuesj-900	197	15	may	may	AUX
cuesj-900	197	16	be	be	AUX
cuesj-900	197	17	found	find	VERB
cuesj-900	197	18	,	,	PUNCT
cuesj-900	197	19	respectively	respectively	ADV
cuesj-900	197	20	,	,	PUNCT
cuesj-900	197	21	in	in	ADP
cuesj-900	197	22	the	the	DET
cuesj-900	197	23	second	second	ADJ
cuesj-900	197	24	,	,	PUNCT
cuesj-900	197	25	third	third	ADJ
cuesj-900	197	26	,	,	PUNCT
cuesj-900	197	27	fourth	fourth	ADJ
cuesj-900	197	28	,	,	PUNCT
cuesj-900	197	29	and	and	CCONJ
cuesj-900	197	30	fifth	fifth	ADJ
cuesj-900	197	31	columns	column	NOUN
cuesj-900	197	32	.	.	PUNCT
cuesj-900	198	1	the	the	DET
cuesj-900	198	2	centroid	centroid	NOUN
cuesj-900	198	3	coordinate	coordinate	NOUN
cuesj-900	198	4	for	for	ADP
cuesj-900	198	5	each	each	DET
cuesj-900	198	6	row	row	NOUN
cuesj-900	198	7	’s	’s	PART
cuesj-900	198	8	respective	respective	ADJ
cuesj-900	198	9	dimension	dimension	NOUN
cuesj-900	198	10	is	be	AUX
cuesj-900	198	11	provided	provide	VERB
cuesj-900	198	12	.	.	PUNCT
cuesj-900	199	1	it	it	PRON
cuesj-900	199	2	is	be	AUX
cuesj-900	199	3	important	important	ADJ
cuesj-900	199	4	to	to	PART
cuesj-900	199	5	note	note	VERB
cuesj-900	199	6	that	that	SCONJ
cuesj-900	199	7	this	this	DET
cuesj-900	199	8	step	step	NOUN
cuesj-900	199	9	of	of	ADP
cuesj-900	199	10	the	the	DET
cuesj-900	199	11	process	process	NOUN
cuesj-900	199	12	is	be	AUX
cuesj-900	199	13	locating	locate	VERB
cuesj-900	199	14	the	the	DET
cuesj-900	199	15	centroids	centroid	NOUN
cuesj-900	199	16	.	.	PUNCT
cuesj-900	200	1	the	the	DET
cuesj-900	200	2	centroids	centroid	NOUN
cuesj-900	200	3	are	be	AUX
cuesj-900	200	4	not	not	PART
cuesj-900	200	5	unique	unique	ADJ
cuesj-900	200	6	and	and	CCONJ
cuesj-900	200	7	are	be	AUX
cuesj-900	200	8	the	the	DET
cuesj-900	200	9	output	output	NOUN
cuesj-900	200	10	of	of	ADP
cuesj-900	200	11	a	a	DET
cuesj-900	200	12	particular	particular	ADJ
cuesj-900	200	13	run	run	NOUN
cuesj-900	200	14	of	of	ADP
cuesj-900	200	15	the	the	DET
cuesj-900	200	16	algorithm	algorithm	NOUN
cuesj-900	200	17	;	;	PUNCT
cuesj-900	200	18	a	a	DET
cuesj-900	200	19	different	different	ADJ
cuesj-900	200	20	run	run	NOUN
cuesj-900	200	21	could	could	AUX
cuesj-900	200	22	produce	produce	VERB
cuesj-900	200	23	a	a	DET
cuesj-900	200	24	different	different	ADJ
cuesj-900	200	25	set	set	NOUN
cuesj-900	200	26	of	of	ADP
cuesj-900	200	27	centroids	centroid	NOUN
cuesj-900	200	28	.	.	PUNCT
cuesj-900	201	1	there	there	PRON
cuesj-900	201	2	is	be	VERB
cuesj-900	201	3	a	a	DET
cuesj-900	201	4	“	"	PUNCT
cuesj-900	201	5	clusters	cluster	NOUN
cuesj-900	201	6	prior	prior	ADJ
cuesj-900	201	7	”	"	PUNCT
cuesj-900	201	8	probability	probability	NOUN
cuesj-900	201	9	for	for	ADP
cuesj-900	201	10	each	each	DET
cuesj-900	201	11	cluster	cluster	NOUN
cuesj-900	201	12	.	.	PUNCT
cuesj-900	202	1	in	in	ADP
cuesj-900	202	2	the	the	DET
cuesj-900	202	3	estimators	estimator	NOUN
cuesj-900	202	4	,	,	PUNCT
cuesj-900	202	5	each	each	DET
cuesj-900	202	6	conceivable	conceivable	ADJ
cuesj-900	202	7	attribute	attribute	NOUN
cuesj-900	202	8	value	value	NOUN
cuesj-900	202	9	is	be	AUX
cuesj-900	202	10	represented	represent	VERB
cuesj-900	202	11	by	by	ADP
cuesj-900	202	12	a	a	DET
cuesj-900	202	13	number	number	NOUN
cuesj-900	202	14	,	,	PUNCT
cuesj-900	202	15	which	which	PRON
cuesj-900	202	16	is	be	AUX
cuesj-900	202	17	treated	treat	VERB
cuesj-900	202	18	sequentially	sequentially	ADV
cuesj-900	202	19	.	.	PUNCT
cuesj-900	203	1	classes	class	NOUN
cuesj-900	203	2	when	when	SCONJ
cuesj-900	203	3	we	we	PRON
cuesj-900	203	4	used	use	VERB
cuesj-900	203	5	k	k	ADJ
cuesj-900	203	6	-	-	ADJ
cuesj-900	203	7	mean	mean	ADJ
cuesj-900	203	8	algorithms	algorithm	NOUN
cuesj-900	203	9	:	:	PUNCT
cuesj-900	203	10	•	•	NUM
cuesj-900	203	11	class	class	NOUN
cuesj-900	203	12	0	0	NUM
cuesj-900	203	13	has	have	AUX
cuesj-900	203	14	total	total	ADJ
cuesj-900	203	15	232	232	NUM
cuesj-900	203	16	things	thing	NOUN
cuesj-900	203	17	,	,	PUNCT
cuesj-900	203	18	out	out	ADP
cuesj-900	203	19	of	of	ADP
cuesj-900	203	20	which	which	DET
cuesj-900	203	21	majority	majority	NOUN
cuesj-900	203	22	of	of	ADP
cuesj-900	203	23	things	thing	NOUN
cuesj-900	203	24	(	(	PUNCT
cuesj-900	203	25	16	16	NUM
cuesj-900	203	26	%	%	NOUN
cuesj-900	203	27	)	)	PUNCT
cuesj-900	203	28	data	datum	NOUN
cuesj-900	203	29	sets	set	NOUN
cuesj-900	203	30	.	.	PUNCT
cuesj-900	204	1	•	•	NUM
cuesj-900	204	2	class	class	NOUN
cuesj-900	204	3	1	1	NUM
cuesj-900	204	4	has	have	VERB
cuesj-900	204	5	total	total	NOUN
cuesj-900	204	6	of	of	ADP
cuesj-900	204	7	449	449	NUM
cuesj-900	204	8	things	thing	NOUN
cuesj-900	204	9	,	,	PUNCT
cuesj-900	204	10	out	out	ADP
cuesj-900	204	11	of	of	ADP
cuesj-900	204	12	which	which	DET
cuesj-900	204	13	majority	majority	NOUN
cuesj-900	204	14	of	of	ADP
cuesj-900	204	15	things	thing	NOUN
cuesj-900	204	16	(	(	PUNCT
cuesj-900	204	17	31	31	NUM
cuesj-900	204	18	%	%	NOUN
cuesj-900	204	19	)	)	PUNCT
cuesj-900	204	20	data	datum	NOUN
cuesj-900	204	21	sets	set	NOUN
cuesj-900	204	22	.	.	PUNCT
cuesj-900	205	1	•	•	NUM
cuesj-900	205	2	class	class	NOUN
cuesj-900	205	3	has	have	AUX
cuesj-900	205	4	total	total	ADJ
cuesj-900	205	5	355	355	NUM
cuesj-900	205	6	things	thing	NOUN
cuesj-900	205	7	,	,	PUNCT
cuesj-900	205	8	out	out	ADP
cuesj-900	205	9	of	of	ADP
cuesj-900	205	10	which	which	DET
cuesj-900	205	11	majority	majority	NOUN
cuesj-900	205	12	of	of	ADP
cuesj-900	205	13	things	thing	NOUN
cuesj-900	205	14	(	(	PUNCT
cuesj-900	205	15	24	24	NUM
cuesj-900	205	16	%	%	NOUN
cuesj-900	205	17	)	)	PUNCT
cuesj-900	205	18	data	datum	NOUN
cuesj-900	205	19	sets	set	NOUN
cuesj-900	205	20	.	.	PUNCT
cuesj-900	206	1	•	•	NUM
cuesj-900	206	2	class	class	NOUN
cuesj-900	206	3	3	3	NUM
cuesj-900	206	4	has	have	AUX
cuesj-900	206	5	total	total	VERB
cuesj-900	206	6	258	258	NUM
cuesj-900	206	7	things	thing	NOUN
cuesj-900	206	8	,	,	PUNCT
cuesj-900	206	9	out	out	ADP
cuesj-900	206	10	of	of	ADP
cuesj-900	206	11	which	which	DET
cuesj-900	206	12	majority	majority	NOUN
cuesj-900	206	13	of	of	ADP
cuesj-900	206	14	things	thing	NOUN
cuesj-900	206	15	(	(	PUNCT
cuesj-900	206	16	18	18	NUM
cuesj-900	206	17	%	%	NOUN
cuesj-900	206	18	)	)	PUNCT
cuesj-900	206	19	data	datum	NOUN
cuesj-900	206	20	sets	set	NOUN
cuesj-900	206	21	.	.	PUNCT
cuesj-900	207	1	•	•	NUM
cuesj-900	207	2	class	class	NOUN
cuesj-900	207	3	4	4	NUM
cuesj-900	207	4	has	have	AUX
cuesj-900	207	5	total	total	ADJ
cuesj-900	207	6	167	167	NUM
cuesj-900	207	7	things	thing	NOUN
cuesj-900	207	8	,	,	PUNCT
cuesj-900	207	9	out	out	ADP
cuesj-900	207	10	of	of	ADP
cuesj-900	207	11	which	which	DET
cuesj-900	207	12	majority	majority	NOUN
cuesj-900	207	13	of	of	ADP
cuesj-900	207	14	things	thing	NOUN
cuesj-900	207	15	(	(	PUNCT
cuesj-900	207	16	11	11	NUM
cuesj-900	207	17	%	%	NOUN
cuesj-900	207	18	)	)	PUNCT
cuesj-900	207	19	data	datum	NOUN
cuesj-900	207	20	sets	set	NOUN
cuesj-900	207	21	.	.	PUNCT
cuesj-900	208	1	classes	class	NOUN
cuesj-900	208	2	when	when	SCONJ
cuesj-900	208	3	we	we	PRON
cuesj-900	208	4	used	use	VERB
cuesj-900	208	5	hierarchical	hierarchical	ADJ
cuesj-900	208	6	algorithms	algorithm	NOUN
cuesj-900	208	7	:	:	PUNCT
cuesj-900	208	8	•	•	NUM
cuesj-900	208	9	class	class	NOUN
cuesj-900	208	10	0	0	NUM
cuesj-900	208	11	has	have	AUX
cuesj-900	208	12	total	total	VERB
cuesj-900	208	13	53	53	NUM
cuesj-900	208	14	things	thing	NOUN
cuesj-900	208	15	,	,	PUNCT
cuesj-900	208	16	out	out	ADP
cuesj-900	208	17	of	of	ADP
cuesj-900	208	18	which	which	DET
cuesj-900	208	19	majority	majority	NOUN
