id	sid	tid	token	lemma	pos
cet-4972	1	1	microsoft	microsoft	PROPN
cet-4972	1	2	word	word	PROPN
cet-4972	1	3	476hernandez.docx	476hernandez.docx	NUM
cet-4972	1	4	chemical	chemical	NOUN
cet-4972	1	5	engineering	engineering	NOUN
cet-4972	1	6	transactions	transaction	NOUN
cet-4972	1	7	vol	vol	NOUN
cet-4972	1	8	.	.	PROPN
cet-4972	1	9	43	43	NUM
cet-4972	1	10	,	,	PUNCT
cet-4972	1	11	2015	2015	NUM
cet-4972	1	12	a	a	DET
cet-4972	1	13	publication	publication	NOUN
cet-4972	1	14	of	of	ADP
cet-4972	1	15	the	the	DET
cet-4972	1	16	italian	italian	ADJ
cet-4972	1	17	association	association	NOUN
cet-4972	1	18	of	of	ADP
cet-4972	1	19	chemical	chemical	PROPN
cet-4972	1	20	engineering	engineering	NOUN
cet-4972	1	21	online	online	ADV
cet-4972	1	22	at	at	ADP
cet-4972	1	23	www.aidic.it/cet	www.aidic.it/cet	PROPN
cet-4972	1	24	chief	chief	ADJ
cet-4972	1	25	editors	editor	NOUN
cet-4972	1	26	:	:	PUNCT
cet-4972	1	27	sauro	sauro	PROPN
cet-4972	1	28	pierucci	pierucci	PROPN
cet-4972	1	29	,	,	PUNCT
cet-4972	1	30	jiří	jiří	NOUN
cet-4972	1	31	j.	j.	PROPN
cet-4972	1	32	klemeš	klemeš	PROPN
cet-4972	1	33	copyright	copyright	PROPN
cet-4972	1	34	©	©	PROPN
cet-4972	1	35	2015	2015	NUM
cet-4972	1	36	,	,	PUNCT
cet-4972	1	37	aidic	aidic	ADJ
cet-4972	1	38	servizi	servizi	PROPN
cet-4972	1	39	s.r.l	s.r.l	NOUN
cet-4972	1	40	.	.	PUNCT
cet-4972	1	41	,	,	PUNCT
cet-4972	1	42	isbn	isbn	PROPN
cet-4972	1	43	978	978	NUM
cet-4972	1	44	-	-	SYM
cet-4972	1	45	88	88	NUM
cet-4972	1	46	-	-	PUNCT
cet-4972	1	47	95608	95608	NUM
cet-4972	1	48	-	-	PUNCT
cet-4972	1	49	34	34	NUM
cet-4972	1	50	-	-	SYM
cet-4972	1	51	1	1	NUM
cet-4972	1	52	;	;	PUNCT
cet-4972	1	53	issn	issn	PROPN
cet-4972	1	54	2283	2283	NUM
cet-4972	1	55	-	-	SYM
cet-4972	1	56	9216	9216	NUM
cet-4972	1	57	ensemble	ensemble	ADJ
cet-4972	1	58	clustering	clustering	NOUN
cet-4972	1	59	for	for	ADP
cet-4972	1	60	fault	fault	NOUN
cet-4972	1	61	diagnosis	diagnosis	NOUN
cet-4972	1	62	in	in	ADP
cet-4972	1	63	industrial	industrial	ADJ
cet-4972	1	64	plants	plant	NOUN
cet-4972	1	65	sameer	sameer	PROPN
cet-4972	1	66	al	al	PROPN
cet-4972	1	67	-	-	PUNCT
cet-4972	1	68	dahidia	dahidia	NOUN
cet-4972	1	69	,	,	PUNCT
cet-4972	1	70	francesco	francesco	PROPN
cet-4972	1	71	di	di	PROPN
cet-4972	1	72	maioa	maioa	PROPN
cet-4972	1	73	*	*	PROPN
cet-4972	1	74	,	,	PUNCT
cet-4972	1	75	piero	piero	PROPN
cet-4972	1	76	baraldia	baraldia	PROPN
cet-4972	1	77	,	,	PUNCT
cet-4972	1	78	and	and	CCONJ
cet-4972	1	79	enrico	enrico	PROPN
cet-4972	1	80	zioa	zioa	PROPN
cet-4972	1	81	,	,	PUNCT
cet-4972	1	82	b	b	PROPN
cet-4972	1	83	aenergy	aenergy	PROPN
cet-4972	1	84	department	department	PROPN
cet-4972	1	85	,	,	PUNCT
cet-4972	1	86	politecnico	politecnico	PROPN
cet-4972	1	87	di	di	PROPN
cet-4972	1	88	milano	milano	PROPN
cet-4972	1	89	,	,	PUNCT
cet-4972	1	90	milan	milan	PROPN
cet-4972	1	91	,	,	PUNCT
cet-4972	1	92	italy	italy	PROPN
cet-4972	1	93	bchair	bchair	PROPN
cet-4972	1	94	on	on	ADP
cet-4972	1	95	systems	system	NOUN
cet-4972	1	96	science	science	NOUN
cet-4972	1	97	and	and	CCONJ
cet-4972	1	98	the	the	DET
cet-4972	1	99	energetic	energetic	ADJ
cet-4972	1	100	challenge	challenge	NOUN
cet-4972	1	101	,	,	PUNCT
cet-4972	1	102	fondation	fondation	PROPN
cet-4972	1	103	edf	edf	PROPN
cet-4972	1	104	,	,	PUNCT
cet-4972	1	105	centrale	centrale	ADJ
cet-4972	1	106	supélec	supélec	NOUN
cet-4972	1	107	,	,	PUNCT
cet-4972	1	108	paris	paris	PROPN
cet-4972	1	109	,	,	PUNCT
cet-4972	1	110	france	france	PROPN
cet-4972	1	111	francesco.dimaio@polimi.it	francesco.dimaio@polimi.it	NOUN
cet-4972	1	112	in	in	ADP
cet-4972	1	113	this	this	DET
cet-4972	1	114	paper	paper	NOUN
cet-4972	1	115	,	,	PUNCT
cet-4972	1	116	we	we	PRON
cet-4972	1	117	propose	propose	VERB
cet-4972	1	118	an	an	DET
cet-4972	1	119	unsupervised	unsupervised	ADJ
cet-4972	1	120	ensemble	ensemble	ADJ
cet-4972	1	121	clustering	clustering	NOUN
cet-4972	1	122	approach	approach	NOUN
cet-4972	1	123	for	for	ADP
cet-4972	1	124	fault	fault	NOUN
cet-4972	1	125	diagnosis	diagnosis	NOUN
cet-4972	1	126	in	in	ADP
cet-4972	1	127	industrial	industrial	ADJ
cet-4972	1	128	plants	plant	NOUN
cet-4972	1	129	.	.	PUNCT
cet-4972	2	1	the	the	DET
cet-4972	2	2	basic	basic	ADJ
cet-4972	2	3	idea	idea	NOUN
cet-4972	2	4	is	be	AUX
cet-4972	2	5	to	to	PART
cet-4972	2	6	combine	combine	VERB
cet-4972	2	7	multiple	multiple	ADJ
cet-4972	2	8	base	base	NOUN
cet-4972	2	9	clusterings	clustering	NOUN
cet-4972	2	10	of	of	ADP
cet-4972	2	11	operational	operational	ADJ
cet-4972	2	12	transients	transient	NOUN
cet-4972	2	13	of	of	ADP
cet-4972	2	14	industrial	industrial	ADJ
cet-4972	2	15	equipment	equipment	NOUN
cet-4972	2	16	,	,	PUNCT
cet-4972	2	17	when	when	SCONJ
cet-4972	2	18	the	the	DET
cet-4972	2	19	number	number	NOUN
cet-4972	2	20	of	of	ADP
cet-4972	2	21	clusters	cluster	NOUN
cet-4972	2	22	in	in	ADP
cet-4972	2	23	the	the	DET
cet-4972	2	24	final	final	ADJ
cet-4972	2	25	ensemble	ensemble	ADJ
cet-4972	2	26	clustering	clustering	NOUN
cet-4972	2	27	(	(	PUNCT
cet-4972	2	28	p	p	NOUN
cet-4972	2	29	*	*	PUNCT
cet-4972	2	30	)	)	PUNCT
cet-4972	2	31	is	be	AUX
cet-4972	2	32	unknown	unknown	ADJ
cet-4972	2	33	.	.	PUNCT
cet-4972	3	1	in	in	ADP
cet-4972	3	2	practice	practice	NOUN
cet-4972	3	3	,	,	PUNCT
cet-4972	3	4	a	a	DET
cet-4972	3	5	cluster	cluster	NOUN
cet-4972	3	6	-	-	PUNCT
cet-4972	3	7	based	base	VERB
cet-4972	3	8	similarity	similarity	NOUN
cet-4972	3	9	partitioning	partition	VERB
cet-4972	3	10	algorithm	algorithm	NOUN
cet-4972	3	11	(	(	PUNCT
cet-4972	3	12	cspa	cspa	NOUN
cet-4972	3	13	)	)	PUNCT
cet-4972	3	14	is	be	AUX
cet-4972	3	15	employed	employ	VERB
cet-4972	3	16	to	to	PART
cet-4972	3	17	quantify	quantify	VERB
cet-4972	3	18	the	the	DET
cet-4972	3	19	co	co	NOUN
cet-4972	3	20	-	-	NOUN
cet-4972	3	21	association	association	NOUN
cet-4972	3	22	matrix	matrix	NOUN
cet-4972	3	23	that	that	PRON
cet-4972	3	24	describes	describe	VERB
cet-4972	3	25	the	the	DET
cet-4972	3	26	similarity	similarity	NOUN
cet-4972	3	27	among	among	ADP
cet-4972	3	28	the	the	DET
cet-4972	3	29	different	different	ADJ
cet-4972	3	30	base	base	NOUN
cet-4972	3	31	clusterings	clustering	NOUN
cet-4972	3	32	and	and	CCONJ
cet-4972	3	33	,	,	PUNCT
cet-4972	3	34	then	then	ADV
cet-4972	3	35	,	,	PUNCT
cet-4972	3	36	a	a	DET
cet-4972	3	37	spectral	spectral	ADJ
cet-4972	3	38	clustering	clustering	ADJ
cet-4972	3	39	technique	technique	NOUN
cet-4972	3	40	embedding	embed	VERB
cet-4972	3	41	an	an	DET
cet-4972	3	42	unsupervised	unsupervised	ADJ
cet-4972	3	43	k	k	ADJ
cet-4972	3	44	-	-	PUNCT
cet-4972	3	45	means	means	NOUN
cet-4972	3	46	algorithm	algorithm	NOUN
cet-4972	3	47	is	be	AUX
cet-4972	3	48	used	use	VERB
cet-4972	3	49	to	to	PART
cet-4972	3	50	find	find	VERB
cet-4972	3	51	the	the	DET
cet-4972	3	52	optimum	optimum	ADJ
cet-4972	3	53	number	number	NOUN
cet-4972	3	54	of	of	ADP
cet-4972	3	55	clusters	cluster	NOUN
cet-4972	3	56	of	of	ADP
cet-4972	3	57	p	p	X
cet-4972	3	58	*	*	PUNCT
cet-4972	3	59	based	base	VERB
cet-4972	3	60	on	on	ADP
cet-4972	3	61	silhouette	silhouette	NOUN
cet-4972	3	62	validity	validity	NOUN
cet-4972	3	63	index	index	NOUN
cet-4972	3	64	calculation	calculation	NOUN
cet-4972	3	65	.	.	PUNCT
cet-4972	4	1	the	the	DET
cet-4972	4	2	identified	identify	VERB
cet-4972	4	3	clusters	cluster	NOUN
cet-4972	4	4	allow	allow	VERB
cet-4972	4	5	distinguishing	distinguish	VERB
cet-4972	4	6	different	different	ADJ
cet-4972	4	7	operational	operational	ADJ
cet-4972	4	8	behaviors	behavior	NOUN
cet-4972	4	9	of	of	ADP
cet-4972	4	10	the	the	DET
cet-4972	4	11	equipment	equipment	NOUN
cet-4972	4	12	.	.	PUNCT
cet-4972	5	1	the	the	DET
cet-4972	5	2	proposed	propose	VERB
cet-4972	5	3	approach	approach	NOUN
cet-4972	5	4	is	be	AUX
cet-4972	5	5	verified	verify	VERB
cet-4972	5	6	with	with	ADP
cet-4972	5	7	respect	respect	NOUN
cet-4972	5	8	to	to	ADP
cet-4972	5	9	an	an	DET
cet-4972	5	10	artificial	artificial	ADJ
cet-4972	5	11	case	case	NOUN
cet-4972	5	12	study	study	NOUN
cet-4972	5	13	representative	representative	NOUN
cet-4972	5	14	of	of	ADP
cet-4972	5	15	the	the	DET
cet-4972	5	16	signal	signal	ADJ
cet-4972	5	17	trend	trend	NOUN
cet-4972	5	18	behavior	behavior	NOUN
cet-4972	5	19	of	of	ADP
cet-4972	5	20	an	an	DET
cet-4972	5	21	industrial	industrial	ADJ
cet-4972	5	22	equipment	equipment	NOUN
cet-4972	5	23	during	during	ADP
cet-4972	5	24	shut	shut	NOUN
cet-4972	5	25	-	-	PUNCT
cet-4972	5	26	down	down	ADP
cet-4972	5	27	operations	operation	NOUN
cet-4972	5	28	.	.	PUNCT
cet-4972	6	1	the	the	DET
cet-4972	6	2	obtained	obtain	VERB
cet-4972	6	3	results	result	NOUN
cet-4972	6	4	have	have	AUX
cet-4972	6	5	been	be	AUX
cet-4972	6	6	compared	compare	VERB
cet-4972	6	7	with	with	ADP
cet-4972	6	8	those	those	PRON
cet-4972	6	9	achieved	achieve	VERB
cet-4972	6	10	by	by	ADP
cet-4972	6	11	a	a	DET
cet-4972	6	12	state	state	NOUN
cet-4972	6	13	-	-	PUNCT
cet-4972	6	14	of	of	ADP
cet-4972	6	15	-	-	PUNCT
cet-4972	6	16	art	art	NOUN
cet-4972	6	17	approach	approach	NOUN
cet-4972	6	18	,	,	PUNCT
cet-4972	6	19	known	know	VERB
cet-4972	6	20	as	as	ADP
cet-4972	6	21	cluster	cluster	NOUN
cet-4972	6	22	-	-	PUNCT
cet-4972	6	23	based	base	VERB
cet-4972	6	24	similarity	similarity	NOUN
cet-4972	6	25	partitioning	partitioning	NOUN
cet-4972	6	26	and	and	CCONJ
cet-4972	6	27	serial	serial	ADJ
cet-4972	6	28	graph	graph	NOUN
cet-4972	6	29	partitioning	partitioning	NOUN
cet-4972	6	30	and	and	CCONJ
cet-4972	6	31	fill	fill	NOUN
cet-4972	6	32	-	-	PUNCT
cet-4972	6	33	reducing	reduce	VERB
cet-4972	6	34	matrix	matrix	NOUN
cet-4972	6	35	ordering	ordering	NOUN
cet-4972	6	36	algorithms	algorithm	NOUN
cet-4972	6	37	(	(	PUNCT
cet-4972	6	38	cspa	cspa	NOUN
cet-4972	6	39	-	-	PUNCT
cet-4972	6	40	metis	metis	NOUN
cet-4972	6	41	):	):	PUNCT
cet-4972	6	42	the	the	DET
cet-4972	6	43	results	result	NOUN
cet-4972	6	44	show	show	VERB
cet-4972	6	45	that	that	SCONJ
cet-4972	6	46	the	the	DET
cet-4972	6	47	novel	novel	ADJ
cet-4972	6	48	approach	approach	NOUN
cet-4972	6	49	is	be	AUX
cet-4972	6	50	able	able	ADJ
cet-4972	6	51	to	to	PART
cet-4972	6	52	identify	identify	VERB
cet-4972	6	53	the	the	DET
cet-4972	6	54	final	final	ADJ
cet-4972	6	55	ensemble	ensemble	ADJ
cet-4972	6	56	clustering	clustering	NOUN
cet-4972	6	57	with	with	ADP
cet-4972	6	58	a	a	DET
cet-4972	6	59	lower	low	ADJ
cet-4972	6	60	misclassification	misclassification	NOUN
cet-4972	6	61	rate	rate	NOUN
cet-4972	6	62	than	than	ADP
cet-4972	6	63	the	the	DET
cet-4972	6	64	cspa	cspa	NOUN
cet-4972	6	65	-	-	PUNCT
cet-4972	6	66	metis	metis	NOUN
cet-4972	6	67	approach	approach	NOUN
cet-4972	6	68	.	.	PUNCT
cet-4972	7	1	1	1	X
cet-4972	7	2	.	.	X
cet-4972	7	3	introduction	introduction	NOUN
cet-4972	7	4	in	in	ADP
cet-4972	7	5	industries	industry	NOUN
cet-4972	7	6	such	such	ADJ