cuesj-900	208	20	of	of	ADP
cuesj-900	208	21	things	thing	NOUN
cuesj-900	208	22	(	(	PUNCT
cuesj-900	208	23	4	4	NUM
cuesj-900	208	24	%	%	NOUN
cuesj-900	208	25	)	)	PUNCT
cuesj-900	208	26	data	datum	NOUN
cuesj-900	208	27	sets	set	NOUN
cuesj-900	208	28	.	.	PUNCT
cuesj-900	209	1	•	•	NUM
cuesj-900	209	2	class	class	NOUN
cuesj-900	209	3	1	1	NUM
cuesj-900	209	4	has	have	VERB
cuesj-900	209	5	total	total	NOUN
cuesj-900	209	6	of	of	ADP
cuesj-900	209	7	641	641	NUM
cuesj-900	209	8	things	thing	NOUN
cuesj-900	209	9	,	,	PUNCT
cuesj-900	209	10	out	out	ADP
cuesj-900	209	11	of	of	ADP
cuesj-900	209	12	which	which	DET
cuesj-900	209	13	majority	majority	NOUN
cuesj-900	209	14	of	of	ADP
cuesj-900	209	15	things	thing	NOUN
cuesj-900	209	16	(	(	PUNCT
cuesj-900	209	17	44	44	NUM
cuesj-900	209	18	%	%	NOUN
cuesj-900	209	19	)	)	PUNCT
cuesj-900	209	20	data	datum	NOUN
cuesj-900	209	21	sets	set	NOUN
cuesj-900	209	22	.	.	PUNCT
cuesj-900	210	1	•	•	NUM
cuesj-900	210	2	class	class	NOUN
cuesj-900	210	3	has	have	AUX
cuesj-900	210	4	total	total	VERB
cuesj-900	210	5	640	640	NUM
cuesj-900	210	6	things	thing	NOUN
cuesj-900	210	7	,	,	PUNCT
cuesj-900	210	8	out	out	ADP
cuesj-900	210	9	of	of	ADP
cuesj-900	210	10	which	which	DET
cuesj-900	210	11	majority	majority	NOUN
cuesj-900	210	12	of	of	ADP
cuesj-900	210	13	things	thing	NOUN
cuesj-900	210	14	(	(	PUNCT
cuesj-900	210	15	44	44	NUM
cuesj-900	210	16	%	%	NOUN
cuesj-900	210	17	)	)	PUNCT
cuesj-900	210	18	data	datum	NOUN
cuesj-900	210	19	sets	set	NOUN
cuesj-900	210	20	.	.	PUNCT
cuesj-900	211	1	•	•	NUM
cuesj-900	211	2	class	class	NOUN
cuesj-900	211	3	3	3	NUM
cuesj-900	211	4	has	have	AUX
cuesj-900	211	5	total	total	ADJ
cuesj-900	211	6	26	26	NUM
cuesj-900	211	7	things	thing	NOUN
cuesj-900	211	8	,	,	PUNCT
cuesj-900	211	9	out	out	ADP
cuesj-900	211	10	of	of	ADP
cuesj-900	211	11	which	which	DET
cuesj-900	211	12	majority	majority	NOUN
cuesj-900	211	13	of	of	ADP
cuesj-900	211	14	things	thing	NOUN
cuesj-900	211	15	(	(	PUNCT
cuesj-900	211	16	2	2	NUM
cuesj-900	211	17	%	%	NOUN
cuesj-900	211	18	)	)	PUNCT
cuesj-900	211	19	data	datum	NOUN
cuesj-900	211	20	sets	set	NOUN
cuesj-900	211	21	.	.	PUNCT
cuesj-900	212	1	•	•	NUM
cuesj-900	212	2	class	class	NOUN
cuesj-900	212	3	4	4	NUM
cuesj-900	212	4	has	have	AUX
cuesj-900	212	5	total	total	VERB
cuesj-900	212	6	101	101	NUM
cuesj-900	212	7	things	thing	NOUN
cuesj-900	212	8	,	,	PUNCT
cuesj-900	212	9	out	out	ADP
cuesj-900	212	10	of	of	ADP
cuesj-900	212	11	which	which	DET
cuesj-900	212	12	majority	majority	NOUN
cuesj-900	212	13	of	of	ADP
cuesj-900	212	14	things	thing	NOUN
cuesj-900	212	15	(	(	PUNCT
cuesj-900	212	16	7	7	NUM
cuesj-900	212	17	%	%	NOUN
cuesj-900	212	18	)	)	PUNCT
cuesj-900	212	19	data	datum	NOUN
cuesj-900	212	20	sets	set	NOUN
cuesj-900	212	21	.	.	PUNCT
cuesj-900	213	1	table	table	NOUN
cuesj-900	213	2	3	3	NUM
cuesj-900	213	3	:	:	PUNCT
cuesj-900	213	4	the	the	DET
cuesj-900	213	5	performance	performance	NOUN
cuesj-900	213	6	of	of	ADP
cuesj-900	213	7	the	the	DET
cuesj-900	213	8	classification	classification	NOUN
cuesj-900	213	9	algorithms	algorithm	NOUN
cuesj-900	213	10	using	use	VERB
cuesj-900	213	11	for	for	ADP
cuesj-900	213	12	weather	weather	NOUN
cuesj-900	213	13	data	datum	NOUN
cuesj-900	213	14	summary	summary	NOUN
cuesj-900	213	15	accuracy	accuracy	NOUN
cuesj-900	213	16	precision	precision	NOUN
cuesj-900	213	17	f	f	NOUN
cuesj-900	213	18	-	-	PUNCT
cuesj-900	213	19	measure	measure	NOUN
cuesj-900	213	20	roc	roc	PROPN
cuesj-900	213	21	area	area	NOUN
cuesj-900	213	22	dt	dt	X
cuesj-900	213	23	84.8	84.8	NUM
cuesj-900	213	24	78.7	78.7	NUM
cuesj-900	213	25	80.7	80.7	NUM
cuesj-900	213	26	89.5	89.5	NUM
cuesj-900	213	27	ann	ann	PROPN
cuesj-900	213	28	82.5	82.5	NUM
cuesj-900	213	29	74.4	74.4	NUM
cuesj-900	213	30	84.9	84.9	NUM
cuesj-900	213	31	91.7	91.7	NUM
cuesj-900	213	32	knn	knn	NOUN
cuesj-900	213	33	67.3	67.3	NUM
cuesj-900	213	34	67.8	67.8	NUM
cuesj-900	213	35	67.6	67.6	NUM
cuesj-900	213	36	74.5	74.5	NUM
cuesj-900	213	37	dt	dt	NOUN
cuesj-900	213	38	:	:	PUNCT
cuesj-900	213	39	decision	decision	NOUN
cuesj-900	213	40	tree	tree	NOUN
cuesj-900	213	41	,	,	PUNCT
cuesj-900	213	42	ann	ann	PROPN
cuesj-900	213	43	:	:	PUNCT
cuesj-900	213	44	artificial	artificial	ADJ
cuesj-900	213	45	neural	neural	ADJ
cuesj-900	213	46	network	network	NOUN
cuesj-900	213	47	,	,	PUNCT
cuesj-900	213	48	knn	knn	PROPN
cuesj-900	213	49	:	:	PUNCT
cuesj-900	213	50	k	k	ADJ
cuesj-900	213	51	-	-	PUNCT
cuesj-900	213	52	nearest	near	ADJ
cuesj-900	213	53	neighbor	neighbor	NOUN
cuesj-900	213	54	figure	figure	NOUN
cuesj-900	213	55	3	3	NUM
cuesj-900	213	56	:	:	PUNCT
cuesj-900	213	57	decision	decision	NOUN
cuesj-900	213	58	tree	tree	NOUN
cuesj-900	213	59	visualization	visualization	NOUN
cuesj-900	213	60	mulla	mulla	NOUN
cuesj-900	213	61	and	and	CCONJ
cuesj-900	213	62	demir	demir	PROPN
cuesj-900	213	63	:	:	PUNCT
cuesj-900	213	64	the	the	DET
cuesj-900	213	65	use	use	NOUN
cuesj-900	213	66	of	of	ADP
cuesj-900	213	67	clustering	clustering	NOUN
cuesj-900	213	68	and	and	CCONJ
cuesj-900	213	69	classification	classification	NOUN
cuesj-900	213	70	methods	method	NOUN
cuesj-900	213	71	in	in	ADP
cuesj-900	213	72	machine	machine	NOUN
cuesj-900	213	73	learning	learn	VERB
cuesj-900	213	74	58	58	NUM
cuesj-900	213	75	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-900	213	76	cuesj	cuesj	NOUN
cuesj-900	213	77	2023	2023	NUM
cuesj-900	213	78	,	,	PUNCT
cuesj-900	213	79	7	7	NUM
cuesj-900	213	80	(	(	PUNCT
cuesj-900	213	81	1	1	NUM
cuesj-900	213	82	):	):	PUNCT
cuesj-900	213	83	52	52	NUM
cuesj-900	213	84	-	-	SYM
cuesj-900	213	85	59	59	NUM
cuesj-900	213	86	we	we	PRON
cuesj-900	213	87	examined	examine	VERB
cuesj-900	213	88	two	two	NUM
cuesj-900	213	89	clustering	clustering	ADJ
cuesj-900	213	90	techniques	technique	NOUN
cuesj-900	213	91	from	from	ADP
cuesj-900	213	92	table	table	NOUN
cuesj-900	213	93	5	5	NUM
cuesj-900	213	94	and	and	CCONJ
cuesj-900	213	95	separated	separate	VERB
cuesj-900	213	96	both	both	PRON
cuesj-900	213	97	of	of	ADP
cuesj-900	213	98	them	they	PRON
cuesj-900	213	99	into	into	ADP
cuesj-900	213	100	five	five	NUM
cuesj-900	213	101	clusters	cluster	NOUN
cuesj-900	213	102	in	in	ADP
cuesj-900	213	103	order	order	NOUN
cuesj-900	213	104	to	to	PART
cuesj-900	213	105	identify	identify	VERB
cuesj-900	213	106	some	some	DET
cuesj-900	213	107	distinct	distinct	ADJ
cuesj-900	213	108	things	thing	NOUN
cuesj-900	213	109	.	.	PUNCT
cuesj-900	214	1	at	at	ADP
cuesj-900	214	2	the	the	DET
cuesj-900	214	3	time	time	NOUN
cuesj-900	214	4	,	,	PUNCT
cuesj-900	214	5	k	k	X
cuesj-900	214	6	-	-	ADJ
cuesj-900	214	7	mean	mean	PROPN
cuesj-900	214	8	was	be	AUX
cuesj-900	214	9	preferred	prefer	VERB
cuesj-900	214	10	over	over	ADP
cuesj-900	214	11	hierarchical	hierarchical	ADJ
cuesj-900	214	12	methods	method	NOUN
cuesj-900	214	13	.	.	PUNCT
cuesj-900	215	1	fewer	few	ADJ
cuesj-900	215	2	iterations	iteration	NOUN
cuesj-900	215	3	in	in	ADP
cuesj-900	215	4	the	the	DET
cuesj-900	215	5	hierarchy	hierarchy	NOUN
cuesj-900	215	6	than	than	ADP
cuesj-900	215	7	the	the	DET
cuesj-900	215	8	k	k	NOUN
cuesj-900	215	9	-	-	NOUN
cuesj-900	215	10	mean	mean	ADJ
cuesj-900	215	11	.	.	PUNCT
cuesj-900	216	1	on	on	ADP
cuesj-900	216	2	the	the	DET
cuesj-900	216	3	other	other	ADJ
cuesj-900	216	4	hand	hand	NOUN
cuesj-900	216	5	,	,	PUNCT
cuesj-900	216	6	when	when	SCONJ
cuesj-900	216	7	using	use	VERB
cuesj-900	216	8	hierarchical	hierarchical	ADJ
cuesj-900	216	9	,	,	PUNCT
cuesj-900	216	10	we	we	PRON