cet-4972	7	7	as	as	ADP
cet-4972	7	8	chemical	chemical	NOUN
cet-4972	7	9	,	,	PUNCT
cet-4972	7	10	oil	oil	NOUN
cet-4972	7	11	and	and	CCONJ
cet-4972	7	12	gas	gas	NOUN
cet-4972	7	13	,	,	PUNCT
cet-4972	7	14	and	and	CCONJ
cet-4972	7	15	nuclear	nuclear	ADJ
cet-4972	7	16	,	,	PUNCT
cet-4972	7	17	equipment	equipment	NOUN
cet-4972	7	18	is	be	AUX
cet-4972	7	19	subjected	subject	VERB
cet-4972	7	20	to	to	ADP
cet-4972	7	21	several	several	ADJ
cet-4972	7	22	causes	cause	NOUN
cet-4972	7	23	of	of	ADP
cet-4972	7	24	performance	performance	NOUN
cet-4972	7	25	degradation	degradation	NOUN
cet-4972	7	26	(	(	PUNCT
cet-4972	7	27	e.g.	e.g.	ADV
cet-4972	7	28	,	,	PUNCT
cet-4972	7	29	presence	presence	NOUN
cet-4972	7	30	of	of	ADP
cet-4972	7	31	manufacturing	manufacturing	NOUN
cet-4972	7	32	defects	defect	NOUN
cet-4972	7	33	,	,	PUNCT
cet-4972	7	34	wear	wear	VERB
cet-4972	7	35	and	and	CCONJ
cet-4972	7	36	tear	tear	ADJ
cet-4972	7	37	)	)	PUNCT
cet-4972	7	38	that	that	PRON
cet-4972	7	39	lead	lead	VERB
cet-4972	7	40	systems	system	NOUN
cet-4972	7	41	to	to	PART
cet-4972	7	42	work	work	VERB
cet-4972	7	43	in	in	ADP
cet-4972	7	44	anomalous	anomalous	ADJ
cet-4972	7	45	conditions	condition	NOUN
cet-4972	7	46	(	(	PUNCT
cet-4972	7	47	baraldi	baraldi	NOUN
cet-4972	7	48	et	et	NOUN
cet-4972	7	49	al	al	PROPN
cet-4972	7	50	.	.	PROPN
cet-4972	7	51	2013a	2013a	NUM
cet-4972	7	52	)	)	PUNCT
cet-4972	7	53	.	.	PUNCT
cet-4972	8	1	capturing	capture	VERB
cet-4972	8	2	the	the	DET
cet-4972	8	3	different	different	ADJ
cet-4972	8	4	operational	operational	ADJ
cet-4972	8	5	conditions	condition	NOUN
cet-4972	8	6	of	of	ADP
cet-4972	8	7	this	this	DET
cet-4972	8	8	equipment	equipment	NOUN
cet-4972	8	9	,	,	PUNCT
cet-4972	8	10	detecting	detect	VERB
cet-4972	8	11	the	the	DET
cet-4972	8	12	onset	onset	NOUN
cet-4972	8	13	of	of	ADP
cet-4972	8	14	abnormal	abnormal	ADJ
cet-4972	8	15	conditions	condition	NOUN
cet-4972	8	16	and	and	CCONJ
cet-4972	8	17	classifying	classify	VERB
cet-4972	8	18	them	they	PRON
cet-4972	8	19	in	in	ADP
cet-4972	8	20	different	different	ADJ
cet-4972	8	21	types	type	NOUN
cet-4972	8	22	can	can	AUX
cet-4972	8	23	aid	aid	VERB
cet-4972	8	24	the	the	DET
cet-4972	8	25	decision	decision	NOUN
cet-4972	8	26	maker	maker	NOUN
cet-4972	8	27	to	to	PART
cet-4972	8	28	decide	decide	VERB
cet-4972	8	29	a	a	DET
cet-4972	8	30	proper	proper	ADJ
cet-4972	8	31	maintenance	maintenance	NOUN
cet-4972	8	32	intervention	intervention	NOUN
cet-4972	8	33	and	and	CCONJ
cet-4972	8	34	,	,	PUNCT
cet-4972	8	35	hence	hence	ADV
cet-4972	8	36	,	,	PUNCT
cet-4972	8	37	increase	increase	VERB
cet-4972	8	38	equipment	equipment	NOUN
cet-4972	8	39	availability	availability	NOUN
cet-4972	8	40	and	and	CCONJ
cet-4972	8	41	system	system	NOUN
cet-4972	8	42	safety	safety	NOUN
cet-4972	8	43	,	,	PUNCT
cet-4972	8	44	while	while	SCONJ
cet-4972	8	45	reducing	reduce	VERB
cet-4972	8	46	overall	overall	ADJ
cet-4972	8	47	corrective	corrective	ADJ
cet-4972	8	48	maintenance	maintenance	NOUN
cet-4972	8	49	costs	cost	NOUN
cet-4972	8	50	(	(	PUNCT
cet-4972	8	51	piccinini	piccinini	NOUN
cet-4972	8	52	and	and	CCONJ
cet-4972	8	53	demichela	demichela	NOUN
cet-4972	8	54	,	,	PUNCT
cet-4972	8	55	2008	2008	NUM
cet-4972	8	56	;	;	PUNCT
cet-4972	8	57	al	al	PROPN
cet-4972	8	58	-	-	PUNCT
cet-4972	8	59	dahidi	dahidi	PROPN
cet-4972	8	60	et	et	PROPN
cet-4972	8	61	al	al	PROPN
cet-4972	8	62	.	.	PROPN
cet-4972	8	63	2014	2014	NUM
cet-4972	8	64	;	;	PUNCT
cet-4972	8	65	demichela	demichela	PROPN
cet-4972	8	66	and	and	CCONJ
cet-4972	8	67	camuncoli	camuncoli	NOUN
cet-4972	8	68	,	,	PUNCT
cet-4972	8	69	2014	2014	NUM
cet-4972	8	70	)	)	PUNCT
cet-4972	8	71	.	.	PUNCT
cet-4972	9	1	fault	fault	VERB
cet-4972	9	2	diagnosis	diagnosis	NOUN
cet-4972	9	3	aims	aim	VERB
cet-4972	9	4	at	at	ADP
cet-4972	9	5	partitioning	partition	VERB
cet-4972	9	6	the	the	DET
cet-4972	9	7	collected	collect	VERB
cet-4972	9	8	data	data	NOUN
cet-4972	9	9	representative	representative	NOUN
cet-4972	9	10	of	of	ADP
cet-4972	9	11	different	different	ADJ
cet-4972	9	12	operational	operational	ADJ
cet-4972	9	13	conditions	condition	NOUN
cet-4972	9	14	of	of	ADP
cet-4972	9	15	the	the	DET
cet-4972	9	16	equipment	equipment	NOUN
cet-4972	9	17	into	into	ADP
cet-4972	9	18	dissimilar	dissimilar	ADJ
cet-4972	9	19	groups	group	NOUN
cet-4972	9	20	(	(	PUNCT
cet-4972	9	21	whose	whose	DET
cet-4972	9	22	number	number	NOUN
cet-4972	9	23	may	may	AUX
cet-4972	9	24	be	be	AUX
cet-4972	9	25	“	"	PUNCT
cet-4972	9	26	a	a	DET
cet-4972	9	27	priori	priori	ADJ
cet-4972	9	28	”	"	PUNCT
cet-4972	9	29	unknown	unknown	ADJ
cet-4972	9	30	)	)	PUNCT
cet-4972	9	31	such	such	ADJ
cet-4972	9	32	that	that	SCONJ
cet-4972	9	33	data	datum	NOUN
cet-4972	9	34	belonging	belong	VERB
cet-4972	9	35	to	to	ADP
cet-4972	9	36	the	the	DET
cet-4972	9	37	same	same	ADJ
cet-4972	9	38	group	group	NOUN
cet-4972	9	39	are	be	AUX
cet-4972	9	40	more	more	ADV
cet-4972	9	41	similar	similar	ADJ
cet-4972	9	42	than	than	ADP
cet-4972	9	43	those	those	PRON
cet-4972	9	44	belonging	belong	VERB
cet-4972	9	45	to	to	ADP
cet-4972	9	46	the	the	DET
cet-4972	9	47	other	other	ADJ
cet-4972	9	48	groups	group	NOUN
cet-4972	9	49	.	.	PUNCT
cet-4972	10	1	once	once	SCONJ
cet-4972	10	2	the	the	DET
cet-4972	10	3	groups	group	NOUN
cet-4972	10	4	are	be	AUX
cet-4972	10	5	identified	identify	VERB
cet-4972	10	6	,	,	PUNCT
cet-4972	10	7	one	one	PRON
cet-4972	10	8	can	can	AUX
cet-4972	10	9	distinguish	distinguish	VERB
cet-4972	10	10	,	,	PUNCT
cet-4972	10	11	among	among	ADP
cet-4972	10	12	these	these	PRON
cet-4972	10	13	,	,	PUNCT
cet-4972	10	14	anomalous	anomalous	ADJ
cet-4972	10	15	behaviors	behavior	NOUN
cet-4972	10	16	of	of	ADP
cet-4972	10	17	the	the	DET
cet-4972	10	18	equipment	equipment	NOUN
cet-4972	10	19	(	(	PUNCT
cet-4972	10	20	baraldi	baraldi	NOUN
cet-4972	10	21	et	et	NOUN
cet-4972	10	22	al	al	PROPN
cet-4972	10	23	.	.	PROPN
cet-4972	10	24	2013a	2013a	NUM
cet-4972	10	25	)	)	PUNCT
cet-4972	10	26	.	.	PUNCT
cet-4972	11	1	in	in	ADP
cet-4972	11	2	this	this	DET
cet-4972	11	3	paper	paper	NOUN
cet-4972	11	4	,	,	PUNCT
cet-4972	11	5	we	we	PRON
cet-4972	11	6	consider	consider	VERB
cet-4972	11	7	the	the	DET
cet-4972	11	8	practical	practical	ADJ
cet-4972	11	9	case	case	NOUN
cet-4972	11	10	in	in	ADP
cet-4972	11	11	which	which	PRON
cet-4972	11	12	the	the	DET
cet-4972	11	13	number	number	NOUN
cet-4972	11	14	of	of	ADP
cet-4972	11	15	groups	group	NOUN
cet-4972	11	16	is	be	AUX
cet-4972	11	17	a	a	DET
cet-4972	11	18	priori	priori	ADJ
cet-4972	11	19	unknown	unknown	ADJ
cet-4972	11	20	and	and	CCONJ
cet-4972	11	21	formulate	formulate	VERB
cet-4972	11	22	the	the	DET
cet-4972	11	23	problem	problem	NOUN
cet-4972	11	24	as	as	ADP
cet-4972	11	25	an	an	DET
cet-4972	11	26	unsupervised	unsupervised	ADJ
cet-4972	11	27	classification	classification	NOUN
cet-4972	11	28	problem	problem	NOUN
cet-4972	11	29	aimed	aim	VERB
cet-4972	11	30	at	at	ADP
cet-4972	11	31	partitioning	partition	VERB
cet-4972	11	32	the	the	DET
cet-4972	11	33	data	datum	NOUN
cet-4972	11	34	into	into	ADP
cet-4972	11	35	homogeneous	homogeneous	ADJ
cet-4972	11	36	clusters	cluster	NOUN
cet-4972	11	37	so	so	SCONJ
cet-4972	11	38	that	that	SCONJ
cet-4972	11	39	those	those	DET
cet-4972	11	40	data	datum	NOUN
cet-4972	11	41	belonging	belong	VERB
cet-4972	11	42	to	to	ADP
cet-4972	11	43	the	the	DET
cet-4972	11	44	same	same	ADJ
cet-4972	11	45	cluster	cluster	NOUN
cet-4972	11	46	are	be	AUX
cet-4972	11	47	very	very	ADV
cet-4972	11	48	similar	similar	ADJ
cet-4972	11	49	to	to	ADP
cet-4972	11	50	each	each	DET
cet-4972	11	51	other	other	ADJ
cet-4972	11	52	and	and	CCONJ
cet-4972	11	53	dissimilar	dissimilar	ADJ
cet-4972	11	54	to	to	ADP
cet-4972	11	55	those	those	PRON
cet-4972	11	56	of	of	ADP
cet-4972	11	57	the	the	DET
cet-4972	11	58	other	other	ADJ
cet-4972	11	59	clusters	cluster	NOUN
cet-4972	11	60	(	(	PUNCT
cet-4972	11	61	salvador	salvador	NOUN
cet-4972	11	62	,	,	PUNCT
cet-4972	11	63	2002	2002	NUM
cet-4972	11	64	)	)	PUNCT
cet-4972	11	65	.	.	PUNCT
cet-4972	12	1	several	several	ADJ
cet-4972	12	2	clustering	clustering	ADJ
cet-4972	12	3	algorithms	algorithm	NOUN
cet-4972	12	4	have	have	AUX
cet-4972	12	5	been	be	AUX
cet-4972	12	6	proposed	propose	VERB
cet-4972	12	7	and	and	CCONJ
cet-4972	12	8	practically	practically	ADV
cet-4972	12	9	used	used	ADJ
cet-4972	12	10	to	to	PART
cet-4972	12	11	solve	solve	VERB
cet-4972	12	12	unsupervised	unsupervised	ADJ
cet-4972	12	13	classification	classification	NOUN
cet-4972	12	14	problems	problem	NOUN
cet-4972	12	15	,	,	PUNCT
cet-4972	12	16	for	for	ADP
cet-4972	12	17	example	example	NOUN
cet-4972	13	1	k	k	X
cet-4972	13	2	-	-	PUNCT
cet-4972	13	3	means	means	PROPN
cet-4972	13	4	(	(	PUNCT
cet-4972	13	5	liao	liao	NOUN
cet-4972	13	6	and	and	CCONJ
cet-4972	13	7	bolt	bolt	NOUN
cet-4972	13	8	,	,	PUNCT
cet-4972	13	9	2002	2002	NUM
cet-4972	13	10	)	)	PUNCT
cet-4972	13	11	,	,	PUNCT
cet-4972	13	12	self	self	NOUN
cet-4972	13	13	-	-	PUNCT
cet-4972	13	14	organizing	organize	VERB
cet-4972	13	15	maps	map	NOUN
cet-4972	13	16	(	(	PUNCT
cet-4972	13	17	som	som	NOUN
cet-4972	13	18	)	)	PUNCT
cet-4972	13	19	(	(	PUNCT
cet-4972	13	20	al	al	PROPN
cet-4972	13	21	-	-	PUNCT
cet-4972	13	22	dahidi	dahidi	NOUN
cet-4972	13	23	,	,	PUNCT
cet-4972	13	24	2014	2014	NUM
cet-4972	13	25	)	)	PUNCT
cet-4972	13	26	,	,	PUNCT
cet-4972	13	27	fuzzy	fuzzy	ADJ
cet-4972	13	28	c	c	NOUN
cet-4972	13	29	-	-	PUNCT
cet-4972	13	30	means	means	NOUN
cet-4972	13	31	(	(	PUNCT
cet-4972	13	32	fcm	fcm	PROPN
cet-4972	13	33	)	)	PUNCT
cet-4972	13	34	(	(	PUNCT
cet-4972	13	35	di	di	INTJ
cet-4972	13	36	maio	maio	PROPN
cet-4972	13	37	et	et	PROPN
cet-4972	13	38	al	al	PROPN
cet-4972	13	39	.	.	PROPN
cet-4972	13	40	2011	2011	NUM
cet-4972	13	41	;	;	PUNCT
cet-4972	13	42	di	di	PROPN
cet-4972	13	43	maio	maio	PROPN
cet-4972	13	44	et	et	PROPN
cet-4972	13	45	al	al	PROPN
cet-4972	13	46	.	.	PROPN
cet-4972	13	47	2012	2012	NUM
cet-4972	13	48	;	;	PUNCT
cet-4972	13	49	baraldi	baraldi	PROPN
cet-4972	13	50	et	et	PROPN
cet-4972	13	51	al	al	PROPN
cet-4972	13	52	.	.	PROPN
cet-4972	13	53	2013a	2013a	NUM
cet-4972	13	54	)	)	PUNCT
cet-4972	13	55	,	,	PUNCT
cet-4972	13	56	classification	classification	NOUN
cet-4972	13	57	tree	tree	NOUN
cet-4972	13	58	(	(	PUNCT
cet-4972	13	59	baraldi	baraldi	NOUN
cet-4972	13	60	et	et	PROPN
cet-4972	13	61	al	al	PROPN
cet-4972	13	62	.	.	PROPN
cet-4972	13	63	2012	2012	NUM
cet-4972	13	64	)	)	PUNCT
cet-4972	13	65	and	and	CCONJ
cet-4972	13	66	spectral	spectral	ADJ
cet-4972	13	67	clustering	clustering	NOUN
cet-4972	13	68	(	(	PUNCT
cet-4972	13	69	von	von	PROPN
cet-4972	13	70	luxburg	luxburg	PROPN
cet-4972	13	71	,	,	PUNCT
cet-4972	13	72	2007	2007	NUM
cet-4972	13	73	;	;	PUNCT
cet-4972	13	74	baraldi	baraldi	PROPN
cet-4972	13	75	et	et	PROPN
cet-4972	13	76	al	al	PROPN
cet-4972	13	77	.	.	PROPN
cet-4972	13	78	2013a	2013a	NUM
cet-4972	13	79	;	;	PUNCT
cet-4972	13	80	baraldi	baraldi	PROPN
cet-4972	13	81	et	et	PROPN
cet-4972	13	82	al	al	PROPN
cet-4972	13	83	.	.	PROPN
cet-4972	13	84	2013b	2013b	NUM
cet-4972	13	85	;	;	PUNCT
cet-4972	13	86	baraldi	baraldi	PROPN
cet-4972	13	87	et	et	PROPN
cet-4972	13	88	al	al	PROPN
cet-4972	13	89	.	.	PROPN
cet-4972	13	90	2014	2014	NUM
cet-4972	13	91	)	)	PUNCT
cet-4972	13	92	.	.	PUNCT
cet-4972	14	1	however	however	ADV
cet-4972	14	2	,	,	PUNCT
cet-4972	14	3	there	there	PRON
cet-4972	14	4	is	be	VERB
cet-4972	14	5	no	no	DET
cet-4972	14	6	unique	unique	ADJ
cet-4972	14	7	clustering	clustering	ADJ
cet-4972	14	8	algorithm	algorithm	NOUN
cet-4972	14	9	capable	capable	ADJ
cet-4972	14	10	of	of	ADP
cet-4972	14	11	correctly	correctly	ADV
cet-4972	14	12	identifying	identify	VERB
cet-4972	14	13	the	the	DET
cet-4972	14	14	underlying	underlie	VERB
cet-4972	14	15	structure	structure	NOUN
cet-4972	14	16	of	of	ADP
cet-4972	14	17	any	any	DET
cet-4972	14	18	kind	kind	NOUN
cet-4972	14	19	of	of	ADP
cet-4972	14	20	dataset	dataset	NOUN
cet-4972	14	21	.	.	PUNCT
cet-4972	15	1	even	even	ADV
cet-4972	15	2	the	the	DET
cet-4972	15	3	application	application	NOUN
cet-4972	15	4	of	of	ADP
cet-4972	15	5	different	different	ADJ
cet-4972	15	6	clustering	clustering	ADJ
cet-4972	15	7	algorithms	algorithm	NOUN
cet-4972	15	8	to	to	ADP
cet-4972	15	9	the	the	DET
cet-4972	15	10	same	same	ADJ
cet-4972	15	11	set	set	NOUN
cet-4972	15	12	of	of	ADP
cet-4972	15	13	data	datum	NOUN
cet-4972	15	14	,	,	PUNCT
cet-4972	15	15	or	or	CCONJ
cet-4972	15	16	of	of	ADP
cet-4972	15	17	the	the	DET
cet-4972	15	18	same	same	ADJ
cet-4972	15	19	algorithm	algorithm	NOUN
cet-4972	15	20	with	with	ADP
cet-4972	15	21	different	different	ADJ
cet-4972	15	22	parameter	parameter	NOUN
cet-4972	15	23	settings	setting	NOUN
cet-4972	15	24	,	,	PUNCT
cet-4972	15	25	leads	lead	VERB
cet-4972	15	26	to	to	ADP
cet-4972	15	27	different	different	ADJ
cet-4972	15	28	clustering	clustering	NOUN
cet-4972	15	29	results	result	NOUN
cet-4972	15	30	(	(	PUNCT
cet-4972	15	31	fred	fred	NOUN
cet-4972	15	32	and	and	CCONJ
cet-4972	15	33	jain	jain	PROPN
cet-4972	15	34	,	,	PUNCT
cet-4972	15	35	2005	2005	NUM
cet-4972	15	36	;	;	PUNCT
cet-4972	15	37	fern	fern	NOUN
cet-4972	15	38	and	and	CCONJ
cet-4972	15	39	lin	lin	PROPN
cet-4972	15	40	,	,	PUNCT
cet-4972	15	41	2008	2008	NUM
cet-4972	15	42	;	;	PUNCT
cet-4972	15	43	vega	vega	PROPN
cet-4972	15	44	-	-	PUNCT
cet-4972	15	45	pons	pon	NOUN
cet-4972	15	46	and	and	CCONJ
cet-4972	15	47	ruiz	ruiz	NOUN
cet-4972	15	48	-	-	PUNCT
cet-4972	15	49	shulcloper	shulcloper	NOUN
cet-4972	15	50	,	,	PUNCT
cet-4972	15	51	2011	2011	NUM
cet-4972	15	52	)	)	PUNCT
cet-4972	15	53	.	.	PUNCT
cet-4972	16	1	without	without	ADP
cet-4972	16	2	any	any	DET
cet-4972	16	3	prior	prior	ADJ
cet-4972	16	4	knowledge	knowledge	NOUN
cet-4972	16	5	on	on	ADP
cet-4972	16	6	the	the	DET
cet-4972	16	7	doi	doi	NOUN
cet-4972	16	8	:	:	PUNCT
cet-4972	16	9	10.3303	10.3303	NUM
cet-4972	16	10	/	/	SYM
cet-4972	16	11	cet1543205	cet1543205	NOUN
cet-4972	16	12	please	please	INTJ
cet-4972	16	13	cite	cite	VERB
cet-4972	16	14	this	this	DET
cet-4972	16	15	article	article	NOUN
cet-4972	16	16	as	as	ADP
cet-4972	16	17	:	:	PUNCT
cet-4972	16	18	al	al	PROPN
cet-4972	16	19	-	-	PUNCT
cet-4972	16	20	dahidi	dahidi	PROPN
cet-4972	16	21	s.	s.	PROPN
cet-4972	16	22	,	,	PUNCT
cet-4972	16	23	di	di	PROPN
cet-4972	16	24	maio	maio	PROPN
cet-4972	16	25	f.	f.	PROPN
cet-4972	16	26	,	,	PUNCT
cet-4972	16	27	baraldi	baraldi	PROPN
cet-4972	16	28	p.	p.	PROPN
cet-4972	16	29	,	,	PUNCT
cet-4972	16	30	zio	zio	PROPN
cet-4972	16	31	e.	e.	PROPN
cet-4972	16	32	,	,	PUNCT
cet-4972	16	33	2015	2015	NUM
cet-4972	16	34	,	,	PUNCT
cet-4972	16	35	ensemble	ensemble	ADJ
cet-4972	16	36	clustering	clustering	NOUN
cet-4972	16	37	for	for	ADP
cet-4972	16	38	fault	fault	NOUN
cet-4972	16	39	diagnosis	diagnosis	NOUN
cet-4972	16	40	in	in	ADP
cet-4972	16	41	industrial	industrial	ADJ
cet-4972	16	42	plants	plant	NOUN
cet-4972	16	43	,	,	PUNCT
cet-4972	16	44	chemical	chemical	NOUN
cet-4972	16	45	engineering	engineering	NOUN
cet-4972	16	46	transactions	transaction	NOUN
cet-4972	16	47	,	,	PUNCT
cet-4972	16	48	43	43	NUM
cet-4972	16	49	,	,	PUNCT
cet-4972	16	50	1225	1225	NUM
cet-4972	16	51	-	-	SYM
cet-4972	16	52	1230	1230	NUM
cet-4972	16	53	doi	doi	NOUN
cet-4972	16	54	:	:	PUNCT
cet-4972	16	55	10.3303	10.3303	NUM
cet-4972	16	56	/	/	SYM
cet-4972	16	57	cet1543205	cet1543205	NOUN
cet-4972	16	58	1225	1225	NUM
cet-4972	16	59	underlying	underlie	VERB
cet-4972	16	60	structure	structure	NOUN
cet-4972	16	61	of	of	ADP
cet-4972	16	62	the	the	DET
cet-4972	16	63	dataset	dataset	NOUN
cet-4972	16	64	,	,	PUNCT
cet-4972	16	65	one	one	PRON
cet-4972	16	66	can	can	AUX
cet-4972	16	67	only	only	ADV
cet-4972	16	68	say	say	VERB
cet-4972	16	69	that	that	SCONJ
cet-4972	16	70	the	the	DET
cet-4972	16	71	different	different	ADJ
cet-4972	16	72	results	result	NOUN
cet-4972	16	73	obtained	obtain	VERB
cet-4972	16	74	are	be	AUX
cet-4972	16	75	equally	equally	ADV
cet-4972	16	76	plausible	plausible	ADJ
cet-4972	16	77	(	(	PUNCT
cet-4972	16	78	vega	vega	NOUN
cet-4972	16	79	-	-	PUNCT
cet-4972	16	80	pons	pon	NOUN
cet-4972	16	81	and	and	CCONJ
cet-4972	16	82	ruiz	ruiz	NOUN
cet-4972	16	83	-	-	PUNCT
cet-4972	16	84	shulcloper	shulcloper	NOUN
cet-4972	16	85	,	,	PUNCT
cet-4972	16	86	2011	2011	NUM
cet-4972	16	87	)	)	PUNCT
cet-4972	16	88	.	.	PUNCT
cet-4972	17	1	to	to	PART
cet-4972	17	2	proceed	proceed	VERB
cet-4972	17	3	with	with	ADP
cet-4972	17	4	this	this	DET
cet-4972	17	5	uncertainty	uncertainty	NOUN
cet-4972	17	6	in	in	ADP
cet-4972	17	7	the	the	DET
cet-4972	17	8	clustering	clustering	ADJ
cet-4972	17	9	model	model	NOUN
cet-4972	17	10	,	,	PUNCT
cet-4972	17	11	ensemble	ensemble	ADJ
cet-4972	17	12	approaches	approach	NOUN
cet-4972	17	13	have	have	AUX
cet-4972	17	14	been	be	AUX
cet-4972	17	15	proposed	propose	VERB
cet-4972	17	16	(	(	PUNCT
cet-4972	17	17	strehl	strehl	NOUN
cet-4972	17	18	and	and	CCONJ
cet-4972	17	19	ghosh	ghosh	PROPN
cet-4972	17	20	,	,	PUNCT
cet-4972	17	21	2002	2002	NUM
cet-4972	17	22	)	)	PUNCT
cet-4972	17	23	.	.	PUNCT
cet-4972	18	1	a	a	DET
cet-4972	18	2	typical	typical	ADJ
cet-4972	18	3	ensemble	ensemble	ADJ
cet-4972	18	4	clustering	clustering	NOUN
cet-4972	18	5	scheme	scheme	NOUN
cet-4972	18	6	is	be	AUX
cet-4972	18	7	shown	show	VERB
cet-4972	18	8	in	in	ADP
cet-4972	18	9	figure	figure	NOUN
cet-4972	18	10	1	1	NUM
cet-4972	18	11	.	.	PUNCT
cet-4972	19	1	for	for	ADP
cet-4972	19	2	a	a	DET
cet-4972	19	3	given	give	VERB
cet-4972	19	4	dataset	dataset	NOUN
cet-4972	19	5	x	x	SYM
cet-4972	19	6	,	,	PUNCT
cet-4972	19	7	ensemble	ensemble	ADJ
cet-4972	19	8	clustering	clustering	NOUN
cet-4972	19	9	usually	usually	ADV
cet-4972	19	10	entails	entail	VERB
cet-4972	19	11	the	the	DET
cet-4972	19	12	collection	collection	NOUN
cet-4972	19	13	of	of	ADP
cet-4972	19	14	the	the	DET
cet-4972	19	15	results	result	NOUN
cet-4972	19	16	from	from	ADP
cet-4972	19	17	multiple	multiple	ADJ
cet-4972	19	18	base	base	NOUN
cet-4972	19	19	clusterings	clustering	NOUN
cet-4972	19	20	.	.	PUNCT
cet-4972	20	1	the	the	DET
cet-4972	20	2	base	base	NOUN
cet-4972	20	3	clusterings	clustering	NOUN
cet-4972	20	4	composing	compose	VERB
cet-4972	20	5	the	the	DET
cet-4972	20	6	ensemble	ensemble	ADJ
cet-4972	20	7	can	can	AUX
cet-4972	20	8	be	be	AUX
cet-4972	20	9	different	different	ADJ
cet-4972	20	10	because	because	SCONJ
cet-4972	20	11	of	of	ADP
cet-4972	20	12	the	the	DET
cet-4972	20	13	different	different	ADJ
cet-4972	20	14	clustering	clustering	ADJ
cet-4972	20	15	algorithm	algorithm	NOUN
cet-4972	20	16	used	use	VERB
cet-4972	20	17	or	or	CCONJ
cet-4972	20	18	because	because	SCONJ
cet-4972	20	19	of	of	ADP
cet-4972	20	20	the	the	DET
cet-4972	20	21	different	different	ADJ
cet-4972	20	22	data	data	NOUN
cet-4972	20	23	features	feature	NOUN
cet-4972	20	24	extracted	extract	VERB
cet-4972	20	25	from	from	ADP
cet-4972	20	26	x	x	PUNCT
cet-4972	20	27	upon	upon	SCONJ
cet-4972	20	28	which	which	PRON
cet-4972	20	29	(	(	PUNCT
cet-4972	20	30	some	some	PRON
cet-4972	20	31	)	)	PUNCT
cet-4972	20	32	clustering	cluster	VERB
cet-4972	20	33	algorithm	algorithm	NOUN
cet-4972	20	34	is	be	AUX
cet-4972	20	35	applied	apply	VERB
cet-4972	20	36	.	.	PUNCT
cet-4972	21	1	the	the	DET
cet-4972	21	2	outcome	outcome	NOUN
cet-4972	21	3	of	of	ADP
cet-4972	21	4	the	the	DET
cet-4972	21	5	individual	individual	ADJ
cet-4972	21	6	base	base	NOUN
cet-4972	21	7	clusterings	clustering	NOUN
cet-4972	21	8	are	be	AUX
cet-4972	21	9	,	,	PUNCT
cet-4972	21	10	then	then	ADV
cet-4972	21	11	,	,	PUNCT
cet-4972	21	12	aggregated	aggregate	VERB
cet-4972	21	13	into	into	ADP
cet-4972	21	14	the	the	DET
cet-4972	21	15	final	final	ADJ
cet-4972	21	16	ensemble	ensemble	ADJ
cet-4972	21	17	clustering	clustering	NOUN
cet-4972	21	18	p	p	X
cet-4972	21	19	*	*	PUNCT
cet-4972	21	20	by	by	ADP
cet-4972	21	21	a	a	DET
cet-4972	21	22	given	give	VERB
cet-4972	21	23	method	method	NOUN
cet-4972	21	24	of	of	ADP
cet-4972	21	25	aggregation	aggregation	NOUN
cet-4972	21	26	(	(	PUNCT
cet-4972	21	27	strehl	strehl	NOUN
cet-4972	21	28	and	and	CCONJ
cet-4972	21	29	ghosh	ghosh	PROPN
cet-4972	21	30	,	,	PUNCT
cet-4972	21	31	2002	2002	NUM
cet-4972	21	32	;	;	PUNCT
cet-4972	21	33	topchy	topchy	NOUN
cet-4972	21	34	et	et	PROPN
cet-4972	21	35	al	al	PROPN
cet-4972	21	36	.	.	PROPN
cet-4972	21	37	2005	2005	NUM
cet-4972	21	38	;	;	PUNCT
cet-4972	21	39	chen	chen	PROPN
cet-4972	21	40	,	,	PUNCT
cet-4972	21	41	2007	2007	NUM
cet-4972	21	42	;	;	PUNCT
cet-4972	21	43	vega	vega	PROPN
cet-4972	21	44	-	-	PUNCT
cet-4972	21	45	pons	pon	NOUN
cet-4972	21	46	and	and	CCONJ
cet-4972	21	47	ruizshulcloper	ruizshulcloper	NOUN
cet-4972	21	48	,	,	PUNCT
cet-4972	21	49	2011	2011	NUM
cet-4972	21	50	)	)	PUNCT
cet-4972	21	51	.	.	PUNCT
cet-4972	22	1	figure	figure	VERB
cet-4972	22	2	1	1	NUM
cet-4972	22	3	:	:	PUNCT
cet-4972	22	4	scheme	scheme	NOUN
cet-4972	22	5	of	of	ADP
cet-4972	22	6	ensemble	ensemble	ADJ
cet-4972	22	7	clustering	clustering	NOUN
cet-4972	22	8	approach	approach	NOUN
cet-4972	22	9	several	several	ADJ
cet-4972	22	10	methods	method	NOUN
cet-4972	22	11	have	have	AUX
cet-4972	22	12	been	be	AUX
cet-4972	22	13	used	use	VERB
cet-4972	22	14	to	to	PART
cet-4972	22	15	obtain	obtain	VERB
cet-4972	22	16	the	the	DET
cet-4972	22	17	final	final	ADJ
cet-4972	22	18	ensemble	ensemble	ADJ
cet-4972	22	19	clustering	clustering	NOUN
cet-4972	22	20	.	.	PUNCT
cet-4972	23	1	for	for	ADP
cet-4972	23	2	example	example	NOUN
cet-4972	23	3	graph	graph	NOUN
cet-4972	23	4	and	and	CCONJ
cet-4972	23	5	hypergraph	hypergraph	VERB