cuesj-900	216	11	had	have	VERB
cuesj-900	216	12	zero	zero	NUM
cuesj-900	216	13	root	root	NOUN
cuesj-900	216	14	mean	mean	ADJ
cuesj-900	216	15	square	square	ADJ
cuesj-900	216	16	errors	error	NOUN
cuesj-900	216	17	(	(	PUNCT
cuesj-900	216	18	rmse	rmse	PROPN
cuesj-900	216	19	)	)	PUNCT
cuesj-900	216	20	,	,	PUNCT
cuesj-900	216	21	k	k	X
cuesj-900	216	22	-	-	ADJ
cuesj-900	216	23	mean	mean	ADJ
cuesj-900	216	24	algorithms	algorithm	NOUN
cuesj-900	216	25	experienced	experience	VERB
cuesj-900	216	26	a	a	DET
cuesj-900	216	27	1707.63	1707.63	NUM
cuesj-900	216	28	(	(	PUNCT
cuesj-900	216	29	rmse	rmse	NOUN
cuesj-900	216	30	)	)	PUNCT
cuesj-900	216	31	.	.	PUNCT
cuesj-900	217	1	conclusion	conclusion	VERB
cuesj-900	217	2	the	the	DET
cuesj-900	217	3	application	application	NOUN
cuesj-900	217	4	of	of	ADP
cuesj-900	217	5	data	datum	NOUN
cuesj-900	217	6	mining	mining	NOUN
cuesj-900	217	7	to	to	ADP
cuesj-900	217	8	enrollment	enrollment	NOUN
cuesj-900	217	9	management	management	NOUN
cuesj-900	217	10	is	be	AUX
cuesj-900	217	11	a	a	DET
cuesj-900	217	12	relatively	relatively	ADV
cuesj-900	217	13	recent	recent	ADJ
cuesj-900	217	14	innovation	innovation	NOUN
cuesj-900	217	15	.	.	PUNCT
cuesj-900	218	1	at	at	ADP
cuesj-900	218	2	the	the	DET
cuesj-900	218	3	moment	moment	NOUN
cuesj-900	218	4	,	,	PUNCT
cuesj-900	218	5	category	category	NOUN
cuesj-900	218	6	,	,	PUNCT
cuesj-900	218	7	and	and	CCONJ
cuesj-900	218	8	straightforward	straightforward	ADJ
cuesj-900	218	9	numerical	numerical	ADJ
cuesj-900	218	10	data	datum	NOUN
cuesj-900	218	11	are	be	AUX
cuesj-900	218	12	most	most	ADV
cuesj-900	218	13	frequently	frequently	ADV
cuesj-900	218	14	employed	employ	VERB
cuesj-900	218	15	in	in	ADP
cuesj-900	218	16	data	datum	NOUN
cuesj-900	218	17	mining	mining	NOUN
cuesj-900	218	18	.	.	PUNCT
cuesj-900	219	1	data	datum	NOUN
cuesj-900	219	2	mining	mining	NOUN
cuesj-900	219	3	will	will	AUX
cuesj-900	219	4	increasingly	increasingly	ADV
cuesj-900	219	5	incorporate	incorporate	VERB
cuesj-900	219	6	complicated	complicated	ADJ
cuesj-900	219	7	data	datum	NOUN
cuesj-900	219	8	types	type	NOUN
cuesj-900	219	9	in	in	ADP
cuesj-900	219	10	the	the	DET
cuesj-900	219	11	future	future	NOUN
cuesj-900	219	12	.	.	PUNCT
cuesj-900	220	1	by	by	ADP
cuesj-900	220	2	taking	take	VERB
cuesj-900	220	3	into	into	ADP
cuesj-900	220	4	account	account	NOUN
cuesj-900	220	5	more	more	ADJ
cuesj-900	220	6	variables	variable	NOUN
cuesj-900	220	7	and	and	CCONJ
cuesj-900	220	8	how	how	SCONJ
cuesj-900	220	9	they	they	PRON
cuesj-900	220	10	interact	interact	VERB
cuesj-900	220	11	with	with	ADP
cuesj-900	220	12	one	one	NUM
cuesj-900	220	13	another	another	DET
cuesj-900	220	14	,	,	PUNCT
cuesj-900	220	15	any	any	DET
cuesj-900	220	16	model	model	NOUN
cuesj-900	220	17	that	that	PRON
cuesj-900	220	18	has	have	AUX
cuesj-900	220	19	been	be	AUX
cuesj-900	220	20	created	create	VERB
cuesj-900	220	21	may	may	AUX
cuesj-900	220	22	also	also	ADV
cuesj-900	220	23	be	be	AUX
cuesj-900	220	24	reinforced	reinforce	VERB
cuesj-900	220	25	.	.	PUNCT
cuesj-900	221	1	new	new	ADJ
cuesj-900	221	2	techniques	technique	NOUN
cuesj-900	221	3	for	for	ADP
cuesj-900	221	4	discovering	discover	VERB
cuesj-900	221	5	the	the	DET
cuesj-900	221	6	most	most	ADV
cuesj-900	221	7	exciting	exciting	ADJ
cuesj-900	221	8	features	feature	NOUN
cuesj-900	221	9	,	,	PUNCT
cuesj-900	221	10	data	datum	NOUN
cuesj-900	221	11	mining	mining	NOUN
cuesj-900	221	12	research	research	NOUN
cuesj-900	221	13	will	will	AUX
cuesj-900	221	14	lead	lead	VERB
cuesj-900	221	15	to	to	ADP
cuesj-900	221	16	the	the	DET
cuesj-900	221	17	development	development	NOUN
cuesj-900	221	18	of	of	ADP
cuesj-900	221	19	the	the	DET
cuesj-900	221	20	data	datum	NOUN
cuesj-900	221	21	.	.	PUNCT
cuesj-900	222	1	as	as	SCONJ
cuesj-900	222	2	models	model	NOUN
cuesj-900	222	3	are	be	AUX
cuesj-900	222	4	developed	develop	VERB
cuesj-900	222	5	and	and	CCONJ
cuesj-900	222	6	implemented	implement	VERB
cuesj-900	222	7	,	,	PUNCT
cuesj-900	222	8	they	they	PRON
cuesj-900	222	9	can	can	AUX
cuesj-900	222	10	be	be	AUX
cuesj-900	222	11	used	use	VERB
cuesj-900	222	12	as	as	ADP
cuesj-900	222	13	a	a	DET
cuesj-900	222	14	tool	tool	NOUN
cuesj-900	222	15	for	for	ADP
cuesj-900	222	16	enrollment	enrollment	NOUN
cuesj-900	222	17	management	management	NOUN
cuesj-900	222	18	.	.	PUNCT
cuesj-900	223	1	this	this	DET
cuesj-900	223	2	study	study	NOUN
cuesj-900	223	3	showed	show	VERB
cuesj-900	223	4	that	that	SCONJ
cuesj-900	223	5	the	the	DET
cuesj-900	223	6	best	good	ADJ
cuesj-900	223	7	model	model	NOUN
cuesj-900	223	8	was	be	AUX
cuesj-900	223	9	dt	dt	VERB
cuesj-900	223	10	according	accord	VERB
cuesj-900	223	11	to	to	ADP
cuesj-900	223	12	accuracy	accuracy	NOUN
cuesj-900	223	13	and	and	CCONJ
cuesj-900	223	14	precision	precision	NOUN
cuesj-900	223	15	measures	measure	NOUN
cuesj-900	223	16	but	but	CCONJ
cuesj-900	223	17	the	the	DET
cuesj-900	223	18	best	good	ADJ
cuesj-900	223	19	model	model	NOUN
cuesj-900	223	20	according	accord	VERB
cuesj-900	223	21	to	to	ADP
cuesj-900	223	22	f	f	NOUN
cuesj-900	223	23	-	-	PUNCT
cuesj-900	223	24	measure	measure	NOUN
cuesj-900	223	25	and	and	CCONJ
cuesj-900	223	26	roc	roc	PROPN
cuesj-900	223	27	area	area	NOUN
cuesj-900	223	28	measures	measure	NOUN
cuesj-900	223	29	was	be	AUX
cuesj-900	224	1	ann	ann	PROPN
cuesj-900	224	2	.	.	PUNCT
cuesj-900	224	3	weka	weka	PROPN
cuesj-900	224	4	,	,	PUNCT
cuesj-900	224	5	a	a	DET
cuesj-900	224	6	data	data	NOUN
cuesj-900	224	7	mining	mining	NOUN
cuesj-900	224	8	tool	tool	NOUN
cuesj-900	224	9	,	,	PUNCT
cuesj-900	224	10	is	be	AUX
cuesj-900	224	11	utilized	utilize	VERB
cuesj-900	224	12	in	in	ADP
cuesj-900	224	13	this	this	DET
cuesj-900	224	14	paper	paper	NOUN
cuesj-900	224	15	to	to	PART
cuesj-900	224	16	carry	carry	VERB
cuesj-900	224	17	out	out	ADP
cuesj-900	224	18	the	the	DET
cuesj-900	224	19	clustering	clustering	NOUN
cuesj-900	224	20	.	.	PUNCT
cuesj-900	225	1	the	the	DET
cuesj-900	225	2	k	k	ADJ
cuesj-900	225	3	-	-	ADJ
cuesj-900	225	4	mean	mean	ADJ
cuesj-900	225	5	approach	approach	NOUN
cuesj-900	225	6	can	can	AUX
cuesj-900	225	7	cluster	cluster	VERB
cuesj-900	225	8	large	large	ADJ
cuesj-900	225	9	data	datum	NOUN
cuesj-900	225	10	sets	set	NOUN
cuesj-900	225	11	efficiently	efficiently	ADV
cuesj-900	225	12	,	,	PUNCT
cuesj-900	225	13	and	and	CCONJ
cuesj-900	225	14	as	as	SCONJ
cuesj-900	225	15	the	the	DET
cuesj-900	225	16	number	number	NOUN
cuesj-900	225	17	of	of	ADP
cuesj-900	225	18	clusters	cluster	NOUN
cuesj-900	225	19	rises	rise	VERB
cuesj-900	225	20	,	,	PUNCT
cuesj-900	225	21	so	so	ADV
cuesj-900	225	22	does	do	VERB
cuesj-900	225	23	its	its	PRON
cuesj-900	225	24	efficiency	efficiency	NOUN
cuesj-900	225	25	.	.	PUNCT
cuesj-900	226	1	the	the	DET
cuesj-900	226	2	k	k	ADJ
cuesj-900	226	3	-	-	ADJ
cuesj-900	226	4	mean	mean	ADJ
cuesj-900	226	5	method	method	NOUN
cuesj-900	226	6	outperforms	outperform	VERB
cuesj-900	226	7	the	the	DET
cuesj-900	226	8	hierarchical	hierarchical	ADJ
cuesj-900	226	9	clustering	clustering	ADJ
cuesj-900	226	10	algorithm	algorithm	NOUN
cuesj-900	226	11	.	.	PUNCT
cuesj-900	227	1	the	the	DET
cuesj-900	227	2	performance	performance	NOUN
cuesj-900	227	3	of	of	ADP
cuesj-900	227	4	k	k	NOUN
cuesj-900	227	5	-	-	PUNCT
cuesj-900	227	6	means	mean	VERB
cuesj-900	227	7	method	method	NOUN
cuesj-900	227	8	increases	increase	NOUN
cuesj-900	227	9	as	as	SCONJ
cuesj-900	227	10	the	the	DET
cuesj-900	227	11	root	root	NOUN
cuesj-900	227	12	mean	mean	VERB