cet-4972	23	6	partitioning	partitioning	NOUN
cet-4972	23	7	algorithms	algorithm	NOUN
cet-4972	23	8	,	,	PUNCT
cet-4972	23	9	such	such	ADJ
cet-4972	23	10	as	as	ADP
cet-4972	23	11	the	the	DET
cet-4972	23	12	cluster	cluster	NOUN
cet-4972	23	13	-	-	PUNCT
cet-4972	23	14	based	base	VERB
cet-4972	23	15	similarity	similarity	NOUN
cet-4972	23	16	partitioning	partitioning	NOUN
cet-4972	23	17	(	(	PUNCT
cet-4972	23	18	cspa	cspa	NOUN
cet-4972	23	19	)	)	PUNCT
cet-4972	23	20	,	,	PUNCT
cet-4972	23	21	construct	construct	VERB
cet-4972	23	22	a	a	DET
cet-4972	23	23	graph	graph	NOUN
cet-4972	23	24	from	from	ADP
cet-4972	23	25	the	the	DET
cet-4972	23	26	similarities	similarity	NOUN
cet-4972	23	27	among	among	ADP
cet-4972	23	28	the	the	DET
cet-4972	23	29	base	base	NOUN
cet-4972	23	30	clusterings	clustering	NOUN
cet-4972	23	31	,	,	PUNCT
cet-4972	23	32	and	and	CCONJ
cet-4972	23	33	cluster	cluster	NOUN
cet-4972	23	34	it	it	PRON
cet-4972	23	35	using	use	VERB
cet-4972	23	36	a	a	DET
cet-4972	23	37	graphic	graphic	NOUN
cet-4972	23	38	-	-	PUNCT
cet-4972	23	39	based	base	VERB
cet-4972	23	40	clustering	clustering	ADJ
cet-4972	23	41	algorithm	algorithm	NOUN
cet-4972	23	42	such	such	ADJ
cet-4972	23	43	as	as	ADP
cet-4972	23	44	serial	serial	ADJ
cet-4972	23	45	graph	graph	NOUN
cet-4972	23	46	partitioning	partitioning	NOUN
cet-4972	23	47	and	and	CCONJ
cet-4972	23	48	fill	fill	NOUN
cet-4972	23	49	-	-	PUNCT
cet-4972	23	50	reducing	reduce	VERB
cet-4972	23	51	matrix	matrix	NOUN
cet-4972	23	52	ordering	order	VERB
cet-4972	23	53	algorithm	algorithm	NOUN
cet-4972	23	54	(	(	PUNCT
cet-4972	23	55	metis	metis	PROPN
cet-4972	23	56	)	)	PUNCT
cet-4972	23	57	(	(	PUNCT
cet-4972	23	58	karypis	karypi	VERB
cet-4972	23	59	and	and	CCONJ
cet-4972	23	60	kumar	kumar	PROPN
cet-4972	23	61	,	,	PUNCT
cet-4972	23	62	1995	1995	NUM
cet-4972	23	63	;	;	PUNCT
cet-4972	23	64	strehl	strehl	NOUN
cet-4972	23	65	and	and	CCONJ
cet-4972	23	66	ghosh	ghosh	PROPN
cet-4972	23	67	,	,	PUNCT
cet-4972	23	68	2002	2002	NUM
cet-4972	23	69	)	)	PUNCT
cet-4972	23	70	,	,	PUNCT
cet-4972	23	71	for	for	ADP
cet-4972	23	72	a	a	DET
cet-4972	23	73	predetermined	predetermine	VERB
cet-4972	23	74	number	number	NOUN
cet-4972	23	75	of	of	ADP
cet-4972	23	76	clusters	cluster	NOUN
cet-4972	23	77	m	m	VERB
cet-4972	23	78	in	in	ADP
cet-4972	23	79	the	the	DET
cet-4972	23	80	final	final	ADJ
cet-4972	23	81	ensemble	ensemble	ADJ
cet-4972	23	82	clustering	clustering	NOUN
cet-4972	23	83	(	(	PUNCT
cet-4972	23	84	topchy	topchy	NOUN
cet-4972	23	85	et	et	PROPN
cet-4972	23	86	al	al	PROPN
cet-4972	23	87	.	.	PROPN
cet-4972	23	88	2005	2005	NUM
cet-4972	23	89	)	)	PUNCT
cet-4972	23	90	.	.	PUNCT
cet-4972	24	1	in	in	ADP
cet-4972	24	2	this	this	DET
cet-4972	24	3	paper	paper	NOUN
cet-4972	24	4	,	,	PUNCT
cet-4972	24	5	cspa	cspa	NOUN
cet-4972	24	6	and	and	CCONJ
cet-4972	24	7	metis	metis	NOUN
cet-4972	24	8	algorithms	algorithm	NOUN
cet-4972	24	9	have	have	AUX
cet-4972	24	10	been	be	AUX
cet-4972	24	11	taken	take	VERB
cet-4972	24	12	as	as	ADP
cet-4972	24	13	reference	reference	NOUN
cet-4972	24	14	for	for	ADP
cet-4972	24	15	comparison	comparison	NOUN
cet-4972	24	16	because	because	SCONJ
cet-4972	24	17	cspa	cspa	NOUN
cet-4972	24	18	-	-	PUNCT
cet-4972	24	19	metis	metis	NOUN
cet-4972	24	20	is	be	AUX
cet-4972	24	21	the	the	DET
cet-4972	24	22	simplest	simple	ADJ
cet-4972	24	23	and	and	CCONJ
cet-4972	24	24	often	often	ADV
cet-4972	24	25	best	well	ADV
cet-4972	24	26	performing	perform	VERB
cet-4972	24	27	method	method	NOUN
cet-4972	24	28	for	for	ADP
cet-4972	24	29	ensemble	ensemble	ADJ
cet-4972	24	30	aggregation	aggregation	NOUN
cet-4972	24	31	(	(	PUNCT
cet-4972	24	32	strehl	strehl	NOUN
cet-4972	24	33	and	and	CCONJ
cet-4972	24	34	ghosh	ghosh	PROPN
cet-4972	24	35	,	,	PUNCT
cet-4972	24	36	2002	2002	NUM
cet-4972	24	37	)	)	PUNCT
cet-4972	24	38	.	.	PUNCT
cet-4972	25	1	however	however	ADV
cet-4972	25	2	,	,	PUNCT
cet-4972	25	3	for	for	ADP
cet-4972	25	4	most	most	ADJ
cet-4972	25	5	industrial	industrial	ADJ
cet-4972	25	6	applications	application	NOUN
cet-4972	25	7	,	,	PUNCT
cet-4972	25	8	the	the	DET
cet-4972	25	9	“	"	PUNCT
cet-4972	25	10	a	a	DET
cet-4972	25	11	priori	priori	ADJ
cet-4972	25	12	”	"	PUNCT
cet-4972	25	13	knowledge	knowledge	NOUN
cet-4972	25	14	of	of	ADP
cet-4972	25	15	the	the	DET
cet-4972	25	16	number	number	NOUN
cet-4972	25	17	of	of	ADP
cet-4972	25	18	clusters	cluster	NOUN
cet-4972	25	19	m	m	VERB
cet-4972	25	20	to	to	PART
cet-4972	25	21	be	be	AUX
cet-4972	25	22	found	find	VERB
cet-4972	25	23	in	in	ADP
cet-4972	25	24	the	the	DET
cet-4972	25	25	final	final	ADJ
cet-4972	25	26	clustering	clustering	NOUN
cet-4972	25	27	is	be	AUX
cet-4972	25	28	rarely	rarely	ADV
cet-4972	25	29	available	available	ADJ
cet-4972	25	30	(	(	PUNCT
cet-4972	25	31	chakaravathy	chakaravathy	ADJ
cet-4972	25	32	and	and	CCONJ
cet-4972	25	33	ghosh	ghosh	PROPN
cet-4972	25	34	,	,	PUNCT
cet-4972	25	35	1996	1996	NUM
cet-4972	25	36	;	;	PUNCT
cet-4972	25	37	strehl	strehl	NOUN
cet-4972	25	38	and	and	CCONJ
cet-4972	25	39	ghosh	ghosh	PROPN
cet-4972	25	40	,	,	PUNCT
cet-4972	25	41	2002	2002	NUM
cet-4972	25	42	)	)	PUNCT
cet-4972	25	43	.	.	PUNCT
cet-4972	26	1	for	for	ADP
cet-4972	26	2	this	this	DET
cet-4972	26	3	reason	reason	NOUN
cet-4972	26	4	,	,	PUNCT
cet-4972	26	5	an	an	DET
cet-4972	26	6	objective	objective	NOUN
cet-4972	26	7	of	of	ADP
cet-4972	26	8	the	the	DET
cet-4972	26	9	present	present	ADJ
cet-4972	26	10	work	work	NOUN
cet-4972	26	11	is	be	AUX
cet-4972	26	12	to	to	PART
cet-4972	26	13	develop	develop	VERB
cet-4972	26	14	a	a	DET
cet-4972	26	15	novel	novel	ADJ
cet-4972	26	16	unsupervised	unsupervised	ADJ
cet-4972	26	17	ensemble	ensemble	ADJ
cet-4972	26	18	clustering	clustering	NOUN
cet-4972	26	19	approach	approach	NOUN
cet-4972	26	20	capable	capable	ADJ
cet-4972	26	21	of	of	ADP
cet-4972	26	22	identifying	identify	VERB
cet-4972	26	23	the	the	DET
cet-4972	26	24	final	final	ADJ
cet-4972	26	25	ensemble	ensemble	ADJ
cet-4972	26	26	clustering	clustering	NOUN
cet-4972	26	27	without	without	ADP
cet-4972	26	28	any	any	DET
cet-4972	26	29	previous	previous	ADJ
cet-4972	26	30	knowledge	knowledge	NOUN
cet-4972	26	31	of	of	ADP
cet-4972	26	32	m.	m.	NOUN
cet-4972	26	33	to	to	ADP
cet-4972	26	34	this	this	DET
cet-4972	26	35	aim	aim	NOUN
cet-4972	26	36	,	,	PUNCT
cet-4972	26	37	we	we	PRON
cet-4972	26	38	propose	propose	VERB
cet-4972	26	39	to	to	PART
cet-4972	26	40	replace	replace	VERB
cet-4972	26	41	metis	metis	NOUN
cet-4972	26	42	algorithm	algorithm	NOUN
cet-4972	26	43	with	with	ADP
cet-4972	26	44	spectral	spectral	ADJ
cet-4972	26	45	clustering	clustering	NOUN
cet-4972	26	46	(	(	PUNCT
cet-4972	26	47	von	von	PROPN
cet-4972	26	48	luxburg	luxburg	PROPN
cet-4972	26	49	,	,	PUNCT
cet-4972	26	50	2007	2007	NUM
cet-4972	26	51	;	;	PUNCT
cet-4972	26	52	baraldi	baraldi	PROPN
cet-4972	26	53	et	et	PROPN
cet-4972	26	54	al	al	PROPN
cet-4972	26	55	.	.	PROPN
cet-4972	26	56	2013a	2013a	NUM
cet-4972	26	57	)	)	PUNCT
cet-4972	26	58	and	and	CCONJ
cet-4972	26	59	silhouette	silhouette	NOUN
cet-4972	26	60	validity	validity	NOUN
cet-4972	26	61	index	index	NOUN
cet-4972	26	62	(	(	PUNCT
cet-4972	26	63	rousseeuw	rousseeuw	PROPN
cet-4972	26	64	,	,	PUNCT
cet-4972	26	65	1987	1987	NUM
cet-4972	26	66	)	)	PUNCT
cet-4972	26	67	to	to	PART
cet-4972	26	68	automatically	automatically	ADV
cet-4972	26	69	determine	determine	VERB
cet-4972	26	70	m.	m.	NOUN
cet-4972	26	71	practically	practically	ADV
cet-4972	26	72	,	,	PUNCT
cet-4972	26	73	the	the	DET
cet-4972	26	74	base	base	NOUN
cet-4972	26	75	clustering	clustering	NOUN
cet-4972	26	76	results	result	NOUN
cet-4972	26	77	are	be	AUX
cet-4972	26	78	summarized	summarize	VERB
cet-4972	26	79	in	in	ADP
cet-4972	26	80	a	a	DET
cet-4972	26	81	co	co	NOUN
cet-4972	26	82	-	-	NOUN
cet-4972	26	83	association	association	NOUN
cet-4972	26	84	matrix	matrix	NOUN
cet-4972	26	85	by	by	ADP
cet-4972	26	86	pairwise	pairwise	NOUN
cet-4972	26	87	similarity	similarity	NOUN
cet-4972	26	88	computation	computation	NOUN
cet-4972	26	89	.	.	PUNCT
cet-4972	27	1	then	then	ADV
cet-4972	27	2	,	,	PUNCT
cet-4972	27	3	a	a	DET
cet-4972	27	4	spectral	spectral	ADJ
cet-4972	27	5	clustering	clustering	NOUN
cet-4972	27	6	technique	technique	NOUN
cet-4972	27	7	(	(	PUNCT
cet-4972	27	8	von	von	PROPN
cet-4972	27	9	luxburg	luxburg	PROPN
cet-4972	27	10	,	,	PUNCT
cet-4972	27	11	2007	2007	NUM
cet-4972	27	12	;	;	PUNCT
cet-4972	27	13	baraldi	baraldi	PROPN
cet-4972	27	14	et	et	PROPN
cet-4972	27	15	al	al	PROPN
cet-4972	27	16	.	.	PROPN
cet-4972	27	17	2013a	2013a	NUM
cet-4972	27	18	)	)	PUNCT
cet-4972	27	19	,	,	PUNCT
cet-4972	27	20	embedding	embed	VERB
cet-4972	27	21	the	the	DET
cet-4972	27	22	unsupervised	unsupervised	ADJ
cet-4972	27	23	kmeans	kmean	NOUN
cet-4972	27	24	algorithm	algorithm	NOUN
cet-4972	27	25	,	,	PUNCT
cet-4972	27	26	is	be	AUX
cet-4972	27	27	fed	feed	VERB
cet-4972	27	28	by	by	ADP
cet-4972	27	29	the	the	DET
cet-4972	27	30	similarity	similarity	NOUN
cet-4972	27	31	matrix	matrix	NOUN
cet-4972	27	32	values	value	NOUN
cet-4972	27	33	for	for	ADP
cet-4972	27	34	mining	mine	VERB
cet-4972	27	35	the	the	DET
cet-4972	27	36	clusters	cluster	NOUN
cet-4972	27	37	that	that	PRON
cet-4972	27	38	are	be	AUX
cet-4972	27	39	formed	form	VERB
cet-4972	27	40	by	by	ADP
cet-4972	27	41	the	the	DET
cet-4972	27	42	most	most	ADV
cet-4972	27	43	similar	similar	ADJ
cet-4972	27	44	data	datum	NOUN
cet-4972	27	45	.	.	PUNCT
cet-4972	28	1	the	the	DET
cet-4972	28	2	optimum	optimum	ADJ
cet-4972	28	3	number	number	NOUN
cet-4972	28	4	of	of	ADP
cet-4972	28	5	clusters	cluster	NOUN
cet-4972	28	6	c	c	VERB
cet-4972	28	7	*	*	VERB
cet-4972	28	8	is	be	AUX
cet-4972	28	9	selected	select	VERB
cet-4972	28	10	among	among	ADP
cet-4972	28	11	several	several	ADJ
cet-4972	28	12	candidates	candidate	NOUN
cet-4972	28	13	ccandidate	ccandidate	ADJ
cet-4972	28	14	,	,	PUNCT
cet-4972	28	15	based	base	VERB
cet-4972	28	16	on	on	ADP
cet-4972	28	17	the	the	DET
cet-4972	28	18	morphology	morphology	NOUN
cet-4972	28	19	of	of	ADP
cet-4972	28	20	the	the	DET
cet-4972	28	21	obtained	obtain	VERB
cet-4972	28	22	final	final	ADJ
cet-4972	28	23	ensemble	ensemble	ADJ
cet-4972	28	24	clusters	cluster	NOUN
cet-4972	28	25	,	,	PUNCT
cet-4972	28	26	evaluated	evaluate	VERB
cet-4972	28	27	by	by	ADP
cet-4972	28	28	the	the	DET
cet-4972	28	29	silhouette	silhouette	NOUN
cet-4972	28	30	validity	validity	NOUN
cet-4972	28	31	index	index	NOUN
cet-4972	28	32	that	that	PRON
cet-4972	28	33	measures	measure	VERB
cet-4972	28	34	the	the	DET
cet-4972	28	35	similarity	similarity	NOUN
cet-4972	28	36	of	of	ADP
cet-4972	28	37	the	the	DET
cet-4972	28	38	data	datum	NOUN
cet-4972	28	39	belonging	belong	VERB
cet-4972	28	40	to	to	ADP