cuesj-900	227	13	square	square	ADJ
cuesj-900	227	14	errors	error	NOUN
cuesj-900	227	15	(	(	PUNCT
cuesj-900	227	16	rmse	rmse	NOUN
cuesj-900	227	17	)	)	PUNCT
cuesj-900	227	18	drops	drop	NOUN
cuesj-900	227	19	and	and	CCONJ
cuesj-900	227	20	the	the	DET
cuesj-900	227	21	rmse	rmse	NOUN
cuesj-900	227	22	falls	fall	VERB
cuesj-900	227	23	as	as	ADP
cuesj-900	227	24	the	the	DET
cuesj-900	227	25	number	number	NOUN
cuesj-900	227	26	of	of	ADP
cuesj-900	227	27	clusters	cluster	NOUN
cuesj-900	227	28	.	.	PUNCT
cuesj-900	228	1	all	all	DET
cuesj-900	228	2	the	the	DET
cuesj-900	228	3	techniques	technique	NOUN
cuesj-900	228	4	have	have	VERB
cuesj-900	228	5	some	some	DET
cuesj-900	228	6	noise	noise	NOUN
cuesj-900	228	7	or	or	CCONJ
cuesj-900	228	8	ambiguity	ambiguity	NOUN
cuesj-900	228	9	in	in	ADP
cuesj-900	228	10	certain	certain	ADJ
cuesj-900	228	11	data	datum	NOUN
cuesj-900	228	12	when	when	SCONJ
cuesj-900	228	13	clustered	cluster	VERB
cuesj-900	228	14	.	.	PUNCT
cuesj-900	229	1	using	use	VERB
cuesj-900	229	2	massive	massive	ADJ
cuesj-900	229	3	datasets	dataset	NOUN
cuesj-900	229	4	significantly	significantly	ADV
cuesj-900	229	5	raises	raise	VERB
cuesj-900	229	6	the	the	DET
cuesj-900	229	7	quality	quality	NOUN
cuesj-900	229	8	of	of	ADP
cuesj-900	229	9	all	all	DET
cuesj-900	229	10	algorithms	algorithm	NOUN
cuesj-900	229	11	.	.	PUNCT
cuesj-900	230	1	the	the	DET
cuesj-900	230	2	k	k	NOUN
cuesj-900	230	3	-	-	PUNCT
cuesj-900	230	4	means	means	NOUN
cuesj-900	230	5	algorithm	algorithm	NOUN
cuesj-900	230	6	is	be	AUX
cuesj-900	230	7	quite	quite	ADV
cuesj-900	230	8	susceptible	susceptible	ADJ
cuesj-900	230	9	to	to	ADP
cuesj-900	230	10	dataset	dataset	NOUN
cuesj-900	230	11	noise	noise	NOUN
cuesj-900	230	12	.	.	PUNCT
cuesj-900	231	1	this	this	DET
cuesj-900	231	2	noise	noise	NOUN
cuesj-900	231	3	interferes	interfere	VERB
cuesj-900	231	4	with	with	ADP
cuesj-900	231	5	the	the	DET
cuesj-900	231	6	algorithm	algorithm	NOUN
cuesj-900	231	7	’s	’s	PART
cuesj-900	231	8	ability	ability	NOUN
cuesj-900	231	9	to	to	PART
cuesj-900	231	10	cluster	cluster	VERB
cuesj-900	231	11	data	datum	NOUN
cuesj-900	231	12	into	into	ADP
cuesj-900	231	13	the	the	DET
cuesj-900	231	14	proper	proper	ADJ
cuesj-900	231	15	clusters	cluster	NOUN
cuesj-900	231	16	and	and	CCONJ
cuesj-900	231	17	affects	affect	VERB
cuesj-900	231	18	the	the	DET
cuesj-900	231	19	algorithm	algorithm	NOUN
cuesj-900	231	20	’s	’s	PART
cuesj-900	231	21	output	output	NOUN
cuesj-900	231	22	.	.	PUNCT
cuesj-900	232	1	when	when	SCONJ
cuesj-900	232	2	working	work	VERB
cuesj-900	232	3	with	with	ADP
cuesj-900	232	4	huge	huge	ADJ
cuesj-900	232	5	datasets	dataset	NOUN
cuesj-900	232	6	,	,	PUNCT
cuesj-900	232	7	the	the	DET
cuesj-900	232	8	k	k	NOUN
cuesj-900	232	9	-	-	PUNCT
cuesj-900	232	10	means	means	NOUN
cuesj-900	232	11	method	method	NOUN
cuesj-900	232	12	produces	produce	VERB
cuesj-900	232	13	good	good	ADJ
cuesj-900	232	14	clusters	cluster	NOUN
cuesj-900	232	15	more	more	ADV
cuesj-900	232	16	quickly	quickly	ADV
cuesj-900	232	17	than	than	ADP
cuesj-900	232	18	alternative	alternative	ADJ
cuesj-900	232	19	clustering	clustering	ADJ
cuesj-900	232	20	techniques	technique	NOUN
cuesj-900	232	21	.	.	PUNCT
cuesj-900	233	1	for	for	ADP
cuesj-900	233	2	noisy	noisy	ADJ
cuesj-900	233	3	data	datum	NOUN
cuesj-900	233	4	,	,	PUNCT
cuesj-900	233	5	the	the	DET
cuesj-900	233	6	hierarchical	hierarchical	ADJ
cuesj-900	233	7	clustering	clustering	NOUN
cuesj-900	233	8	technique	technique	NOUN
cuesj-900	233	9	is	be	AUX
cuesj-900	233	10	more	more	ADV
cuesj-900	233	11	sensitive	sensitive	ADJ
cuesj-900	233	12	.	.	PUNCT
cuesj-900	234	1	weka	weka	NOUN
cuesj-900	234	2	only	only	ADV
cuesj-900	234	3	enables	enable	VERB
cuesj-900	234	4	sequential	sequential	ADJ
cuesj-900	234	5	single	single	ADJ
cuesj-900	234	6	-	-	PUNCT
cuesj-900	234	7	node	node	ADJ
cuesj-900	234	8	execution	execution	NOUN
cuesj-900	234	9	,	,	PUNCT
cuesj-900	234	10	which	which	PRON
cuesj-900	234	11	leads	lead	VERB
cuesj-900	234	12	to	to	ADP
cuesj-900	234	13	its	its	PRON
cuesj-900	234	14	inability	inability	NOUN
cuesj-900	234	15	to	to	PART
cuesj-900	234	16	handle	handle	VERB
cuesj-900	234	17	very	very	ADV
cuesj-900	234	18	large	large	ADJ
cuesj-900	234	19	datasets	dataset	NOUN
cuesj-900	234	20	.	.	PUNCT
cuesj-900	235	1	as	as	ADP
cuesj-900	235	2	a	a	DET
cuesj-900	235	3	result	result	NOUN
cuesj-900	235	4	,	,	PUNCT
cuesj-900	235	5	there	there	PRON
cuesj-900	235	6	is	be	VERB
cuesj-900	235	7	little	little	ADJ
cuesj-900	235	8	similarity	similarity	NOUN
cuesj-900	235	9	between	between	ADP
cuesj-900	235	10	clusters	cluster	NOUN
cuesj-900	235	11	when	when	SCONJ
cuesj-900	235	12	data	datum	NOUN
cuesj-900	235	13	are	be	AUX
cuesj-900	235	14	grouped	group	VERB
cuesj-900	235	15	or	or	CCONJ
cuesj-900	235	16	clustered	cluster	VERB
cuesj-900	235	17	,	,	PUNCT
cuesj-900	235	18	and	and	CCONJ
cuesj-900	235	19	significant	significant	ADJ
cuesj-900	235	20	similarity	similarity	NOUN
cuesj-900	235	21	within	within	ADP
cuesj-900	235	22	clusters	cluster	NOUN
cuesj-900	235	23	.	.	PUNCT
cuesj-900	236	1	one	one	NUM
cuesj-900	236	2	of	of	ADP
cuesj-900	236	3	the	the	DET
cuesj-900	236	4	clustering	clustering	ADJ
cuesj-900	236	5	methods	method	NOUN
cuesj-900	236	6	is	be	AUX
cuesj-900	236	7	the	the	DET
cuesj-900	236	8	k	k	ADJ
cuesj-900	236	9	-	-	PUNCT
cuesj-900	236	10	means	means	NOUN
cuesj-900	236	11	algorithm	algorithm	NOUN
cuesj-900	236	12	,	,	PUNCT
cuesj-900	236	13	which	which	PRON
cuesj-900	236	14	gathers	gather	VERB
cuesj-900	236	15	data	datum	NOUN
cuesj-900	236	16	based	base	VERB
cuesj-900	236	17	on	on	ADP
cuesj-900	236	18	their	their	PRON
cuesj-900	236	19	traits	trait	NOUN
cuesj-900	236	20	and	and	CCONJ
cuesj-900	236	21	qualities	quality	NOUN
cuesj-900	236	22	and	and	CCONJ
cuesj-900	236	23	runs	run	VERB
cuesj-900	236	24	the	the	DET
cuesj-900	236	25	clustering	cluster	VERB
cuesj-900	236	26	process	process	NOUN
cuesj-900	236	27	by	by	ADP
cuesj-900	236	28	shortening	shorten	VERB
cuesj-900	236	29	the	the	DET
cuesj-900	236	30	distances	distance	NOUN
cuesj-900	236	31	between	between	ADP
cuesj-900	236	32	the	the	DET
cuesj-900	236	33	data	datum	NOUN
cuesj-900	236	34	centers.[5	centers.[5	NOUN
cuesj-900	236	35	]	]	X
cuesj-900	236	36	the	the	DET
cuesj-900	236	37	finally	finally	ADV
cuesj-900	236	38	used	use	VERB
cuesj-900	236	39	dt	dt	PROPN
cuesj-900	236	40	,	,	PUNCT
cuesj-900	236	41	nb	nb	PROPN
cuesj-900	236	42	and	and	CCONJ
cuesj-900	236	43	svm	svm	VERB
cuesj-900	236	44	to	to	PART
cuesj-900	236	45	make	make	VERB
cuesj-900	236	46	design	design	NOUN
cuesj-900	236	47	of	of	ADP
cuesj-900	236	48	model	model	NOUN
cuesj-900	236	49	in	in	ADP
cuesj-900	236	50	pima	pima	PROPN
cuesj-900	236	51	dataset	dataset	PROPN
cuesj-900	236	52	but	but	CCONJ
cuesj-900	236	53	here	here	ADV
cuesj-900	236	54	we	we	PRON
cuesj-900	236	55	used	use	VERB
cuesj-900	236	56	dt	dt	PROPN
cuesj-900	236	57	,	,	PUNCT
cuesj-900	236	58	ann	ann	PROPN
cuesj-900	236	59	,	,	PUNCT
cuesj-900	236	60	knn	knn	VERB
cuesj-900	236	61	in	in	ADP
cuesj-900	236	62	weather	weather	NOUN
cuesj-900	236	63	dataset	dataset	PROPN
cuesj-900	236	64	.	.	PUNCT
cuesj-900	237	1	references	reference	NOUN
cuesj-900	237	2	1	1	NUM
cuesj-900	237	3	.	.	PUNCT
cuesj-900	237	4	m.	m.	NOUN
cuesj-900	237	5	baran	baran	PROPN
cuesj-900	237	6	.	.	PUNCT
cuesj-900	238	1	maki	maki	NOUN
cuesj-900	238	2	̇ne	̇ne	NOUN
cuesj-900	238	3	öğrenmesi̇	öğrenmesi̇	NOUN