cet-4972	28	41	the	the	DET
cet-4972	28	42	same	same	ADJ
cet-4972	28	43	cluster	cluster	NOUN
cet-4972	28	44	and	and	CCONJ
cet-4972	28	45	the	the	DET
cet-4972	28	46	dissimilarity	dissimilarity	NOUN
cet-4972	28	47	of	of	ADP
cet-4972	28	48	these	these	PRON
cet-4972	28	49	in	in	ADP
cet-4972	28	50	the	the	DET
cet-4972	28	51	other	other	ADJ
cet-4972	28	52	clusters	cluster	NOUN
cet-4972	28	53	(	(	PUNCT
cet-4972	28	54	a	a	DET
cet-4972	28	55	large	large	ADJ
cet-4972	28	56	silhouette	silhouette	NOUN
cet-4972	28	57	value	value	NOUN
cet-4972	28	58	indicates	indicate	VERB
cet-4972	28	59	that	that	SCONJ
cet-4972	28	60	the	the	DET
cet-4972	28	61	obtained	obtain	VERB
cet-4972	28	62	clusters	cluster	NOUN
cet-4972	28	63	of	of	ADP
cet-4972	28	64	the	the	DET
cet-4972	28	65	final	final	ADJ
cet-4972	28	66	ensemble	ensemble	ADJ
cet-4972	28	67	clustering	clustering	NOUN
cet-4972	28	68	are	be	AUX
cet-4972	28	69	well	well	ADV
cet-4972	28	70	separated	separate	VERB
cet-4972	28	71	and	and	CCONJ
cet-4972	28	72	compacted	compact	VERB
cet-4972	28	73	(	(	PUNCT
cet-4972	28	74	rousseeuw	rousseeuw	PROPN
cet-4972	28	75	,	,	PUNCT
cet-4972	28	76	1987	1987	NUM
cet-4972	28	77	)	)	PUNCT
cet-4972	28	78	)	)	PUNCT
cet-4972	28	79	.	.	PUNCT
cet-4972	29	1	the	the	DET
cet-4972	29	2	proposed	propose	VERB
cet-4972	29	3	approach	approach	NOUN
cet-4972	29	4	is	be	AUX
cet-4972	29	5	tested	test	VERB
cet-4972	29	6	with	with	ADP
cet-4972	29	7	respect	respect	NOUN
cet-4972	29	8	to	to	ADP
cet-4972	29	9	an	an	DET
cet-4972	29	10	artificial	artificial	ADJ
cet-4972	29	11	case	case	NOUN
cet-4972	29	12	study	study	NOUN
cet-4972	29	13	representative	representative	NOUN
cet-4972	29	14	of	of	ADP
cet-4972	29	15	the	the	DET
cet-4972	29	16	signal	signal	ADJ
cet-4972	29	17	trend	trend	NOUN
cet-4972	29	18	behavior	behavior	NOUN
cet-4972	29	19	of	of	ADP
cet-4972	29	20	industrial	industrial	ADJ
cet-4972	29	21	equipment	equipment	NOUN
cet-4972	29	22	during	during	ADP
cet-4972	29	23	shut	shut	NOUN
cet-4972	29	24	-	-	PUNCT
cet-4972	29	25	down	down	ADP
cet-4972	29	26	transients	transient	NOUN
cet-4972	29	27	.	.	PUNCT
cet-4972	30	1	the	the	DET
cet-4972	30	2	results	result	NOUN
cet-4972	30	3	obtained	obtain	VERB
cet-4972	30	4	have	have	AUX
cet-4972	30	5	been	be	AUX
cet-4972	30	6	compared	compare	VERB
cet-4972	30	7	with	with	ADP
cet-4972	30	8	those	those	PRON
cet-4972	30	9	achieved	achieve	VERB
cet-4972	30	10	by	by	ADP
cet-4972	30	11	cspa	cspa	NOUN
cet-4972	30	12	-	-	PUNCT
cet-4972	30	13	metis	metis	NOUN
cet-4972	30	14	approach	approach	NOUN
cet-4972	30	15	.	.	PUNCT
cet-4972	31	1	the	the	DET
cet-4972	31	2	remaining	remain	VERB
cet-4972	31	3	of	of	ADP
cet-4972	31	4	this	this	DET
cet-4972	31	5	paper	paper	NOUN
cet-4972	31	6	is	be	AUX
cet-4972	31	7	organized	organize	VERB
cet-4972	31	8	as	as	SCONJ
cet-4972	31	9	follows	follow	VERB
cet-4972	31	10	.	.	PUNCT
cet-4972	32	1	in	in	ADP
cet-4972	32	2	section	section	NOUN
cet-4972	32	3	2	2	NUM
cet-4972	32	4	,	,	PUNCT
cet-4972	32	5	the	the	DET
cet-4972	32	6	novel	novel	ADJ
cet-4972	32	7	unsupervised	unsupervised	ADJ
cet-4972	32	8	ensemble	ensemble	ADJ
cet-4972	32	9	clustering	clustering	NOUN
cet-4972	32	10	approach	approach	NOUN
cet-4972	32	11	is	be	AUX
cet-4972	32	12	proposed	propose	VERB
cet-4972	32	13	.	.	PUNCT
cet-4972	33	1	the	the	DET
cet-4972	33	2	artificial	artificial	ADJ
cet-4972	33	3	case	case	NOUN
cet-4972	33	4	study	study	NOUN
cet-4972	33	5	representative	representative	NOUN
cet-4972	33	6	of	of	ADP
cet-4972	33	7	the	the	DET
cet-4972	33	8	signal	signal	ADJ
cet-4972	33	9	trend	trend	NOUN
cet-4972	33	10	behavior	behavior	NOUN
cet-4972	33	11	of	of	ADP
cet-4972	33	12	industrial	industrial	ADJ
cet-4972	33	13	equipment	equipment	NOUN
cet-4972	33	14	during	during	ADP
cet-4972	33	15	shut	shut	NOUN
cet-4972	33	16	-	-	PUNCT
cet-4972	33	17	down	down	ADP
cet-4972	33	18	transients	transient	NOUN
cet-4972	33	19	is	be	AUX
cet-4972	33	20	introduced	introduce	VERB
cet-4972	33	21	in	in	ADP
cet-4972	33	22	section	section	NOUN
cet-4972	33	23	3	3	NUM
cet-4972	33	24	.	.	PUNCT
cet-4972	34	1	furthermore	furthermore	ADV
cet-4972	34	2	,	,	PUNCT
cet-4972	34	3	the	the	DET
cet-4972	34	4	results	result	NOUN
cet-4972	34	5	obtained	obtain	VERB
cet-4972	34	6	with	with	ADP
cet-4972	34	7	the	the	DET
cet-4972	34	8	application	application	NOUN
cet-4972	34	9	of	of	ADP
cet-4972	34	10	the	the	DET
cet-4972	34	11	novel	novel	ADJ
cet-4972	34	12	approach	approach	NOUN
cet-4972	34	13	to	to	ADP
cet-4972	34	14	the	the	DET
cet-4972	34	15	artificial	artificial	ADJ
cet-4972	34	16	case	case	NOUN
cet-4972	34	17	and	and	CCONJ
cet-4972	34	18	the	the	DET
cet-4972	34	19	comparison	comparison	NOUN
cet-4972	34	20	with	with	ADP
cet-4972	34	21	cspa	cspa	NOUN
cet-4972	34	22	-	-	PUNCT
cet-4972	34	23	metis	metis	NOUN
cet-4972	34	24	,	,	PUNCT
cet-4972	34	25	are	be	AUX
cet-4972	34	26	discussed	discuss	VERB
cet-4972	34	27	in	in	ADP
cet-4972	34	28	section	section	NOUN
cet-4972	34	29	4	4	NUM
cet-4972	34	30	.	.	PUNCT
cet-4972	35	1	finally	finally	ADV
cet-4972	35	2	,	,	PUNCT
cet-4972	35	3	section	section	NOUN
cet-4972	35	4	5	5	NUM
cet-4972	35	5	concludes	conclude	VERB
cet-4972	35	6	the	the	DET
cet-4972	35	7	paper	paper	NOUN
cet-4972	35	8	with	with	ADP
cet-4972	35	9	some	some	DET
cet-4972	35	10	considerations	consideration	NOUN
cet-4972	35	11	.	.	PUNCT
cet-4972	36	1	2	2	X
cet-4972	36	2	.	.	X
cet-4972	36	3	the	the	DET
cet-4972	36	4	novel	novel	ADJ
cet-4972	36	5	unsupervised	unsupervised	ADJ
cet-4972	36	6	ensemble	ensemble	ADJ
cet-4972	36	7	clustering	clustering	NOUN
cet-4972	36	8	approach	approach	NOUN
cet-4972	36	9	in	in	ADP
cet-4972	36	10	this	this	DET
cet-4972	36	11	section	section	NOUN
cet-4972	36	12	,	,	PUNCT
cet-4972	36	13	the	the	DET
cet-4972	36	14	novel	novel	ADJ
cet-4972	36	15	unsupervised	unsupervised	ADJ
cet-4972	36	16	ensemble	ensemble	ADJ
cet-4972	36	17	clustering	clustering	NOUN
cet-4972	36	18	approach	approach	NOUN
cet-4972	36	19	is	be	AUX
cet-4972	36	20	proposed	propose	VERB
cet-4972	36	21	to	to	PART
cet-4972	36	22	overcome	overcome	VERB
cet-4972	36	23	the	the	DET
cet-4972	36	24	need	need	NOUN
cet-4972	36	25	of	of	ADP
cet-4972	36	26	having	have	VERB
cet-4972	36	27	“	"	PUNCT
cet-4972	36	28	a	a	DET
cet-4972	36	29	priori	priori	ADJ
cet-4972	36	30	”	"	PUNCT
cet-4972	36	31	knowledge	knowledge	NOUN
cet-4972	36	32	of	of	ADP
cet-4972	36	33	the	the	DET
cet-4972	36	34	number	number	NOUN
cet-4972	36	35	of	of	ADP
cet-4972	36	36	clusters	cluster	NOUN
cet-4972	36	37	m	m	VERB
cet-4972	36	38	in	in	ADP
cet-4972	36	39	the	the	DET
cet-4972	36	40	final	final	ADJ
cet-4972	36	41	ensemble	ensemble	ADJ
cet-4972	36	42	clustering	clustering	NOUN
cet-4972	36	43	.	.	PUNCT
cet-4972	37	1	the	the	DET
cet-4972	37	2	flowchart	flowchart	NOUN
cet-4972	37	3	for	for	ADP
cet-4972	37	4	the	the	DET
cet-4972	37	5	method	method	NOUN
cet-4972	37	6	is	be	AUX
cet-4972	37	7	sketched	sketch	VERB
cet-4972	37	8	in	in	ADP
cet-4972	37	9	figure	figure	NOUN
cet-4972	37	10	2	2	NUM
cet-4972	37	11	.	.	PUNCT
cet-4972	38	1	the	the	DET
cet-4972	38	2	algorithm	algorithm	NOUN
cet-4972	38	3	goes	go	VERB
cet-4972	38	4	along	along	ADP
cet-4972	38	5	the	the	DET
cet-4972	38	6	following	follow	VERB
cet-4972	38	7	two	two	NUM
cet-4972	38	8	phases	phase	NOUN
cet-4972	38	9	:	:	PUNCT
cet-4972	38	10	a	a	DET
cet-4972	38	11	procedure	procedure	NOUN
cet-4972	38	12	(	(	PUNCT
cet-4972	38	13	i.e.	i.e.	X
cet-4972	38	14	,	,	PUNCT
cet-4972	38	15	cspa	cspa	NOUN
cet-4972	38	16	)	)	PUNCT
cet-4972	38	17	for	for	ADP
cet-4972	38	18	establishing	establish	VERB
cet-4972	38	19	a	a	DET
cet-4972	38	20	similarity	similarity	NOUN
cet-4972	38	21	matrix	matrix	NOUN
cet-4972	38	22	s	s	NOUN
cet-4972	38	23	and	and	CCONJ
cet-4972	38	24	a	a	DET
cet-4972	38	25	novel	novel	ADJ
cet-4972	38	26	ensemble	ensemble	ADJ
cet-4972	38	27	procedure	procedure	NOUN
cet-4972	38	28	for	for	ADP
cet-4972	38	29	revealing	reveal	VERB
cet-4972	38	30	the	the	DET
cet-4972	38	31	“	"	PUNCT
cet-4972	38	32	hidden	hidden	ADJ
cet-4972	38	33	”	"	PUNCT
cet-4972	38	34	structure	structure	NOUN
cet-4972	38	35	p	p	NOUN
cet-4972	38	36	*	*	PUNCT
cet-4972	38	37	of	of	ADP
cet-4972	38	38	s	s	PRON
cet-4972	38	39	by	by	ADP
cet-4972	38	40	adopting	adopt	VERB
cet-4972	38	41	spectral	spectral	ADJ
cet-4972	38	42	clustering	clustering	NOUN
cet-4972	38	43	and	and	CCONJ
cet-4972	38	44	silhouette	silhouette	NOUN
cet-4972	38	45	validity	validity	NOUN
cet-4972	38	46	index	index	NOUN
cet-4972	38	47	.	.	PUNCT
cet-4972	39	1	dataset	dataset	NOUN
cet-4972	39	2	matrix	matrix	NOUN
cet-4972	39	3	x	x	X
cet-4972	39	4	…	…	PUNCT
cet-4972	39	5	…	…	PUNCT
cet-4972	39	6	base	base	NOUN
cet-4972	39	7	clustering	clustering	ADJ
cet-4972	39	8	base	base	NOUN
cet-4972	39	9	clustering	clustering	NOUN
cet-4972	39	10	base	base	NOUN
cet-4972	39	11	clustering	cluster	VERB
cet-4972	39	12	ensemble	ensemble	ADJ
cet-4972	39	13	clustering	clustering	NOUN
cet-4972	39	14	*	*	PUNCT
cet-4972	39	15	p	p	NOUN
cet-4972	39	16	1226	1226	NUM
cet-4972	39	17	figure	figure	NOUN
cet-4972	39	18	2	2	NUM
cet-4972	39	19	:	:	PUNCT
cet-4972	39	20	flowchart	flowchart	NOUN
cet-4972	39	21	of	of	ADP
cet-4972	39	22	the	the	DET
cet-4972	39	23	proposed	propose	VERB
cet-4972	39	24	approach	approach	NOUN
cet-4972	39	25	we	we	PRON
cet-4972	39	26	consider	consider	VERB
cet-4972	39	27	n	n	PRON
cet-4972	39	28	data	datum	NOUN
cet-4972	39	29	belonging	belong	VERB
cet-4972	39	30	to	to	ADP
cet-4972	39	31	the	the	DET
cet-4972	39	32	dataset	dataset	NOUN
cet-4972	39	33	x	x	PUNCT
cet-4972	39	34	that	that	PRON
cet-4972	39	35	are	be	AUX
cet-4972	39	36	clustered	cluster	VERB
cet-4972	39	37	into	into	ADP
cet-4972	39	38	h	h	PROPN
cet-4972	39	39	base	base	PROPN
cet-4972	39	40	clusterings	clustering	NOUN
cet-4972	39	41	.	.	PUNCT
cet-4972	40	1	for	for	ADP
cet-4972	40	2	each	each	DET
cet-4972	40	3	j	j	PROPN
cet-4972	40	4	-	-	PUNCT
cet-4972	40	5	th	th	VERB
cet-4972	40	6	base	base	NOUN
cet-4972	40	7	clustering	clustering	NOUN
cet-4972	40	8	,	,	PUNCT
cet-4972	40	9	j=1,	j=1,	NOUN
cet-4972	40	10	…	…	PUNCT
cet-4972	40	11	,h	,h	NOUN
cet-4972	40	12	,	,	PUNCT
cet-4972	40	13	each	each	DET
cet-4972	40	14	datum	datum	NOUN
cet-4972	40	15	is	be	AUX
cet-4972	40	16	labeled	label	VERB
cet-4972	40	17	by	by	ADP
cet-4972	40	18	an	an	DET
cet-4972	40	19	integer	integer	NOUN
cet-4972	40	20	number	number	NOUN
cet-4972	40	21	ranging	range	VERB
cet-4972	40	22	in	in	ADP
cet-4972	40	23	[	[	X
cet-4972	40	24	1	1	NUM
cet-4972	40	25	,	,	PUNCT
cet-4972	40	26	]	]	PUNCT
cet-4972	40	27	j	j	PROPN
cet-4972	40	28	optc	optc	NOUN
cet-4972	40	29	,	,	PUNCT
cet-4972	40	30	where	where	SCONJ
cet-4972	40	31	j	j	PROPN
cet-4972	40	32	optc	optc	NOUN
cet-4972	40	33	is	be	AUX