cuesj-900	238	4	yöntemleri̇yle	yöntemleri̇yle	X
cuesj-900	238	5	çoklu	çoklu	ADJ
cuesj-900	238	6	eti̇ketli̇	eti̇ketli̇	PROPN
cuesj-900	238	7	veri̇leri̇n	veri̇leri̇n	PROPN
cuesj-900	238	8	sınıflandırılması	sınıflandırılması	PROPN
cuesj-900	238	9	.	.	PUNCT
cuesj-900	239	1	(	(	PUNCT
cuesj-900	239	2	sivas	sivas	PROPN
cuesj-900	239	3	cumhuriyet	cumhuriyet	PROPN
cuesj-900	239	4	üniversitesi	üniversitesi	PROPN
cuesj-900	239	5	,	,	PUNCT
cuesj-900	239	6	sosya	sosya	NOUN
cuesj-900	239	7	bilimler	bilimler	PROPN
cuesj-900	239	8	enstitüsü	enstitüsü	PROPN
cuesj-900	239	9	,	,	PUNCT
cuesj-900	239	10	turkey	turkey	PROPN
cuesj-900	239	11	,	,	PUNCT
cuesj-900	239	12	2020	2020	NUM
cuesj-900	239	13	.	.	PUNCT
cuesj-900	240	1	2	2	X
cuesj-900	240	2	.	.	X
cuesj-900	240	3	m.	m.	NOUN
cuesj-900	240	4	hall	hall	PROPN
cuesj-900	240	5	,	,	PUNCT
cuesj-900	240	6	e.	e.	PROPN
cuesj-900	240	7	frank	frank	PROPN
cuesj-900	240	8	,	,	PUNCT
cuesj-900	240	9	g.	g.	PROPN
cuesj-900	240	10	holmes	holmes	PROPN
cuesj-900	240	11	,	,	PUNCT
cuesj-900	240	12	b.	b.	PROPN
cuesj-900	240	13	pfahringer	pfahringer	NOUN
cuesj-900	240	14	,	,	PUNCT
cuesj-900	240	15	p.	p.	NOUN
cuesj-900	240	16	reutemann	reutemann	PROPN
cuesj-900	240	17	and	and	CCONJ
cuesj-900	240	18	i.	i.	PROPN
cuesj-900	240	19	wietten	wietten	PROPN
cuesj-900	240	20	.	.	PUNCT
cuesj-900	241	1	the	the	DET
cuesj-900	241	2	weka	weka	PROPN
cuesj-900	241	3	data	data	PROPN
cuesj-900	241	4	mining	mining	NOUN
cuesj-900	241	5	software	software	NOUN
cuesj-900	241	6	:	:	PUNCT
cuesj-900	241	7	an	an	DET
cuesj-900	241	8	update	update	NOUN
cuesj-900	241	9	.	.	PUNCT
cuesj-900	242	1	acm	acm	NOUN
cuesj-900	242	2	sigkdd	sigkdd	VERB
cuesj-900	242	3	explorations	exploration	NOUN
cuesj-900	242	4	newsletter	newsletter	NOUN
cuesj-900	242	5	,	,	PUNCT
cuesj-900	242	6	vol	vol	NOUN
cuesj-900	242	7	.	.	PROPN
cuesj-900	242	8	11	11	NUM
cuesj-900	242	9	,	,	PUNCT
cuesj-900	242	10	pp	pp	ADJ
cuesj-900	242	11	.	.	PUNCT
cuesj-900	243	1	10–18	10–18	NUM
cuesj-900	243	2	,	,	PUNCT
cuesj-900	243	3	2009	2009	NUM
cuesj-900	243	4	.	.	PUNCT
cuesj-900	244	1	3	3	X
cuesj-900	244	2	.	.	PUNCT
cuesj-900	244	3	a.	a.	NOUN
cuesj-900	244	4	a.	a.	PROPN
cuesj-900	244	5	soofi	soofi	PROPN
cuesj-900	244	6	and	and	CCONJ
cuesj-900	244	7	a.	a.	PROPN
cuesj-900	244	8	awan	awan	PROPN
cuesj-900	244	9	.	.	PUNCT
cuesj-900	245	1	classification	classification	NOUN
cuesj-900	245	2	techniques	technique	NOUN
cuesj-900	245	3	in	in	ADP
cuesj-900	245	4	machine	machine	NOUN
cuesj-900	245	5	learning	learning	NOUN
cuesj-900	245	6	:	:	PUNCT
cuesj-900	245	7	applications	application	NOUN
cuesj-900	245	8	and	and	CCONJ
cuesj-900	245	9	issues	issue	NOUN
cuesj-900	245	10	.	.	PUNCT
cuesj-900	246	1	journal	journal	NOUN
cuesj-900	246	2	of	of	ADP
cuesj-900	246	3	basic	basic	ADJ
cuesj-900	246	4	and	and	CCONJ
cuesj-900	246	5	applied	applied	ADJ
cuesj-900	246	6	sciences	science	NOUN
cuesj-900	246	7	,	,	PUNCT
cuesj-900	246	8	vol	vol	NOUN
cuesj-900	246	9	.	.	PROPN
cuesj-900	246	10	13	13	NUM
cuesj-900	246	11	,	,	PUNCT
cuesj-900	246	12	pp	pp	ADJ
cuesj-900	246	13	.	.	PUNCT
cuesj-900	247	1	459–465	459–465	NUM
cuesj-900	247	2	,	,	PUNCT
cuesj-900	247	3	2017	2017	NUM
cuesj-900	247	4	.	.	PUNCT
cuesj-900	248	1	4	4	X
cuesj-900	248	2	.	.	X
cuesj-900	248	3	v.	v.	ADP
cuesj-900	248	4	sharma	sharma	PROPN
cuesj-900	248	5	.	.	PUNCT
cuesj-900	249	1	survey	survey	NOUN
cuesj-900	249	2	of	of	ADP
cuesj-900	249	3	classification	classification	NOUN
cuesj-900	249	4	algorithms	algorithm	NOUN
cuesj-900	249	5	and	and	CCONJ
cuesj-900	249	6	various	various	ADJ
cuesj-900	249	7	model	model	NOUN
cuesj-900	249	8	selection	selection	NOUN
cuesj-900	249	9	methods	method	NOUN
cuesj-900	249	10	.	.	PUNCT
cuesj-900	250	1	journal	journal	NOUN
cuesj-900	250	2	of	of	ADP
cuesj-900	250	3	machine	machine	NOUN
cuesj-900	250	4	learning	learn	VERB
cuesj-900	250	5	research	research	NOUN
cuesj-900	250	6	,	,	PUNCT
cuesj-900	250	7	vol	vol	NOUN
cuesj-900	250	8	.	.	PROPN
cuesj-900	250	9	1	1	NUM
cuesj-900	250	10	,	,	PUNCT
cuesj-900	250	11	pp	pp	ADJ
cuesj-900	250	12	.	.	PUNCT
cuesj-900	251	1	1–48	1–48	NOUN
cuesj-900	251	2	,	,	PUNCT
cuesj-900	251	3	2000	2000	NUM
cuesj-900	251	4	.	.	PUNCT
cuesj-900	252	1	5	5	X
cuesj-900	252	2	.	.	X
cuesj-900	252	3	d.	d.	PROPN
cuesj-900	252	4	sisodia	sisodia	PROPN
cuesj-900	252	5	and	and	CCONJ
cuesj-900	252	6	d.	d.	PROPN
cuesj-900	252	7	s.	s.	PROPN
cuesj-900	252	8	sisodia	sisodia	PROPN
cuesj-900	252	9	.	.	PUNCT
cuesj-900	253	1	prediction	prediction	NOUN
cuesj-900	253	2	of	of	ADP
cuesj-900	253	3	diabetes	diabetes	NOUN
cuesj-900	253	4	using	use	VERB
cuesj-900	253	5	classification	classification	NOUN
cuesj-900	253	6	algorithms	algorithm	NOUN
cuesj-900	253	7	.	.	PUNCT
cuesj-900	254	1	procedia	procedia	NOUN
cuesj-900	254	2	computer	computer	NOUN
cuesj-900	254	3	science	science	NOUN
cuesj-900	254	4	,	,	PUNCT
cuesj-900	254	5	vol	vol	NOUN
cuesj-900	254	6	.	.	PROPN
cuesj-900	254	7	132	132	NUM
cuesj-900	254	8	,	,	PUNCT
cuesj-900	254	9	pp	pp	ADJ
cuesj-900	254	10	.	.	PUNCT
cuesj-900	255	1	1578–1585	1578–1585	NUM
cuesj-900	255	2	,	,	PUNCT
cuesj-900	255	3	2018	2018	NUM
cuesj-900	255	4	.	.	PUNCT
cuesj-900	256	1	6	6	NUM
cuesj-900	256	2	.	.	X
cuesj-900	256	3	m.	m.	NOUN
cuesj-900	256	4	ambigavathi	ambigavathi	PROPN
cuesj-900	256	5	and	and	CCONJ
cuesj-900	256	6	d.	d.	PROPN
cuesj-900	256	7	sridharan	sridharan	PROPN
cuesj-900	256	8	.	.	PUNCT
cuesj-900	257	1	analysis	analysis	NOUN
cuesj-900	257	2	of	of	ADP
cuesj-900	257	3	clustering	clustering	ADJ
cuesj-900	257	4	algorithms	algorithm	NOUN
cuesj-900	257	5	in	in	ADP
cuesj-900	257	6	machine	machine	NOUN
cuesj-900	257	7	learning	learn	VERB
cuesj-900	257	8	for	for	ADP
cuesj-900	257	9	healthcare	healthcare	NOUN
cuesj-900	257	10	data	datum	NOUN
cuesj-900	257	11	.	.	PUNCT
cuesj-900	258	1	in	in	ADP
cuesj-900	258	2	:	:	PUNCT
cuesj-900	258	3	advances	advance	NOUN
cuesj-900	258	4	in	in	ADP
cuesj-900	258	5	computing	computing	NOUN
cuesj-900	258	6	and	and	CCONJ
cuesj-900	258	7	data	datum	NOUN
cuesj-900	258	8	sciences	science	NOUN
cuesj-900	258	9	.	.	PUNCT
cuesj-900	258	10	vol	vol	NOUN
cuesj-900	258	11	.	.	PROPN
cuesj-900	258	12	1244	1244	NUM
cuesj-900	258	13	.	.	PUNCT
cuesj-900	259	1	springer	springer	NOUN
cuesj-900	259	2	,	,	PUNCT
cuesj-900	259	3	berlin	berlin	PROPN
cuesj-900	259	4	,	,	PUNCT
cuesj-900	259	5	pp	pp	ADP
cuesj-900	259	6	.	.	PUNCT
cuesj-900	260	1	117–128	117–128	NUM
cuesj-900	260	2	.	.	PUNCT
cuesj-900	261	1	7	7	X
cuesj-900	261	2	.	.	PUNCT
cuesj-900	261	3	a.	a.	PROPN
cuesj-900	261	4	c.	c.	PROPN
cuesj-900	261	5	lorena	lorena	PROPN
cuesj-900	261	6	,	,	PUNCT
cuesj-900	261	7	l.	l.	PROPN
cuesj-900	261	8	p.	p.	PROPN
cuesj-900	261	9	f.	f.	PROPN
cuesj-900	261	10	garcia	garcia	PROPN
cuesj-900	261	11	,	,	PUNCT
cuesj-900	261	12	j.	j.	PROPN
cuesj-900	261	13	lehmann	lehmann	PROPN
cuesj-900	261	14	,	,	PUNCT
cuesj-900	261	15	m.	m.	NOUN
cuesj-900	261	16	c.	c.	PROPN
cuesj-900	261	17	p.	p.	PROPN
cuesj-900	261	18	souto	souto	PROPN
cuesj-900	261	19	and	and	CCONJ
cuesj-900	261	20	t.	t.	PROPN
cuesj-900	261	21	k.	k.	PROPN
cuesj-900	261	22	ho	ho	PROPN
cuesj-900	261	23	.	.	PUNCT
cuesj-900	262	1	how	how	SCONJ
cuesj-900	262	2	complex	complex	ADJ
cuesj-900	262	3	is	be	AUX
cuesj-900	262	4	your	your	PRON
cuesj-900	262	5	classification	classification	NOUN
cuesj-900	262	6	problem	problem	NOUN
cuesj-900	262	7	?	?	PUNCT
cuesj-900	263	1	a	a	DET
cuesj-900	263	2	survey	survey	NOUN
cuesj-900	263	3	on	on	ADP
cuesj-900	263	4	measuring	measure	VERB
cuesj-900	263	5	classification	classification	NOUN
cuesj-900	263	6	complexity	complexity	NOUN
cuesj-900	263	7	.	.	PUNCT
cuesj-900	264	1	acm	acm	PROPN
cuesj-900	264	2	computing	computing	NOUN
cuesj-900	264	3	surveys	survey	NOUN
cuesj-900	264	4	,	,	PUNCT
cuesj-900	264	5	vol	vol	NOUN
cuesj-900	264	6	.	.	PROPN
cuesj-900	265	1	52	52	NUM
cuesj-900	265	2	,	,	PUNCT
cuesj-900	265	3	pp	pp	ADJ
cuesj-900	265	4	.	.	PUNCT
cuesj-900	266	1	1–34	1–34	NUM
cuesj-900	266	2	,	,	PUNCT
cuesj-900	266	3	2018	2018	NUM
cuesj-900	266	4	.	.	PUNCT
cuesj-900	267	1	8	8	X
cuesj-900	267	2	.	.	X
cuesj-900	267	3	d.	d.	PROPN
cuesj-900	267	4	liu	liu	PROPN
cuesj-900	267	5	,	,	PUNCT
cuesj-900	267	6	r.	r.	PROPN
cuesj-900	267	7	sun	sun	PROPN
cuesj-900	267	8	and	and	CCONJ
cuesj-900	267	9	h.	h.	PROPN
cuesj-900	267	10	ren	ren	PROPN
cuesj-900	267	11	.	.	PUNCT
cuesj-900	268	1	efficient	efficient	ADJ
cuesj-900	268	2	fraud	fraud	NOUN
cuesj-900	268	3	detection	detection	NOUN
cuesj-900	268	4	classification	classification	NOUN
cuesj-900	268	5	:	:	PUNCT
cuesj-900	268	6	class	class	NOUN
cuesj-900	268	7	imbalanceand	imbalanceand	NOUN
cuesj-900	268	8	attribute	attribute	NOUN
cuesj-900	268	9	correlations	correlation	NOUN
cuesj-900	268	10	.	.	PUNCT
cuesj-900	269	1	the	the	DET
cuesj-900	269	2	frontiers	frontier	NOUN
cuesj-900	269	3	of	of	ADP
cuesj-900	269	4	society	society	NOUN
cuesj-900	269	5	,	,	PUNCT
cuesj-900	269	6	science	science	NOUN
cuesj-900	269	7	and	and	CCONJ
cuesj-900	269	8	technology	technology	NOUN
cuesj-900	269	9	,	,	PUNCT
cuesj-900	269	10	vol	vol	NOUN
cuesj-900	269	11	.	.	PROPN
cuesj-900	269	12	2	2	NUM
cuesj-900	269	13	,	,	PUNCT
cuesj-900	269	14	pp	pp	ADJ
cuesj-900	269	15	.	.	PUNCT
cuesj-900	270	1	96–103	96–103	NUM
cuesj-900	270	2	,	,	PUNCT
cuesj-900	270	3	2020	2020	NUM
cuesj-900	270	4	.	.	PUNCT
cuesj-900	271	1	9	9	NUM
cuesj-900	271	2	.	.	X
cuesj-900	271	3	x.	x.	NOUN
cuesj-900	271	4	zheng	zheng	PROPN
cuesj-900	271	5	.	.	PUNCT
cuesj-900	272	1	smote	smote	VERB
cuesj-900	272	2	variants	variant	NOUN
cuesj-900	272	3	for	for	ADP
cuesj-900	272	4	imbalanced	imbalanced	ADJ
cuesj-900	272	5	binary	binary	ADJ
cuesj-900	272	6	classification	classification	NOUN
cuesj-900	272	7	:	:	PUNCT
cuesj-900	272	8	heart	heart	NOUN
cuesj-900	272	9	disease	disease	NOUN
cuesj-900	272	10	prediction	prediction	NOUN
cuesj-900	272	11	.	.	PUNCT
cuesj-900	273	1	university	university	NOUN
cuesj-900	273	2	of	of	ADP
cuesj-900	273	3	california	california	PROPN
cuesj-900	273	4	,	,	PUNCT
cuesj-900	273	5	california	california	PROPN
cuesj-900	273	6	,	,	PUNCT
cuesj-900	273	7	2020	2020	NUM
cuesj-900	273	8	.	.	PUNCT
cuesj-900	274	1	10	10	NUM
cuesj-900	274	2	.	.	PUNCT
cuesj-900	275	1	t.	t.	PROPN
cuesj-900	275	2	f.	f.	PROPN
cuesj-900	275	3	malone	malone	PROPN
cuesj-900	275	4	.	.	PUNCT
cuesj-900	276	1	application	application	NOUN
cuesj-900	276	2	of	of	ADP
cuesj-900	276	3	statistical	statistical	ADJ
cuesj-900	276	4	methods	method	NOUN
cuesj-900	276	5	in	in	ADP
cuesj-900	276	6	weather	weather	NOUN
cuesj-900	276	7	prediction	prediction	NOUN
cuesj-900	276	8	.	.	PUNCT
cuesj-900	277	1	proceedings	proceeding	NOUN
cuesj-900	277	2	of	of	ADP
cuesj-900	277	3	the	the	DET
cuesj-900	277	4	national	national	PROPN
cuesj-900	277	5	academy	academy	PROPN
cuesj-900	277	6	of	of	ADP
cuesj-900	277	7	sciences	sciences	PROPN
cuesj-900	277	8	,	,	PUNCT
cuesj-900	277	9	vol	vol	NOUN
cuesj-900	277	10	.	.	PROPN
cuesj-900	277	11	41	41	NUM
cuesj-900	277	12	,	,	PUNCT
cuesj-900	277	13	pp	pp	ADJ
cuesj-900	277	14	.	.	PUNCT
cuesj-900	278	1	806–815	806–815	NUM
cuesj-900	278	2	,	,	PUNCT
cuesj-900	278	3	1955	1955	NUM
cuesj-900	278	4	.	.	PUNCT
cuesj-900	279	1	11	11	NUM
cuesj-900	279	2	.	.	PUNCT
cuesj-900	280	1	l.	l.	PROPN
cuesj-900	280	2	liu	liu	PROPN
cuesj-900	280	3	and	and	CCONJ
cuesj-900	280	4	j.	j.	PROPN
cuesj-900	280	5	l.	l.	PROPN
cuesj-900	280	6	priestley	priestley	PROPN
cuesj-900	280	7	.	.	PUNCT
cuesj-900	281	1	a	a	DET
cuesj-900	281	2	comparison	comparison	NOUN
cuesj-900	281	3	of	of	ADP
cuesj-900	281	4	machine	machine	NOUN
cuesj-900	281	5	learning	learn	VERB
cuesj-900	281	6	algorithms	algorithm	NOUN
cuesj-900	281	7	for	for	ADP
cuesj-900	281	8	prediction	prediction	NOUN
cuesj-900	281	9	of	of	ADP
cuesj-900	281	10	past	past	ADJ
cuesj-900	281	11	due	due	ADJ
cuesj-900	281	12	service	service	NOUN
cuesj-900	281	13	in	in	ADP
cuesj-900	281	14	commercial	commercial	ADJ
cuesj-900	281	15	credit	credit	NOUN
cuesj-900	281	16	.	.	PUNCT
cuesj-900	282	1	in	in	ADP
cuesj-900	282	2	:	:	PUNCT
cuesj-900	282	3	grey	grey	PROPN
cuesj-900	282	4	literature	literature	PROPN
cuesj-900	282	5	from	from	ADP
cuesj-900	282	6	phd	phd	NOUN
cuesj-900	282	7	candidates	candidate	NOUN
cuesj-900	282	8	.	.	PUNCT
cuesj-900	283	1	digitalcommons	digitalcommons	PROPN
cuesj-900	283	2	kennesaw	kennesaw	PROPN
cuesj-900	283	3	state	state	PROPN
cuesj-900	283	4	university	university	PROPN
cuesj-900	283	5	,	,	PUNCT
cuesj-900	283	6	georgia	georgia	PROPN
cuesj-900	283	7	,	,	PUNCT
cuesj-900	283	8	2018	2018	NUM
cuesj-900	283	9	.	.	PUNCT
cuesj-900	284	1	12	12	NUM
cuesj-900	284	2	.	.	PUNCT
cuesj-900	285	1	g.	g.	PROPN
cuesj-900	285	2	nakhaeizadeh	nakhaeizadeh	PROPN
cuesj-900	285	3	and	and	CCONJ
cuesj-900	285	4	c.	c.	PROPN
cuesj-900	285	5	c.	c.	PROPN
cuesj-900	285	6	taylor	taylor	PROPN
cuesj-900	285	7	.	.	PUNCT
cuesj-900	285	8	machine	machine	NOUN
cuesj-900	285	9	learning	learning	NOUN
cuesj-900	285	10	and	and	CCONJ
cuesj-900	285	11	statistics	statistic	NOUN
cuesj-900	285	12	:	:	PUNCT
cuesj-900	285	13	the	the	DET
cuesj-900	285	14	interface	interface	NOUN
cuesj-900	285	15	.	.	PUNCT
cuesj-900	286	1	wiley	wiley	PROPN
cuesj-900	286	2	,	,	PUNCT
cuesj-900	286	3	united	united	PROPN
cuesj-900	286	4	states	states	PROPN
cuesj-900	286	5	,	,	PUNCT
cuesj-900	286	6	1997	1997	NUM
cuesj-900	286	7	.	.	PUNCT
cuesj-900	287	1	table	table	NOUN
cuesj-900	287	2	4	4	NUM
cuesj-900	287	3	:	:	PUNCT
cuesj-900	287	4	clustering	cluster	VERB
cuesj-900	287	5	method	method	NOUN
cuesj-900	287	6	performance	performance	NOUN
cuesj-900	287	7	attribute	attribute	NOUN
cuesj-900	287	8	full	full	ADJ
cuesj-900	287	9	data	datum	NOUN
cuesj-900	287	10	(	(	PUNCT
cuesj-900	287	11	1461	1461	NUM
cuesj-900	287	12	)	)	PUNCT
cuesj-900	287	13	0	0	NUM
cuesj-900	288	1	(	(	PUNCT
cuesj-900	288	2	232	232	NUM
cuesj-900	288	3	)	)	PUNCT
cuesj-900	288	4	1	1	NUM
cuesj-900	288	5	(	(	PUNCT
cuesj-900	288	6	449	449	NUM
cuesj-900	288	7	)	)	SYM
cuesj-900	288	8	2	2	NUM
cuesj-900	288	9	(	(	PUNCT
cuesj-900	288	10	355	355	NUM
cuesj-900	288	11	)	)	PUNCT
cuesj-900	288	12	3	3	NUM
cuesj-900	288	13	(	(	PUNCT
cuesj-900	288	14	258	258	NUM
cuesj-900	288	15	)	)	PUNCT
cuesj-900	288	16	4	4	NUM
cuesj-900	288	17	(	(	PUNCT
cuesj-900	288	18	167	167	NUM
cuesj-900	288	19	)	)	PUNCT
cuesj-900	288	20	precipitation	precipitation	NOUN
cuesj-900	288	21	3.02	3.02	NUM
cuesj-900	288	22	4.06	4.06	NUM
cuesj-900	288	23	7.61	7.61	NUM
cuesj-900	288	24	0.00	0.00	NUM
cuesj-900	288	25	0.00	0.00	NUM
cuesj-900	288	26	0.36	0.36	NUM
cuesj-900	288	27	temp	temp	NOUN
cuesj-900	288	28	-	-	PUNCT
cuesj-900	288	29	max	max	NOUN
cuesj-900	288	30	16.40	16.40	NUM
cuesj-900	288	31	18.90	18.90	NUM
cuesj-900	288	32	10.60	10.60	NUM
cuesj-900	288	33	26.00	26.00	NUM
cuesj-900	288	34	16.80	16.80	NUM
cuesj-900	288	35	7.52	7.52	NUM
cuesj-900	288	36	temp	temp	NOUN
cuesj-900	288	37	-	-	PUNCT
cuesj-900	288	38	min	min	NOUN
cuesj-900	288	39	8.23	8.23	NUM
cuesj-900	288	40	11.70	11.70	NUM