cet-4972	40	34	the	the	DET
cet-4972	40	35	number	number	NOUN
cet-4972	40	36	of	of	ADP
cet-4972	40	37	clusters	cluster	NOUN
cet-4972	40	38	for	for	ADP
cet-4972	40	39	each	each	DET
cet-4972	40	40	j	j	PROPN
cet-4972	40	41	-	-	PUNCT
cet-4972	40	42	th	th	VERB
cet-4972	40	43	base	base	NOUN
cet-4972	40	44	clustering	clustering	NOUN
cet-4972	40	45	.	.	PUNCT
cet-4972	41	1	the	the	DET
cet-4972	41	2	problem	problem	NOUN
cet-4972	41	3	of	of	ADP
cet-4972	41	4	clustering	cluster	VERB
cet-4972	41	5	the	the	DET
cet-4972	41	6	n	n	PRON
cet-4972	41	7	data	data	NOUN
cet-4972	41	8	is	be	AUX
cet-4972	41	9	,	,	PUNCT
cet-4972	41	10	thus	thus	ADV
cet-4972	41	11	,	,	PUNCT
cet-4972	41	12	transformed	transform	VERB
cet-4972	41	13	into	into	ADP
cet-4972	41	14	an	an	DET
cet-4972	41	15	aggregation	aggregation	NOUN
cet-4972	41	16	problem	problem	NOUN
cet-4972	41	17	of	of	ADP
cet-4972	41	18	the	the	DET
cet-4972	41	19	base	base	NOUN
cet-4972	41	20	clusterings	clustering	NOUN
cet-4972	41	21	outcomes	outcome	NOUN
cet-4972	41	22	y	y	PROPN
cet-4972	41	23	of	of	ADP
cet-4972	41	24	size	size	NOUN
cet-4972	41	25	nxh	nxh	NOUN
cet-4972	41	26	.	.	PUNCT
cet-4972	42	1	the	the	DET
cet-4972	42	2	algorithm	algorithm	NOUN
cet-4972	42	3	entails	entail	VERB
cet-4972	42	4	five	five	NUM
cet-4972	42	5	main	main	ADJ
cet-4972	42	6	steps	step	NOUN
cet-4972	42	7	,	,	PUNCT
cet-4972	42	8	where	where	SCONJ
cet-4972	42	9	the	the	DET
cet-4972	42	10	first	first	ADJ
cet-4972	42	11	two	two	NUM
cet-4972	42	12	are	be	AUX
cet-4972	42	13	the	the	DET
cet-4972	42	14	cspa	cspa	NOUN
cet-4972	42	15	procedure	procedure	NOUN
cet-4972	42	16	and	and	CCONJ
cet-4972	42	17	the	the	DET
cet-4972	42	18	last	last	ADJ
cet-4972	42	19	three	three	NUM
cet-4972	42	20	are	be	AUX
cet-4972	42	21	the	the	DET
cet-4972	42	22	novel	novel	ADJ
cet-4972	42	23	ensemble	ensemble	ADJ
cet-4972	42	24	procedure	procedure	NOUN
cet-4972	42	25	:	:	PUNCT
cet-4972	42	26	step	step	NOUN
cet-4972	42	27	1	1	NUM
cet-4972	42	28	:	:	PUNCT
cet-4972	42	29	adjacency	adjacency	NOUN
cet-4972	42	30	matrix	matrix	NOUN
cet-4972	42	31	computation	computation	NOUN
cet-4972	42	32	.	.	PUNCT
cet-4972	43	1	in	in	ADP
cet-4972	43	2	practice	practice	NOUN
cet-4972	43	3	,	,	PUNCT
cet-4972	43	4	for	for	ADP
cet-4972	43	5	each	each	DET
cet-4972	43	6	j	j	PROPN
cet-4972	43	7	-	-	PUNCT
cet-4972	43	8	th	th	VERB
cet-4972	43	9	base	base	NOUN
cet-4972	43	10	clustering	clustering	NOUN
cet-4972	43	11	,	,	PUNCT
cet-4972	43	12	if	if	SCONJ
cet-4972	43	13	two	two	NUM
cet-4972	43	14	data	datum	NOUN
cet-4972	43	15	belong	belong	VERB
cet-4972	43	16	to	to	ADP
cet-4972	43	17	the	the	DET
cet-4972	43	18	same	same	ADJ
cet-4972	43	19	cluster	cluster	NOUN
cet-4972	43	20	they	they	PRON
cet-4972	43	21	are	be	AUX
cet-4972	43	22	considered	consider	VERB
cet-4972	43	23	similar	similar	ADJ
cet-4972	43	24	,	,	PUNCT
cet-4972	43	25	i.e.	i.e.	X
cet-4972	43	26	,	,	PUNCT
cet-4972	43	27	similarity	similarity	NOUN
cet-4972	43	28	μ=1	μ=1	NOUN
cet-4972	43	29	,	,	PUNCT
cet-4972	43	30	and	and	CCONJ
cet-4972	43	31	if	if	SCONJ
cet-4972	43	32	not	not	PART
cet-4972	43	33	they	they	PRON
cet-4972	43	34	are	be	AUX
cet-4972	43	35	dissimilar	dissimilar	ADJ
cet-4972	43	36	,	,	PUNCT
cet-4972	43	37	i.e.	i.e.	X
cet-4972	43	38	,	,	PUNCT
cet-4972	43	39	similarity	similarity	NOUN
cet-4972	43	40	μ=0	μ=0	NOUN
cet-4972	43	41	.	.	PUNCT
cet-4972	44	1	thus	thus	ADV
cet-4972	44	2	,	,	PUNCT
cet-4972	44	3	an	an	DET
cet-4972	44	4	adjacency	adjacency	NOUN
cet-4972	44	5	binary	binary	PROPN
cet-4972	44	6	similarity	similarity	PROPN
cet-4972	44	7	matrix	matrix	NOUN
cet-4972	44	8	,	,	PUNCT
cet-4972	44	9	a	a	PRON
cet-4972	44	10	,	,	PUNCT
cet-4972	44	11	is	be	AUX
cet-4972	44	12	built	build	VERB
cet-4972	44	13	by	by	ADP
cet-4972	44	14	aggregating	aggregate	VERB
cet-4972	44	15	the	the	DET
cet-4972	44	16	similarities	similarity	NOUN
cet-4972	44	17	of	of	ADP
cet-4972	44	18	the	the	DET
cet-4972	44	19	h	h	NOUN
cet-4972	44	20	base	base	NOUN
cet-4972	44	21	clusterings	clustering	NOUN
cet-4972	44	22	(	(	PUNCT
cet-4972	44	23	strehl	strehl	NOUN
cet-4972	44	24	and	and	CCONJ
cet-4972	44	25	ghosh	ghosh	PROPN
cet-4972	44	26	,	,	PUNCT
cet-4972	44	27	2002	2002	NUM
cet-4972	44	28	)	)	PUNCT
cet-4972	44	29	.	.	PUNCT
cet-4972	45	1	step	step	NOUN
cet-4972	45	2	2	2	NUM
cet-4972	45	3	:	:	PUNCT
cet-4972	45	4	similarity	similarity	NOUN
cet-4972	45	5	matrix	matrix	NOUN
cet-4972	45	6	computation	computation	NOUN
cet-4972	45	7	.	.	PUNCT
cet-4972	46	1	from	from	ADP
cet-4972	46	2	the	the	DET
cet-4972	46	3	adjacency	adjacency	NOUN
cet-4972	46	4	binary	binary	PROPN
cet-4972	46	5	similarity	similarity	PROPN
cet-4972	46	6	matrix	matrix	NOUN
cet-4972	46	7	,	,	PUNCT
cet-4972	46	8	a	a	PRON
cet-4972	46	9	,	,	PUNCT
cet-4972	46	10	the	the	DET
cet-4972	46	11	overall	overall	ADJ
cet-4972	46	12	similarity	similarity	NOUN
cet-4972	46	13	matrix	matrix	NOUN
cet-4972	46	14	s	s	PART
cet-4972	46	15	,	,	PUNCT
cet-4972	46	16	is	be	AUX
cet-4972	46	17	computed	compute	VERB
cet-4972	46	18	as	as	ADP
cet-4972	46	19	the	the	DET
cet-4972	46	20	entry	entry	NOUN
cet-4972	46	21	-	-	PUNCT
cet-4972	46	22	wise	wise	ADJ
cet-4972	46	23	average	average	NOUN
cet-4972	46	24	of	of	ADP
cet-4972	46	25	the	the	DET
cet-4972	46	26	h	h	PROPN
cet-4972	46	27	base	base	PROPN
cet-4972	46	28	clusterings	clustering	NOUN
cet-4972	46	29	,	,	PUNCT
cet-4972	46	30	i.e.	i.e.	X
cet-4972	46	31	,	,	PUNCT
cet-4972	46	32	1	1	NUM
cet-4972	46	33	t	t	NOUN
cet-4972	46	34	s	s	VERB
cet-4972	46	35	a	a	DET
cet-4972	46	36	a	a	DET
cet-4972	46	37	h	h	NOUN
cet-4972	46	38	=	=	PUNCT
cet-4972	46	39	(	(	PUNCT
cet-4972	46	40	strehl	strehl	PROPN
cet-4972	46	41	and	and	CCONJ
cet-4972	46	42	ghosh	ghosh	PROPN
cet-4972	46	43	,	,	PUNCT
cet-4972	46	44	2002	2002	NUM
cet-4972	46	45	)	)	PUNCT
cet-4972	46	46	.	.	PUNCT
cet-4972	47	1	in	in	ADP
cet-4972	47	2	this	this	DET
cet-4972	47	3	way	way	NOUN
cet-4972	47	4	,	,	PUNCT
cet-4972	47	5	each	each	DET
cet-4972	47	6	entry	entry	NOUN
cet-4972	47	7	of	of	ADP
cet-4972	47	8	the	the	DET
cet-4972	47	9	similarity	similarity	NOUN
cet-4972	47	10	matrix	matrix	NOUN
cet-4972	47	11	has	have	VERB
cet-4972	47	12	a	a	DET
cet-4972	47	13	value	value	NOUN
cet-4972	47	14	in	in	ADP
cet-4972	47	15	[	[	X
cet-4972	47	16	0,1	0,1	NUM
cet-4972	47	17	]	]	PUNCT
cet-4972	47	18	,	,	PUNCT
cet-4972	47	19	which	which	PRON
cet-4972	47	20	is	be	AUX
cet-4972	47	21	proportional	proportional	ADJ
cet-4972	47	22	to	to	ADP
cet-4972	47	23	how	how	SCONJ
cet-4972	47	24	likely	likely	ADJ
cet-4972	47	25	a	a	DET
cet-4972	47	26	pair	pair	NOUN
cet-4972	47	27	of	of	ADP
cet-4972	47	28	data	datum	NOUN
cet-4972	47	29	is	be	AUX
cet-4972	47	30	,	,	PUNCT
cet-4972	47	31	when	when	SCONJ
cet-4972	47	32	grouped	group	VERB
cet-4972	47	33	together	together	ADV
cet-4972	47	34	.	.	PUNCT
cet-4972	48	1	step	step	VERB
cet-4972	48	2	3	3	NUM
cet-4972	48	3	:	:	PUNCT
cet-4972	48	4	spectral	spectral	ADJ
cet-4972	48	5	clustering	clustering	NOUN
cet-4972	48	6	.	.	PUNCT
cet-4972	49	1	spectral	spectral	ADJ
cet-4972	49	2	clustering	clustering	NOUN
cet-4972	49	3	aims	aim	VERB
cet-4972	49	4	at	at	ADP
cet-4972	49	5	transforming	transform	VERB
cet-4972	49	6	the	the	DET
cet-4972	49	7	similarity	similarity	NOUN
cet-4972	49	8	matrix	matrix	NOUN
cet-4972	49	9	s	s	VERB
cet-4972	49	10	into	into	ADP
cet-4972	49	11	a	a	DET
cet-4972	49	12	normalized	normalize	VERB
cet-4972	49	13	laplacian	laplacian	ADJ
cet-4972	49	14	matrix	matrix	NOUN
cet-4972	49	15	rsl	rsl	NOUN
cet-4972	49	16	.	.	PUNCT
cet-4972	50	1	then	then	ADV
cet-4972	50	2	,	,	PUNCT
cet-4972	50	3	for	for	ADP
cet-4972	50	4	the	the	DET
cet-4972	50	5	obtained	obtain	VERB
cet-4972	50	6	rsl	rsl	NOUN
cet-4972	50	7	,	,	PUNCT
cet-4972	50	8	the	the	DET
cet-4972	50	9	eigenvectors	eigenvector	NOUN
cet-4972	50	10	1	1	NUM
cet-4972	50	11	2	2	NUM
cet-4972	50	12	,	,	PUNCT
cet-4972	50	13	,	,	PUNCT
cet-4972	50	14	...	...	PUNCT
cet-4972	50	15	,	,	PUNCT
cet-4972	50	16	,	,	PUNCT
cet-4972	50	17	...	...	PUNCT
cet-4972	50	18	,	,	PUNCT
cet-4972	50	19	candidate	candidate	NOUN
cet-4972	50	20	ncu	ncu	PROPN
cet-4972	50	21	u	u	PROPN
cet-4972	50	22	u	u	NOUN
cet-4972	50	23	u	u	NOUN
cet-4972	50	24	are	be	AUX
cet-4972	50	25	calculated	calculate	VERB
cet-4972	50	26	corresponding	correspond	VERB
cet-4972	50	27	to	to	ADP
cet-4972	50	28	the	the	DET
cet-4972	50	29	computed	compute	VERB
cet-4972	50	30	eigenvalues	eigenvalue	NOUN
cet-4972	50	31	in	in	ADP
cet-4972	50	32	ascending	ascend	VERB
cet-4972	50	33	order	order	NOUN
cet-4972	50	34	1	1	NUM
cet-4972	50	35	2	2	NUM
cet-4972	50	36	,	,	PUNCT
cet-4972	50	37	,	,	PUNCT
cet-4972	50	38	...	...	PUNCT
cet-4972	50	39	,	,	PUNCT
cet-4972	50	40	,	,	PUNCT
cet-4972	50	41	...	...	PUNCT
cet-4972	50	42	,	,	PUNCT
cet-4972	50	43	candidate	candidate	NOUN
cet-4972	50	44	ncλ	ncλ	NOUN
cet-4972	50	45	λ	λ	NOUN
cet-4972	50	46	λ	λ	X
cet-4972	50	47	λ	λ	X
cet-4972	50	48	,	,	PUNCT
cet-4972	50	49	and	and	CCONJ
cet-4972	50	50	are	be	AUX
cet-4972	50	51	stored	store	VERB
cet-4972	50	52	in	in	ADP
cet-4972	50	53	a	a	DET
cet-4972	50	54	matrix	matrix	NOUN
cet-4972	50	55	u	u	NOUN
cet-4972	50	56	with	with	ADP
cet-4972	50	57	a	a	DET
cet-4972	50	58	size	size	NOUN
cet-4972	50	59	nxn	nxn	PROPN
cet-4972	50	60	,	,	PUNCT
cet-4972	50	61	where	where	SCONJ
cet-4972	50	62	ccandidate=[cmin	ccandidate=[cmin	NOUN
cet-4972	50	63	,	,	PUNCT
cet-4972	50	64	cmax	cmax	NOUN
cet-4972	50	65	]	]	PUNCT
cet-4972	50	66	,	,	PUNCT
cet-4972	50	67	and	and	CCONJ
cet-4972	50	68	cmin	cmin	NOUN
cet-4972	50	69	and	and	CCONJ
cet-4972	50	70	cmax	cmax	NOUN
cet-4972	50	71	are	be	AUX
cet-4972	50	72	the	the	DET
cet-4972	50	73	possible	possible	ADJ
cet-4972	50	74	minimum	minimum	ADJ
cet-4972	50	75	and	and	CCONJ
cet-4972	50	76	maximum	maximum	ADJ
cet-4972	50	77	numbers	number	NOUN
cet-4972	50	78	of	of	ADP
cet-4972	50	79	clusters	cluster	NOUN
cet-4972	50	80	in	in	ADP
cet-4972	50	81	the	the	DET
cet-4972	50	82	final	final	ADJ
cet-4972	50	83	ensemble	ensemble	ADJ
cet-4972	50	84	clustering	clustering	NOUN
cet-4972	50	85	,	,	PUNCT
cet-4972	50	86	respectively	respectively	ADV
cet-4972	50	87	.	.	PUNCT
cet-4972	51	1	step	step	NOUN
cet-4972	51	2	4	4	NUM
cet-4972	51	3	:	:	PUNCT
cet-4972	51	4	clustering	cluster	VERB
cet-4972	51	5	algorithm	algorithm	NOUN
cet-4972	51	6	.	.	PUNCT
cet-4972	52	1	for	for	SCONJ
cet-4972	52	2	each	each	DET
cet-4972	52	3	candidate	candidate	NOUN
cet-4972	52	4	number	number	NOUN
cet-4972	52	5	of	of	ADP
cet-4972	52	6	clusters	cluster	NOUN
cet-4972	52	7	ccandidate	ccandidate	ADJ
cet-4972	52	8	,	,	PUNCT
cet-4972	52	9	the	the	DET
cet-4972	52	10	eigenvectors	eigenvector	NOUN
cet-4972	52	11	1	1	NUM
cet-4972	52	12	2	2	NUM
cet-4972	52	13	,	,	PUNCT
cet-4972	52	14	,	,	PUNCT
cet-4972	52	15	...	...	PUNCT