cuesj-900	288	41	5.34	5.34	NUM
cuesj-900	288	42	13.70	13.70	NUM
cuesj-900	288	43	7.63	7.63	NUM
cuesj-900	288	44	0.25	0.25	NUM
cuesj-900	288	45	wind	wind	NOUN
cuesj-900	288	46	3.24	3.24	NUM
cuesj-900	288	47	2.89	2.89	NUM
cuesj-900	288	48	4.06	4.06	NUM
cuesj-900	288	49	2.85	2.85	NUM
cuesj-900	288	50	3.06	3.06	NUM
cuesj-900	288	51	2.60	2.60	NUM
cuesj-900	288	52	weather	weather	NOUN
cuesj-900	288	53	rain	rain	NOUN
cuesj-900	288	54	rain	rain	NOUN
cuesj-900	288	55	rain	rain	NOUN
cuesj-900	288	56	sun	sun	PROPN
cuesj-900	288	57	sun	sun	PROPN
cuesj-900	288	58	sun	sun	PROPN
cuesj-900	288	59	table	table	NOUN
cuesj-900	288	60	5	5	NUM
cuesj-900	288	61	:	:	PUNCT
cuesj-900	288	62	comparison	comparison	NOUN
cuesj-900	288	63	result	result	NOUN
cuesj-900	288	64	of	of	ADP
cuesj-900	288	65	clustering	cluster	VERB
cuesj-900	288	66	algorithms	algorithm	NOUN
cuesj-900	288	67	with	with	ADP
cuesj-900	288	68	weather	weather	NOUN
cuesj-900	288	69	dataset	dataset	NOUN
cuesj-900	288	70	using	use	VERB
cuesj-900	288	71	weka	weka	PROPN
cuesj-900	288	72	tool	tool	PROPN
cuesj-900	288	73	hierarchical	hierarchical	ADJ
cuesj-900	288	74	k	k	ADJ
cuesj-900	288	75	-	-	ADJ
cuesj-900	288	76	mean	mean	ADJ
cuesj-900	288	77	name	name	NOUN
cuesj-900	288	78	5	5	NUM
cuesj-900	288	79	5	5	NUM
cuesj-900	288	80	no	no	NOUN
cuesj-900	288	81	of	of	ADP
cuesj-900	288	82	clusters	cluster	NOUN
cuesj-900	288	83	1461	1461	NUM
cuesj-900	288	84	1461	1461	NUM
cuesj-900	288	85	cluster	cluster	NOUN
cuesj-900	288	86	instances	instance	NOUN
cuesj-900	288	87	18	18	NUM
cuesj-900	288	88	no	no	NOUN
cuesj-900	288	89	of	of	ADP
cuesj-900	288	90	iterations	iteration	NOUN
cuesj-900	288	91	1707.637908	1707.637908	PRON
cuesj-900	288	92	root	root	NOUN
cuesj-900	288	93	mean	mean	VERB
cuesj-900	288	94	square	square	ADJ
cuesj-900	288	95	errors	error	NOUN
cuesj-900	288	96	9.9	9.9	NUM
cuesj-900	288	97	s	s	PART
cuesj-900	289	1	0.02	0.02	NUM
cuesj-900	289	2	s	s	NOUN
cuesj-900	289	3	time	time	NOUN
cuesj-900	289	4	is	be	AUX
cuesj-900	289	5	taken	take	VERB
cuesj-900	289	6	to	to	PART
cuesj-900	289	7	build	build	VERB
cuesj-900	289	8	model	model	NOUN
cuesj-900	289	9	0	0	NUM
cuesj-900	289	10	0	0	NUM
cuesj-900	289	11	unclustered	unclustere	VERB
cuesj-900	289	12	instances	instance	NOUN
cuesj-900	289	13	weka	weka	PROPN
cuesj-900	289	14	:	:	PUNCT
cuesj-900	289	15	waikato	waikato	NOUN
cuesj-900	289	16	environment	environment	NOUN
cuesj-900	289	17	for	for	ADP
cuesj-900	289	18	knowledge	knowledge	NOUN
cuesj-900	289	19	analysis	analysis	NOUN
cuesj-900	289	20	mulla	mulla	NOUN
cuesj-900	289	21	and	and	CCONJ
cuesj-900	289	22	demir	demir	PROPN
cuesj-900	289	23	:	:	PUNCT
cuesj-900	289	24	the	the	DET
cuesj-900	289	25	use	use	NOUN
cuesj-900	289	26	of	of	ADP
cuesj-900	289	27	clustering	clustering	NOUN
cuesj-900	289	28	and	and	CCONJ
cuesj-900	289	29	classification	classification	NOUN
cuesj-900	289	30	methods	method	NOUN
cuesj-900	289	31	in	in	ADP
cuesj-900	289	32	machine	machine	NOUN
cuesj-900	289	33	learning	learn	VERB
cuesj-900	289	34	59	59	NUM
cuesj-900	289	35	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-900	289	36	cuesj	cuesj	NOUN
cuesj-900	289	37	2023	2023	NUM
cuesj-900	289	38	,	,	PUNCT
cuesj-900	289	39	7	7	NUM
cuesj-900	289	40	(	(	PUNCT
cuesj-900	289	41	1	1	NUM
cuesj-900	289	42	):	):	PUNCT
cuesj-900	289	43	52	52	NUM
cuesj-900	289	44	-	-	SYM
cuesj-900	289	45	59	59	NUM
cuesj-900	289	46	13	13	NUM
cuesj-900	289	47	.	.	PUNCT
cuesj-900	290	1	g.	g.	PROPN
cuesj-900	290	2	a.	a.	PROPN
cuesj-900	290	3	a.	a.	NOUN
cuesj-900	290	4	mulla	mulla	PROPN
cuesj-900	290	5	,	,	PUNCT
cuesj-900	290	6	y.	y.	PROPN
cuesj-900	290	7	demir	demir	PROPN
cuesj-900	290	8	and	and	CCONJ
cuesj-900	290	9	m.	m.	PROPN
cuesj-900	290	10	m.	m.	PROPN
cuesj-900	290	11	hassan	hassan	PROPN
cuesj-900	290	12	.	.	PUNCT
cuesj-900	291	1	combination	combination	NOUN
cuesj-900	291	2	of	of	ADP
cuesj-900	291	3	pca	pca	PROPN
cuesj-900	291	4	with	with	ADP
cuesj-900	291	5	smote	smote	NOUN
cuesj-900	291	6	oversampling	oversample	VERB
cuesj-900	291	7	for	for	ADP
cuesj-900	291	8	classification	classification	NOUN
cuesj-900	291	9	of	of	ADP
cuesj-900	291	10	high	high	ADJ
cuesj-900	291	11	-	-	PUNCT
cuesj-900	291	12	dimensional	dimensional	ADJ
cuesj-900	291	13	imbalanced	imbalanced	ADJ
cuesj-900	291	14	data	datum	NOUN
cuesj-900	291	15	.	.	PUNCT
cuesj-900	292	1	beu	beu	PROPN
cuesj-900	292	2	journal	journal	PROPN
cuesj-900	292	3	of	of	ADP
cuesj-900	292	4	science	science	NOUN
cuesj-900	292	5	,	,	PUNCT
cuesj-900	292	6	vol	vol	NOUN
cuesj-900	292	7	.	.	PROPN
cuesj-900	292	8	10	10	NUM
cuesj-900	292	9	,	,	PUNCT
cuesj-900	292	10	pp	pp	ADJ
cuesj-900	292	11	.	.	PUNCT
cuesj-900	293	1	858–869	858–869	NUM
cuesj-900	293	2	,	,	PUNCT
cuesj-900	293	3	2021	2021	NUM
cuesj-900	293	4	.	.	PUNCT
cuesj-900	294	1	14	14	NUM
cuesj-900	294	2	.	.	PUNCT
cuesj-900	294	3	m.	m.	NOUN
cuesj-900	294	4	a.	a.	PROPN
cuesj-900	294	5	habara	habara	PROPN
cuesj-900	294	6	.	.	PUNCT
cuesj-900	295	1	credit	credit	NOUN
cuesj-900	295	2	risk	risk	NOUN
cuesj-900	295	3	modelling	modelling	NOUN
cuesj-900	295	4	in	in	ADP
cuesj-900	295	5	a	a	DET
cuesj-900	295	6	developing	develop	VERB
cuesj-900	295	7	economy	economy	NOUN
cuesj-900	295	8	:	:	PUNCT
cuesj-900	295	9	the	the	DET
cuesj-900	295	10	case	case	NOUN
cuesj-900	295	11	of	of	ADP
cuesj-900	295	12	libya	libya	PROPN
cuesj-900	295	13	.	.	PUNCT
cuesj-900	296	1	griffith	griffith	PROPN
cuesj-900	296	2	university	university	PROPN
cuesj-900	296	3	,	,	PUNCT
cuesj-900	296	4	australia	australia	PROPN
cuesj-900	296	5	,	,	PUNCT
cuesj-900	296	6	2009	2009	NUM
cuesj-900	296	7	.	.	PUNCT
cuesj-900	297	1	15	15	NUM
cuesj-900	297	2	.	.	PUNCT
cuesj-900	297	3	l.	l.	PROPN
cuesj-900	297	4	saitta	saitta	PROPN
cuesj-900	297	5	and	and	CCONJ
cuesj-900	297	6	f.	f.	PROPN
cuesj-900	297	7	neri	neri	PROPN
cuesj-900	297	8	.	.	PUNCT
cuesj-900	298	1	learning	learn	VERB
cuesj-900	298	2	in	in	ADP
cuesj-900	298	3	the	the	DET
cuesj-900	298	4	“	"	PUNCT
cuesj-900	298	5	real	real	ADJ
cuesj-900	298	6	world	world	NOUN
cuesj-900	298	7	”	"	PUNCT
cuesj-900	298	8	.	.	PUNCT
cuesj-900	299	1	machine	machine	NOUN
cuesj-900	299	2	learning	learning	PROPN
cuesj-900	299	3	,	,	PUNCT
cuesj-900	299	4	vol	vol	NOUN
cuesj-900	299	5	.	.	PROPN
cuesj-900	299	6	30	30	NUM
cuesj-900	299	7	,	,	PUNCT
cuesj-900	299	8	pp	pp	ADJ
cuesj-900	299	9	.	.	PUNCT
cuesj-900	300	1	133–163	133–163	NUM
cuesj-900	300	2	,	,	PUNCT
cuesj-900	300	3	1998	1998	NUM
cuesj-900	300	4	.	.	PUNCT
cuesj-900	301	1	16	16	NUM
cuesj-900	301	2	.	.	PUNCT
cuesj-900	302	1	l.	l.	PROPN
cuesj-900	302	2	c.	c.	PROPN
cuesj-900	302	3	thomas	thomas	PROPN
cuesj-900	302	4	.	.	PUNCT
cuesj-900	303	1	a	a	DET
cuesj-900	303	2	survey	survey	NOUN
cuesj-900	303	3	of	of	ADP
cuesj-900	303	4	credit	credit	NOUN
cuesj-900	303	5	and	and	CCONJ
cuesj-900	303	6	behavioural	behavioural	ADJ
cuesj-900	303	7	scoring	scoring	NOUN
cuesj-900	303	8	:	:	PUNCT
cuesj-900	303	9	forecasting	forecast	VERB
cuesj-900	303	10	financial	financial	ADJ
cuesj-900	303	11	risk	risk	NOUN
cuesj-900	303	12	of	of	ADP
cuesj-900	303	13	lending	lending	NOUN
cuesj-900	303	14	to	to	ADP
cuesj-900	303	15	consumers	consumer	NOUN
cuesj-900	303	16	.	.	PUNCT
cuesj-900	304	1	international	international	ADJ
cuesj-900	304	2	journal	journal	PROPN
cuesj-900	304	3	of	of	ADP
cuesj-900	304	4	forecasting	forecasting	NOUN
cuesj-900	304	5	,	,	PUNCT
cuesj-900	304	6	vol	vol	NOUN
cuesj-900	304	7	.	.	PROPN
cuesj-900	304	8	16	16	NUM
cuesj-900	304	9	,	,	PUNCT