cet-4972	52	16	,	,	PUNCT
cet-4972	52	17	candidatecu	candidatecu	VERB
cet-4972	52	18	u	u	PRON
cet-4972	52	19	u	u	NOUN
cet-4972	52	20	associated	associate	VERB
cet-4972	52	21	to	to	ADP
cet-4972	52	22	the	the	DET
cet-4972	52	23	ccandidate	ccandidate	ADJ
cet-4972	52	24	smallest	small	ADJ
cet-4972	52	25	eigenvalues	eigenvalue	NOUN
cet-4972	52	26	of	of	ADP
cet-4972	52	27	its	its	PRON
cet-4972	52	28	laplacian	laplacian	ADJ
cet-4972	52	29	matrix	matrix	NOUN
cet-4972	52	30	rsl	rsl	NOUN
cet-4972	52	31	are	be	AUX
cet-4972	52	32	calculated	calculate	VERB
cet-4972	52	33	.	.	PUNCT
cet-4972	53	1	in	in	ADP
cet-4972	53	2	this	this	DET
cet-4972	53	3	way	way	NOUN
cet-4972	53	4	,	,	PUNCT
cet-4972	53	5	the	the	DET
cet-4972	53	6	reduced	reduce	VERB
cet-4972	53	7	matrix	matrix	NOUN
cet-4972	53	8	of	of	ADP
cet-4972	53	9	u	u	NOUN
cet-4972	53	10	with	with	ADP
cet-4972	53	11	a	a	DET
cet-4972	53	12	size	size	NOUN
cet-4972	53	13	nxccandidate	nxccandidate	NOUN
cet-4972	53	14	is	be	AUX
cet-4972	53	15	fed	feed	VERB
cet-4972	53	16	to	to	ADP
cet-4972	53	17	a	a	DET
cet-4972	53	18	clustering	clustering	ADJ
cet-4972	53	19	algorithm	algorithm	NOUN
cet-4972	53	20	to	to	PART
cet-4972	53	21	find	find	VERB
cet-4972	53	22	the	the	DET
cet-4972	53	23	final	final	ADJ
cet-4972	53	24	ensemble	ensemble	ADJ
cet-4972	53	25	clustering	clustering	NOUN
cet-4972	53	26	*	*	PUNCT
cet-4972	53	27	candidatecp	candidatecp	NOUN
cet-4972	53	28	.	.	PUNCT
cet-4972	54	1	in	in	ADP
cet-4972	54	2	this	this	DET
cet-4972	54	3	work	work	NOUN
cet-4972	54	4	,	,	PUNCT
cet-4972	54	5	we	we	PRON
cet-4972	54	6	resort	resort	VERB
cet-4972	54	7	to	to	ADP
cet-4972	54	8	the	the	DET
cet-4972	54	9	k	k	NOUN
cet-4972	54	10	-	-	PUNCT
cet-4972	54	11	means	means	NOUN
cet-4972	54	12	algorithm	algorithm	NOUN
cet-4972	54	13	as	as	ADP
cet-4972	54	14	one	one	NUM
cet-4972	54	15	of	of	ADP
cet-4972	54	16	the	the	DET
cet-4972	54	17	most	most	ADV
cet-4972	54	18	popular	popular	ADJ
cet-4972	54	19	clustering	clustering	ADJ
cet-4972	54	20	methods	method	NOUN
cet-4972	54	21	(	(	PUNCT
cet-4972	54	22	su	su	NOUN
cet-4972	54	23	and	and	CCONJ
cet-4972	54	24	chou	chou	NOUN
cet-4972	54	25	,	,	PUNCT
cet-4972	54	26	2001	2001	NUM
cet-4972	54	27	;	;	PUNCT
cet-4972	54	28	fern	fern	NOUN
cet-4972	54	29	and	and	CCONJ
cet-4972	54	30	lin	lin	PROPN
cet-4972	54	31	,	,	PUNCT
cet-4972	54	32	2008	2008	NUM
cet-4972	54	33	)	)	PUNCT
cet-4972	54	34	.	.	PUNCT
cet-4972	55	1	step	step	NOUN
cet-4972	55	2	5	5	NUM
cet-4972	55	3	:	:	PUNCT
cet-4972	55	4	final	final	ADJ
cet-4972	55	5	ensemble	ensemble	ADJ
cet-4972	55	6	clustering	clustering	NOUN
cet-4972	55	7	selection	selection	NOUN
cet-4972	55	8	.	.	PUNCT
cet-4972	56	1	for	for	ADP
cet-4972	56	2	each	each	DET
cet-4972	56	3	ccandidate	ccandidate	NOUN
cet-4972	56	4	the	the	DET
cet-4972	56	5	obtained	obtain	VERB
cet-4972	56	6	ensemble	ensemble	ADJ
cet-4972	56	7	clustering	clustering	NOUN
cet-4972	56	8	*	*	PUNCT
cet-4972	56	9	candidatecp	candidatecp	NOUN
cet-4972	56	10	is	be	AUX
cet-4972	56	11	evaluated	evaluate	VERB
cet-4972	56	12	by	by	ADP
cet-4972	56	13	computing	compute	VERB
cet-4972	56	14	its	its	PRON
cet-4972	56	15	silhouette	silhouette	NOUN
cet-4972	56	16	validity	validity	NOUN
cet-4972	56	17	index	index	NOUN
cet-4972	56	18	candidatecsv	candidatecsv	PROPN
cet-4972	56	19	(	(	PUNCT
cet-4972	56	20	rousseeuw	rousseeuw	PROPN
cet-4972	56	21	,	,	PUNCT
cet-4972	56	22	1987	1987	NUM
cet-4972	56	23	)	)	PUNCT
cet-4972	56	24	.	.	PUNCT
cet-4972	57	1	the	the	DET
cet-4972	57	2	most	most	ADV
cet-4972	57	3	appropriate	appropriate	ADJ
cet-4972	57	4	ensemble	ensemble	ADJ
cet-4972	57	5	clustering	clustering	NOUN
cet-4972	57	6	*	*	PUNCT
cet-4972	57	7	*	*	PUNCT
cet-4972	57	8	c	c	PRON
cet-4972	57	9	p	p	NOUN
cet-4972	57	10	is	be	AUX
cet-4972	57	11	the	the	DET
cet-4972	57	12	one	one	NUM
cet-4972	57	13	for	for	ADP
cet-4972	57	14	which	which	PRON
cet-4972	57	15	the	the	DET
cet-4972	57	16	silhouette	silhouette	NOUN
cet-4972	57	17	reaches	reach	VERB
cet-4972	57	18	a	a	DET
cet-4972	57	19	maximum	maximum	ADJ
cet-4972	57	20	,	,	PUNCT
cet-4972	57	21	i.e.	i.e.	X
cet-4972	57	22	,	,	PUNCT
cet-4972	57	23	clusters	cluster	NOUN
cet-4972	57	24	are	be	AUX
cet-4972	57	25	well	well	ADV
cet-4972	57	26	separated	separate	VERB
cet-4972	57	27	and	and	CCONJ
cet-4972	57	28	compacted	compact	VERB
cet-4972	57	29	(	(	PUNCT
cet-4972	57	30	rousseeuw	rousseeuw	PROPN
cet-4972	57	31	,	,	PUNCT
cet-4972	57	32	1987	1987	NUM
cet-4972	57	33	)	)	PUNCT
cet-4972	57	34	.	.	PUNCT
cet-4972	58	1	original	original	ADJ
cet-4972	58	2	dataset	dataset	NOUN
cet-4972	58	3	matrix	matrix	NOUN
cet-4972	58	4	(	(	PUNCT
cet-4972	58	5	n	n	CCONJ
cet-4972	58	6	data	data	NOUN
cet-4972	58	7	)	)	PUNCT
cet-4972	58	8	similarity	similarity	NOUN
cet-4972	58	9	matrix	matrix	NOUN
cet-4972	58	10	computation	computation	NOUN
cet-4972	58	11	,	,	PUNCT
cet-4972	58	12	adjacency	adjacency	NOUN
cet-4972	58	13	matrix	matrix	NOUN
cet-4972	58	14	computation	computation	NOUN
cet-4972	58	15	,	,	PUNCT
cet-4972	58	16	clustering	cluster	VERB
cet-4972	58	17	algorithm	algorithm	NOUN
cet-4972	58	18	(	(	PUNCT
cet-4972	58	19	h	h	NOUN
cet-4972	58	20	base	base	PROPN
cet-4972	58	21	clusterings	clusterings	PROPN
cet-4972	58	22	)	)	PUNCT
cet-4972	58	23	j	j	PROPN
cet-4972	58	24	..	..	PUNCT
cet-4972	58	25	..	..	PUNCT
cet-4972	59	1	1	1	NUM
cet-4972	59	2	2	2	NUM
cet-4972	59	3	h	h	NOUN
cet-4972	59	4	n	n	NOUN
cet-4972	59	5	x	x	NOUN
cet-4972	59	6	h1	h1	VERB
cet-4972	59	7	optc	optc	ADJ
cet-4972	59	8	2	2	NUM
cet-4972	59	9	optc	optc	ADJ
cet-4972	59	10	j	j	PROPN
cet-4972	59	11	optc	optc	PROPN
cet-4972	59	12	h	h	PROPN
cet-4972	59	13	optc	optc	ADV
cet-4972	59	14	....	....	PUNCT
cet-4972	60	1	labels	label	NOUN
cet-4972	60	2	matrix	matrix	NOUN
cet-4972	60	3	y	y	PROPN
cet-4972	60	4	ensemble	ensemble	ADJ
cet-4972	60	5	clustering	clustering	NOUN
cet-4972	60	6	cspa	cspa	NOUN
cet-4972	60	7	procedure	procedure	NOUN
cet-4972	60	8	n	n	NOUN
cet-4972	60	9	x	x	SYM
cet-4972	60	10	n	n	PROPN
cet-4972	60	11	..	..	PUNCT
cet-4972	60	12	1	1	NUM
cet-4972	60	13	2	2	NUM
cet-4972	60	14	i	i	NOUN
cet-4972	60	15	n	n	PROPN
cet-4972	60	16	..	..	PUNCT
cet-4972	60	17	..	..	PUNCT
cet-4972	61	1	1u	1u	NUM
cet-4972	61	2	2u	2u	PROPN
cet-4972	61	3	candidatecu	candidatecu	PROPN
cet-4972	61	4	nu	nu	PROPN
cet-4972	61	5	..	..	PROPN
cet-4972	61	6	n	n	CCONJ
cet-4972	61	7	x	x	SYM
cet-4972	61	8	1	1	NUM
cet-4972	61	9	candidatec	candidatec	ADJ
cet-4972	61	10	*	*	PUNCT
cet-4972	61	11	candidatecp	candidatecp	NOUN
cet-4972	61	12	novel	novel	VERB
cet-4972	61	13	ensemble	ensemble	ADJ
cet-4972	61	14	procedure	procedure	NOUN
cet-4972	61	15	n	n	NOUN
cet-4972	61	16	x	x	SYM
cet-4972	61	17	1	1	X
cet-4972	61	18	*	*	PUNCT
cet-4972	61	19	c	c	NOUN
cet-4972	61	20	*	*	PUNCT
cet-4972	61	21	*	*	PUNCT
cet-4972	61	22	c	c	X
cet-4972	61	23	p	p	PRON
cet-4972	61	24	final	final	ADJ
cet-4972	61	25	ensemble	ensemble	ADJ
cet-4972	61	26	clustering	clustering	NOUN
cet-4972	61	27	a	a	DET
cet-4972	61	28	s	s	NOUN
cet-4972	61	29	x	x	NOUN
cet-4972	61	30	computation	computation	NOUN
cet-4972	61	31	of	of	ADP
cet-4972	61	32	the	the	DET
cet-4972	61	33	eigenvectors	eigenvector	NOUN
cet-4972	61	34	,	,	PUNCT
cet-4972	61	35	of	of	ADP
cet-4972	61	36	the	the	DET
cet-4972	61	37	normalized	normalize	VERB
cet-4972	61	38	laplacian	laplacian	ADJ
cet-4972	61	39	matrix	matrix	NOUN
cet-4972	61	40	,	,	PUNCT
cet-4972	61	41	(	(	PUNCT
cet-4972	61	42	spectral	spectral	ADJ
cet-4972	61	43	clustering	clustering	NOUN
cet-4972	61	44	)	)	PUNCT
cet-4972	61	45	u	u	NOUN
cet-4972	61	46	ccandidate	ccandidate	ADJ
cet-4972	61	47	ϵ	ϵ	X
cet-4972	62	1	[	[	X
cet-4972	62	2	cmin	cmin	NOUN
cet-4972	62	3	,	,	PUNCT
cet-4972	62	4	cmax	cmax	NOUN
cet-4972	62	5	]	]	PUNCT
cet-4972	62	6	clustering	cluster	VERB
cet-4972	62	7	algorithm	algorithm	NOUN
cet-4972	62	8	k	k	NOUN
cet-4972	62	9	-	-	PUNCT
cet-4972	62	10	means	means	NOUN
cet-4972	62	11	silhouette	silhouette	NOUN
cet-4972	62	12	validity	validity	NOUN
cet-4972	62	13	index	index	NOUN
cet-4972	62	14	computation	computation	NOUN
cet-4972	62	15	,	,	PUNCT
cet-4972	62	16	sv	sv	AUX
cet-4972	62	17	ccandidate	ccandidate	PROPN
cet-4972	62	18	determine	determine	VERB
cet-4972	62	19	c	c	NOUN
cet-4972	62	20	*	*	PUNCT
cet-4972	62	21	at	at	ADP
cet-4972	62	22	max	max	PROPN
cet-4972	62	23	sv	sv	PROPN
cet-4972	62	24	1	1	NUM
cet-4972	62	25	2	2	NUM
cet-4972	62	26	i	i	NOUN
cet-4972	62	27	n	n	PROPN
cet-4972	62	28	..	..	PUNCT
cet-4972	62	29	..	..	PUNCT
cet-4972	63	1	1	1	NUM
cet-4972	63	2	2	2	NUM
cet-4972	63	3	i	i	NOUN
cet-4972	63	4	n	n	PROPN
cet-4972	63	5	..	..	PUNCT
cet-4972	63	6	..	..	PUNCT
cet-4972	64	1	1	1	NUM
cet-4972	64	2	2	2	NUM
cet-4972	64	3	i	i	NOUN
cet-4972	64	4	n	n	PROPN
cet-4972	64	5	..	..	PUNCT
cet-4972	64	6	..	..	PUNCT
cet-4972	65	1	1227	1227	NUM
cet-4972	65	2	3	3	X
cet-4972	65	3	.	.	PUNCT
cet-4972	65	4	artificial	artificial	ADJ
cet-4972	65	5	case	case	NOUN
cet-4972	65	6	study	study	VERB
cet-4972	65	7	an	an	DET
cet-4972	65	8	artificial	artificial	ADJ
cet-4972	65	9	case	case	NOUN
cet-4972	65	10	study	study	NOUN
cet-4972	65	11	has	have	AUX
cet-4972	65	12	been	be	AUX
cet-4972	65	13	designed	design	VERB
cet-4972	65	14	to	to	PART
cet-4972	65	15	generate	generate	VERB
cet-4972	65	16	n=149	n=149	NOUN
cet-4972	65	17	data	datum	NOUN
cet-4972	65	18	representative	representative	NOUN
cet-4972	65	19	of	of	ADP
cet-4972	65	20	the	the	DET
cet-4972	65	21	signal	signal	ADJ
cet-4972	65	22	trend	trend	NOUN
cet-4972	65	23	behavior	behavior	NOUN
cet-4972	65	24	of	of	ADP
cet-4972	65	25	an	an	DET
cet-4972	65	26	industrial	industrial	ADJ
cet-4972	65	27	equipment	equipment	NOUN
cet-4972	65	28	,	,	PUNCT
cet-4972	65	29	e.g.	e.g.	ADV
cet-4972	65	30	,	,	PUNCT
cet-4972	65	31	a	a	DET
cet-4972	65	32	rotating	rotate	VERB
cet-4972	65	33	machine	machine	NOUN
cet-4972	65	34	,	,	PUNCT
cet-4972	65	35	during	during	ADP
cet-4972	65	36	shut	shut	NOUN
cet-4972	65	37	-	-	PUNCT
cet-4972	65	38	down	down	ADP
cet-4972	65	39	operations	operation	NOUN
cet-4972	65	40	.	.	PUNCT
cet-4972	66	1	each	each	DET
cet-4972	66	2	datum	datum	NOUN
cet-4972	66	3	is	be	AUX
cet-4972	66	4	described	describe	VERB
cet-4972	66	5	by	by	ADP
cet-4972	66	6	h=3	h=3	NOUN
cet-4972	66	7	features	feature	NOUN
cet-4972	66	8	representative	representative	NOUN
cet-4972	66	9	of	of	ADP
cet-4972	66	10	the	the	DET
cet-4972	66	11	equipment	equipment	NOUN
cet-4972	66	12	condition	condition	NOUN
cet-4972	66	13	,	,	PUNCT
cet-4972	66	14	e.g.	e.g.	ADV
cet-4972	66	15	,	,	PUNCT
cet-4972	66	16	vibration	vibration	NOUN
cet-4972	66	17	signals	signal	NOUN
cet-4972	66	18	,	,	PUNCT
cet-4972	66	19	and	and	CCONJ
cet-4972	66	20	of	of	ADP
cet-4972	66	21	the	the	DET
cet-4972	66	22	environmental	environmental	ADJ