cuesj-900	304	10	pp	pp	ADJ
cuesj-900	304	11	.	.	PUNCT
cuesj-900	305	1	149–172	149–172	NUM
cuesj-900	305	2	,	,	PUNCT
cuesj-900	305	3	2000	2000	NUM
cuesj-900	305	4	.	.	PUNCT
cuesj-900	306	1	17	17	NUM
cuesj-900	306	2	.	.	PUNCT
cuesj-900	306	3	w.	w.	PROPN
cuesj-900	306	4	e.	e.	PROPN
cuesj-900	306	5	henley	henley	PROPN
cuesj-900	306	6	and	and	CCONJ
cuesj-900	306	7	d.	d.	PROPN
cuesj-900	306	8	j.	j.	PROPN
cuesj-900	306	9	hand	hand	PROPN
cuesj-900	306	10	.	.	PUNCT
cuesj-900	307	1	a	a	DET
cuesj-900	307	2	k	k	ADJ
cuesj-900	307	3	-	-	PUNCT
cuesj-900	307	4	nearest	near	ADJ
cuesj-900	307	5	-	-	PUNCT
cuesj-900	307	6	neighbour	neighbour	NOUN
cuesj-900	307	7	classifier	classifier	NOUN
cuesj-900	307	8	for	for	ADP
cuesj-900	307	9	assessing	assess	VERB
cuesj-900	307	10	consumer	consumer	NOUN
cuesj-900	307	11	credit	credit	NOUN
cuesj-900	307	12	risk	risk	NOUN
cuesj-900	307	13	.	.	PUNCT
cuesj-900	308	1	journal	journal	NOUN
cuesj-900	308	2	of	of	ADP
cuesj-900	308	3	the	the	DET
cuesj-900	308	4	royal	royal	ADJ
cuesj-900	308	5	statistical	statistical	ADJ
cuesj-900	308	6	society	society	NOUN
cuesj-900	308	7	.	.	PUNCT
cuesj-900	309	1	series	series	PROPN
cuesj-900	309	2	d	d	PROPN
cuesj-900	309	3	,	,	PUNCT
cuesj-900	309	4	vol	vol	NOUN
cuesj-900	309	5	.	.	PROPN
cuesj-900	310	1	45	45	NUM
cuesj-900	310	2	,	,	PUNCT
cuesj-900	310	3	pp	pp	ADJ
cuesj-900	310	4	.	.	PUNCT
cuesj-900	311	1	77–95	77–95	NUM
cuesj-900	311	2	,	,	PUNCT
cuesj-900	311	3	1996	1996	NUM
cuesj-900	311	4	.	.	PUNCT
cuesj-900	312	1	18	18	NUM
cuesj-900	312	2	.	.	PUNCT
cuesj-900	312	3	j.	j.	PROPN
cuesj-900	312	4	a.	a.	PROPN
cuesj-900	312	5	k.	k.	PROPN
cuesj-900	312	6	suykens	suykens	PROPN
cuesj-900	312	7	and	and	CCONJ
cuesj-900	312	8	j.	j.	PROPN
cuesj-900	312	9	vandewalle	vandewalle	PROPN
cuesj-900	312	10	.	.	PUNCT
cuesj-900	313	1	least	least	ADJ
cuesj-900	313	2	squares	square	NOUN
cuesj-900	313	3	support	support	VERB
cuesj-900	313	4	vector	vector	NOUN
cuesj-900	313	5	machine	machine	NOUN
cuesj-900	313	6	classifiers	classifier	NOUN
cuesj-900	313	7	.	.	PUNCT
cuesj-900	314	1	neural	neural	ADJ
cuesj-900	314	2	processing	processing	NOUN
cuesj-900	314	3	letters	letter	NOUN
cuesj-900	314	4	,	,	PUNCT
cuesj-900	314	5	vol	vol	NOUN
cuesj-900	314	6	.	.	PROPN
cuesj-900	314	7	9	9	NUM
cuesj-900	314	8	,	,	PUNCT
cuesj-900	314	9	pp	pp	ADJ
cuesj-900	314	10	.	.	PUNCT
cuesj-900	315	1	293–300	293–300	NUM
cuesj-900	315	2	,	,	PUNCT
cuesj-900	315	3	1999	1999	NUM
cuesj-900	315	4	.	.	PUNCT
cuesj-900	316	1	19	19	NUM
cuesj-900	316	2	.	.	PUNCT
cuesj-900	317	1	p.	p.	NOUN
cuesj-900	317	2	nerurkar	nerurkar	PROPN
cuesj-900	317	3	,	,	PUNCT
cuesj-900	317	4	a.	a.	NOUN
cuesj-900	317	5	shirke	shirke	PROPN
cuesj-900	317	6	,	,	PUNCT
cuesj-900	317	7	m.	m.	NOUN
cuesj-900	317	8	chandane	chandane	NOUN
cuesj-900	317	9	and	and	CCONJ
cuesj-900	317	10	s.	s.	PROPN
cuesj-900	317	11	bhirud	bhirud	PROPN
cuesj-900	317	12	.	.	PUNCT
cuesj-900	318	1	empirical	empirical	ADJ
cuesj-900	318	2	analysis	analysis	NOUN
cuesj-900	318	3	of	of	ADP
cuesj-900	318	4	data	datum	NOUN
cuesj-900	318	5	clustering	cluster	VERB
cuesj-900	318	6	algorithms	algorithm	NOUN
cuesj-900	318	7	.	.	PUNCT
cuesj-900	319	1	procedia	procedia	NOUN
cuesj-900	319	2	computer	computer	NOUN
cuesj-900	319	3	science	science	NOUN
cuesj-900	319	4	,	,	PUNCT
cuesj-900	319	5	125	125	NUM
cuesj-900	319	6	,	,	PUNCT
cuesj-900	319	7	pp	pp	ADJ
cuesj-900	319	8	.	.	PUNCT
cuesj-900	320	1	770–779	770–779	NUM
cuesj-900	320	2	,	,	PUNCT
cuesj-900	320	3	2018	2018	NUM
cuesj-900	320	4	.	.	PUNCT
cuesj-900	321	1	20	20	NUM
cuesj-900	321	2	.	.	PUNCT
cuesj-900	322	1	s.	s.	PROPN
cuesj-900	322	2	b.	b.	PROPN
cuesj-900	322	3	tambe	tambe	PROPN
cuesj-900	322	4	and	and	CCONJ
cuesj-900	322	5	s.	s.	PROPN
cuesj-900	322	6	s.	s.	PROPN
cuesj-900	322	7	gajre	gajre	PROPN
cuesj-900	322	8	.	.	PUNCT
cuesj-900	323	1	cluster	cluster	NOUN
cuesj-900	323	2	-	-	PUNCT
cuesj-900	323	3	based	base	VERB
cuesj-900	323	4	real	real	ADJ
cuesj-900	323	5	-	-	PUNCT
cuesj-900	323	6	time	time	NOUN
cuesj-900	323	7	analysis	analysis	NOUN
cuesj-900	323	8	of	of	ADP
cuesj-900	323	9	mobile	mobile	ADJ
cuesj-900	323	10	healthcare	healthcare	NOUN
cuesj-900	323	11	application	application	NOUN
cuesj-900	323	12	for	for	ADP
cuesj-900	323	13	prediction	prediction	NOUN
cuesj-900	323	14	of	of	ADP
cuesj-900	323	15	physiological	physiological	ADJ
cuesj-900	323	16	data	datum	NOUN
cuesj-900	323	17	.	.	PUNCT
cuesj-900	324	1	journal	journal	PROPN
cuesj-900	324	2	of	of	ADP
cuesj-900	324	3	ambient	ambient	ADJ
cuesj-900	324	4	intelligence	intelligence	NOUN
cuesj-900	324	5	and	and	CCONJ
cuesj-900	324	6	humanized	humanize	VERB
cuesj-900	324	7	computing	computing	NOUN
cuesj-900	324	8	,	,	PUNCT
cuesj-900	324	9	vol	vol	NOUN
cuesj-900	324	10	.	.	PROPN
cuesj-900	324	11	9	9	NUM
cuesj-900	324	12	,	,	PUNCT
cuesj-900	324	13	pp	pp	ADJ
cuesj-900	324	14	.	.	PUNCT
cuesj-900	325	1	429–445	429–445	NUM
cuesj-900	325	2	,	,	PUNCT
cuesj-900	325	3	2017	2017	NUM
cuesj-900	325	4	.	.	PUNCT
cuesj-900	326	1	21	21	NUM
cuesj-900	326	2	.	.	PUNCT
cuesj-900	327	1	p.	p.	NOUN
cuesj-900	327	2	d.	d.	PROPN
cuesj-900	327	3	kumar	kumar	PROPN
cuesj-900	327	4	,	,	PUNCT
cuesj-900	327	5	t.	t.	PROPN
cuesj-900	327	6	amgoth	amgoth	PROPN
cuesj-900	327	7	and	and	CCONJ
cuesj-900	327	8	c.	c.	PROPN
cuesj-900	327	9	s.	s.	PROPN
cuesj-900	327	10	r.	r.	PROPN
cuesj-900	327	11	annavarapu	annavarapu	PROPN
cuesj-900	327	12	.	.	PUNCT
cuesj-900	328	1	machine	machine	NOUN
cuesj-900	328	2	learning	learn	VERB
cuesj-900	328	3	algorithms	algorithm	NOUN
cuesj-900	328	4	for	for	ADP
cuesj-900	328	5	wireless	wireless	ADJ
cuesj-900	328	6	sensor	sensor	NOUN
cuesj-900	328	7	networks	network	NOUN
cuesj-900	328	8	:	:	PUNCT
cuesj-900	328	9	a	a	DET
cuesj-900	328	10	survey	survey	NOUN
cuesj-900	328	11	.	.	PUNCT
cuesj-900	329	1	information	information	NOUN
cuesj-900	329	2	fusion	fusion	NOUN
cuesj-900	329	3	,	,	PUNCT
cuesj-900	329	4	vol	vol	NOUN
cuesj-900	329	5	.	.	PROPN
cuesj-900	329	6	49	49	NUM
cuesj-900	329	7	,	,	PUNCT
cuesj-900	329	8	pp	pp	ADJ
cuesj-900	329	9	.	.	PUNCT
cuesj-900	330	1	1–25	1–25	NOUN
cuesj-900	330	2	,	,	PUNCT
cuesj-900	330	3	2019	2019	NUM
cuesj-900	330	4	.	.	PUNCT
cuesj-900	331	1	22	22	NUM
cuesj-900	331	2	.	.	PUNCT
cuesj-900	332	1	data	datum	NOUN
cuesj-900	332	2	mining	mining	NOUN
cuesj-900	332	3	,	,	PUNCT
cuesj-900	332	4	machine	machine	NOUN
cuesj-900	332	5	learning	learning	NOUN
cuesj-900	332	6	and	and	CCONJ
cuesj-900	332	7	predictive	predictive	ADJ
cuesj-900	332	8	analytics	analytic	NOUN
cuesj-900	332	9	software	software	NOUN
cuesj-900	332	10	minitab	minitab	PROPN
cuesj-900	332	11	.	.	PUNCT
cuesj-900	333	1	minitab	minitab	PROPN
cuesj-900	333	2	,	,	PUNCT
cuesj-900	333	3	2020	2020	NUM
cuesj-900	333	4	.	.	PUNCT
cuesj-900	334	1	available	available	ADJ
cuesj-900	334	2	from	from	ADP
cuesj-900	334	3	:	:	PUNCT
cuesj-900	334	4	https://www.minitab	https://www.minitab	PROPN
cuesj-900	334	5	.	.	PUNCT
cuesj-900	334	6	com	com	NOUN
cuesj-900	334	7	/	/	SYM
cuesj-900	334	8	en	en	PROPN
cuesj-900	334	9	-	-	PUNCT
cuesj-900	334	10	us	we	PRON
cuesj-900	334	11	/	/	SYM
cuesj-900	334	12	products	product	NOUN
cuesj-900	334	13	/	/	SYM
cuesj-900	334	14	spm	spm	PROPN
cuesj-900	335	1	[	[	X
cuesj-900	335	2	last	last	ADV
cuesj-900	335	3	accessed	access	VERB
cuesj-900	335	4	on	on	ADP
cuesj-900	335	5	2023	2023	NUM
cuesj-900	335	6	jun	jun	PROPN
cuesj-900	335	7	07	07	NUM
cuesj-900	335	8	]	]	PUNCT
cuesj-900	335	9	.	.	PUNCT