cet-4972	66	23	and	and	CCONJ
cet-4972	66	24	operational	operational	ADJ
cet-4972	66	25	conditions	condition	NOUN
cet-4972	66	26	that	that	PRON
cet-4972	66	27	can	can	AUX
cet-4972	66	28	influence	influence	VERB
cet-4972	66	29	the	the	DET
cet-4972	66	30	equipment	equipment	NOUN
cet-4972	66	31	behavior	behavior	NOUN
cet-4972	66	32	,	,	PUNCT
cet-4972	66	33	e.g.	e.g.	ADV
cet-4972	66	34	,	,	PUNCT
cet-4972	66	35	vacuum	vacuum	NOUN
cet-4972	66	36	and	and	CCONJ
cet-4972	66	37	temperature	temperature	NOUN
cet-4972	66	38	signals	signal	NOUN
cet-4972	66	39	.	.	PUNCT
cet-4972	67	1	these	these	DET
cet-4972	67	2	data	datum	NOUN
cet-4972	67	3	are	be	AUX
cet-4972	67	4	stored	store	VERB
cet-4972	67	5	in	in	ADP
cet-4972	67	6	a	a	DET
cet-4972	67	7	matrix	matrix	NOUN
cet-4972	67	8	x	x	PUNCT
cet-4972	67	9	of	of	ADP
cet-4972	67	10	a	a	DET
cet-4972	67	11	size	size	NOUN
cet-4972	67	12	149x3	149x3	NUM
cet-4972	67	13	.	.	PUNCT
cet-4972	68	1	the	the	DET
cet-4972	68	2	objective	objective	NOUN
cet-4972	68	3	is	be	AUX
cet-4972	68	4	to	to	PART
cet-4972	68	5	reveal	reveal	VERB
cet-4972	68	6	the	the	DET
cet-4972	68	7	“	"	PUNCT
cet-4972	68	8	hidden	hidden	ADJ
cet-4972	68	9	”	"	PUNCT
cet-4972	68	10	structure	structure	NOUN
cet-4972	68	11	p	p	NOUN
cet-4972	68	12	*	*	PUNCT
cet-4972	68	13	of	of	ADP
cet-4972	68	14	the	the	DET
cet-4972	68	15	dataset	dataset	NOUN
cet-4972	68	16	x	x	PUNCT
cet-4972	68	17	by	by	ADP
cet-4972	68	18	identifying	identify	VERB
cet-4972	68	19	groups	group	NOUN
cet-4972	68	20	of	of	ADP
cet-4972	68	21	data	datum	NOUN
cet-4972	68	22	with	with	ADP
cet-4972	68	23	similar	similar	ADJ
cet-4972	68	24	functional	functional	ADJ
cet-4972	68	25	behaviors	behavior	NOUN
cet-4972	68	26	,	,	PUNCT
cet-4972	68	27	representative	representative	NOUN
cet-4972	68	28	of	of	ADP
cet-4972	68	29	different	different	ADJ
cet-4972	68	30	operational	operational	ADJ
cet-4972	68	31	conditions	condition	NOUN
cet-4972	68	32	of	of	ADP
cet-4972	68	33	the	the	DET
cet-4972	68	34	equipment	equipment	NOUN
cet-4972	68	35	.	.	PUNCT
cet-4972	69	1	without	without	ADP
cet-4972	69	2	loss	loss	NOUN
cet-4972	69	3	of	of	ADP
cet-4972	69	4	generality	generality	NOUN
cet-4972	69	5	,	,	PUNCT
cet-4972	69	6	it	it	PRON
cet-4972	69	7	is	be	AUX
cet-4972	69	8	assumed	assume	VERB
cet-4972	69	9	that	that	SCONJ
cet-4972	69	10	the	the	DET
cet-4972	69	11	operational	operational	ADJ
cet-4972	69	12	conditions	condition	NOUN
cet-4972	69	13	of	of	ADP
cet-4972	69	14	the	the	DET
cet-4972	69	15	industrial	industrial	ADJ
cet-4972	69	16	equipment	equipment	NOUN
cet-4972	69	17	are	be	AUX
cet-4972	69	18	m=7	m=7	NOUN
cet-4972	69	19	:	:	PUNCT
cet-4972	69	20	1	1	X
cet-4972	69	21	)	)	PUNCT
cet-4972	69	22	three	three	NUM
cet-4972	69	23	classes	class	NOUN
cet-4972	69	24	of	of	ADP
cet-4972	69	25	normal	normal	ADJ
cet-4972	69	26	condition	condition	NOUN
cet-4972	69	27	(	(	PUNCT
cet-4972	69	28	nc1	nc1	PROPN
cet-4972	69	29	,	,	PUNCT
cet-4972	69	30	nc2	nc2	NOUN
cet-4972	69	31	,	,	PUNCT
cet-4972	69	32	nc3	nc3	PROPN
cet-4972	69	33	)	)	PUNCT
cet-4972	69	34	,	,	PUNCT
cet-4972	69	35	2	2	X
cet-4972	69	36	)	)	PUNCT
cet-4972	69	37	three	three	NUM
cet-4972	69	38	classes	class	NOUN
cet-4972	69	39	of	of	ADP
cet-4972	69	40	abnormal	abnormal	ADJ
cet-4972	69	41	condition	condition	NOUN
cet-4972	69	42	(	(	PUNCT
cet-4972	69	43	ac1	ac1	PROPN
cet-4972	69	44	,	,	PUNCT
cet-4972	69	45	ac2	ac2	PROPN
cet-4972	69	46	,	,	PUNCT
cet-4972	69	47	ac3	ac3	PROPN
cet-4972	69	48	)	)	PUNCT
cet-4972	69	49	,	,	PUNCT
cet-4972	69	50	and	and	CCONJ
cet-4972	69	51	3	3	X
cet-4972	69	52	)	)	PUNCT
cet-4972	69	53	one	one	NUM
cet-4972	69	54	class	class	NOUN
cet-4972	69	55	of	of	ADP
cet-4972	69	56	outliers	outlier	NOUN
cet-4972	69	57	(	(	PUNCT
cet-4972	69	58	i.e.	i.e.	X
cet-4972	69	59	,	,	PUNCT
cet-4972	69	60	unknown	unknown	ADJ
cet-4972	69	61	behaviours	behaviour	NOUN
cet-4972	69	62	)	)	PUNCT
cet-4972	69	63	.	.	PUNCT
cet-4972	70	1	the	the	DET
cet-4972	70	2	dataset	dataset	NOUN
cet-4972	70	3	x	x	VERB
cet-4972	70	4	is	be	AUX
cet-4972	70	5	pictorially	pictorially	ADV
cet-4972	70	6	shown	show	VERB
cet-4972	70	7	in	in	ADP
cet-4972	70	8	figure	figure	NOUN
cet-4972	70	9	3	3	NUM
cet-4972	70	10	.	.	PUNCT
cet-4972	71	1	the	the	DET
cet-4972	71	2	values	value	NOUN
cet-4972	71	3	of	of	ADP
cet-4972	71	4	each	each	DET
cet-4972	71	5	j	j	PROPN
cet-4972	71	6	-	-	PUNCT
cet-4972	71	7	th	th	VERB
cet-4972	71	8	feature	feature	NOUN
cet-4972	71	9	jx	jx	PROPN
cet-4972	71	10	,	,	PUNCT
cet-4972	71	11	j=1,2,3	j=1,2,3	PROPN
cet-4972	71	12	,	,	PUNCT
cet-4972	71	13	for	for	ADP
cet-4972	71	14	different	different	ADJ
cet-4972	71	15	classes	class	NOUN
cet-4972	71	16	of	of	ADP
cet-4972	71	17	data	datum	NOUN
cet-4972	71	18	have	have	AUX
cet-4972	71	19	been	be	AUX
cet-4972	71	20	created	create	VERB
cet-4972	71	21	by	by	ADP
cet-4972	71	22	randomly	randomly	ADV
cet-4972	71	23	sampling	sample	VERB
cet-4972	71	24	their	their	PRON
cet-4972	71	25	realization	realization	NOUN
cet-4972	71	26	from	from	ADP
cet-4972	71	27	different	different	ADJ
cet-4972	71	28	univariate	univariate	ADJ
cet-4972	71	29	distribution	distribution	NOUN
cet-4972	71	30	functions	function	NOUN
cet-4972	71	31	(	(	PUNCT
cet-4972	71	32	1	1	NUM
cet-4972	71	33	to	to	PART
cet-4972	71	34	10	10	NUM
cet-4972	71	35	in	in	ADP
cet-4972	71	36	figure	figure	NOUN
cet-4972	71	37	3	3	NUM
cet-4972	71	38	)	)	PUNCT
cet-4972	71	39	whose	whose	DET
cet-4972	71	40	combination	combination	NOUN
cet-4972	71	41	characterizes	characterize	VERB
cet-4972	71	42	the	the	DET
cet-4972	71	43	class	class	NOUN
cet-4972	71	44	.	.	PUNCT
cet-4972	72	1	figure	figure	NOUN
cet-4972	72	2	3	3	NUM
cet-4972	72	3	:	:	PUNCT
cet-4972	72	4	the	the	DET
cet-4972	72	5	seven	seven	NUM
cet-4972	72	6	operational	operational	ADJ
cet-4972	72	7	conditions	condition	NOUN
cet-4972	72	8	of	of	ADP
cet-4972	72	9	the	the	DET
cet-4972	72	10	artificial	artificial	ADJ
cet-4972	72	11	case	case	NOUN
cet-4972	72	12	study	study	NOUN
cet-4972	72	13	as	as	SCONJ
cet-4972	72	14	shown	show	VERB
cet-4972	72	15	in	in	ADP
cet-4972	72	16	figure	figure	NOUN
cet-4972	72	17	3	3	NUM
cet-4972	72	18	,	,	PUNCT
cet-4972	72	19	clustering	cluster	VERB
cet-4972	72	20	each	each	DET
cet-4972	72	21	j	j	NOUN
cet-4972	72	22	-	-	PUNCT
cet-4972	72	23	th	th	VERB
cet-4972	72	24	feature	feature	NOUN
cet-4972	72	25	independently	independently	ADV
cet-4972	72	26	may	may	AUX
cet-4972	72	27	reveal	reveal	VERB
cet-4972	72	28	only	only	ADV
cet-4972	72	29	some	some	DET
cet-4972	72	30	groups	group	NOUN
cet-4972	72	31	of	of	ADP
cet-4972	72	32	the	the	DET
cet-4972	72	33	“	"	PUNCT
cet-4972	72	34	hidden	hidden	ADJ
cet-4972	72	35	”	"	PUNCT
cet-4972	72	36	operational	operational	ADJ
cet-4972	72	37	conditions	condition	NOUN
cet-4972	72	38	of	of	ADP
cet-4972	72	39	the	the	DET
cet-4972	72	40	industrial	industrial	ADJ
cet-4972	72	41	equipment	equipment	NOUN
cet-4972	72	42	indicated	indicate	VERB
cet-4972	72	43	in	in	ADP
cet-4972	72	44	figure	figure	NOUN
cet-4972	72	45	3	3	NUM
cet-4972	72	46	,	,	PUNCT
cet-4972	72	47	whereas	whereas	SCONJ
cet-4972	72	48	only	only	ADV
cet-4972	72	49	a	a	DET
cet-4972	72	50	final	final	ADJ
cet-4972	72	51	ensemble	ensemble	ADJ
cet-4972	72	52	clustering	clustering	NOUN
cet-4972	72	53	would	would	AUX
cet-4972	72	54	enlighten	enlighten	VERB
cet-4972	72	55	all	all	DET
cet-4972	72	56	the	the	DET
cet-4972	72	57	m=7	m=7	PROPN
cet-4972	72	58	clusters	cluster	NOUN
cet-4972	72	59	.	.	PUNCT
cet-4972	73	1	for	for	ADP
cet-4972	73	2	example	example	NOUN
cet-4972	73	3	,	,	PUNCT
cet-4972	73	4	clustering	cluster	VERB
cet-4972	73	5	the	the	DET
cet-4972	73	6	feature	feature	NOUN
cet-4972	73	7	j=1	j=1	NOUN
cet-4972	73	8	can	can	AUX
cet-4972	73	9	not	not	PART
cet-4972	73	10	reveal	reveal	VERB
cet-4972	73	11	any	any	DET
cet-4972	73	12	abnormal	abnormal	ADJ
cet-4972	73	13	operational	operational	ADJ
cet-4972	73	14	condition	condition	NOUN
cet-4972	73	15	.	.	PUNCT
cet-4972	74	1	the	the	DET
cet-4972	74	2	objective	objective	NOUN
cet-4972	74	3	is	be	AUX
cet-4972	74	4	,	,	PUNCT
cet-4972	74	5	thus	thus	ADV
cet-4972	74	6	,	,	PUNCT
cet-4972	74	7	to	to	PART
cet-4972	74	8	aggregate	aggregate	VERB
cet-4972	74	9	these	these	DET
cet-4972	74	10	base	base	NOUN
cet-4972	74	11	clusterings	clustering	NOUN
cet-4972	74	12	into	into	ADP
cet-4972	74	13	a	a	DET
cet-4972	74	14	final	final	ADJ
cet-4972	74	15	ensemble	ensemble	ADJ
cet-4972	74	16	clustering	clustering	NOUN
cet-4972	74	17	p	p	X
cet-4972	74	18	*	*	PUNCT
cet-4972	74	19	capable	capable	ADJ
cet-4972	74	20	of	of	ADP
cet-4972	74	21	identifying	identify	VERB
cet-4972	74	22	the	the	DET
cet-4972	74	23	“	"	PUNCT
cet-4972	74	24	true	true	ADJ
cet-4972	74	25	”	"	PUNCT
cet-4972	74	26	grouping	grouping	NOUN
cet-4972	74	27	of	of	ADP
cet-4972	74	28	the	the	DET
cet-4972	74	29	shut	shut	VERB
cet-4972	74	30	-	-	PUNCT
cet-4972	74	31	down	down	ADP
cet-4972	74	32	transients	transient	NOUN
cet-4972	74	33	of	of	ADP
cet-4972	74	34	the	the	DET
cet-4972	74	35	industrial	industrial	ADJ
cet-4972	74	36	equipment	equipment	NOUN
cet-4972	74	37	.	.	PUNCT
cet-4972	75	1	to	to	PART
cet-4972	75	2	mine	mine	VERB
cet-4972	75	3	the	the	DET
cet-4972	75	4	clusters	cluster	NOUN
cet-4972	75	5	shown	show	VERB
cet-4972	75	6	in	in	ADP
cet-4972	75	7	figure	figure	NOUN
cet-4972	75	8	3	3	NUM
cet-4972	75	9	,	,	PUNCT
cet-4972	75	10	the	the	DET
cet-4972	75	11	j	j	PROPN
cet-4972	75	12	-	-	PUNCT
cet-4972	75	13	th	th	VERB
cet-4972	75	14	base	base	NOUN
cet-4972	75	15	clustering	cluster	VERB
cet-4972	75	16	outcomes	outcome	NOUN
cet-4972	75	17	are	be	AUX
cet-4972	75	18	obtained	obtain	VERB
cet-4972	75	19	by	by	ADP
cet-4972	75	20	a	a	DET
cet-4972	75	21	k	k	NOUN
cet-4972	75	22	-	-	PUNCT
cet-4972	75	23	means	mean	VERB
cet-4972	75	24	unsupervised	unsupervised	ADJ
cet-4972	75	25	learning	learn	VERB
cet-4972	75	26	algorithm	algorithm	NOUN
cet-4972	75	27	(	(	PUNCT
cet-4972	75	28	vlachos	vlachos	PROPN
cet-4972	75	29	et	et	PROPN
cet-4972	75	30	al	al	PROPN
cet-4972	75	31	.	.	PROPN
cet-4972	75	32	2003	2003	NUM
cet-4972	75	33	)	)	PUNCT
cet-4972	75	34	.	.	PUNCT
cet-4972	76	1	for	for	ADP
cet-4972	76	2	identifying	identify	VERB
cet-4972	76	3	the	the	DET
cet-4972	76	4	correct	correct	ADJ
cet-4972	76	5	number	number	NOUN
cet-4972	76	6	of	of	ADP
cet-4972	76	7	clusters	cluster	NOUN
cet-4972	76	8	j	j	PROPN
cet-4972	76	9	optc	optc	ADV
cet-4972	76	10	for	for	ADP
cet-4972	76	11	each	each	DET
cet-4972	76	12	base	base	NOUN
cet-4972	76	13	clustering	clustering	NOUN
cet-4972	76	14	,	,	PUNCT
cet-4972	76	15	davies	davy	NOUN
cet-4972	76	16	-	-	PUNCT
cet-4972	76	17	bouldin	bouldin	NOUN
cet-4972	76	18	(	(	PUNCT
cet-4972	76	19	db	db	NOUN
cet-4972	76	20	)	)	PUNCT
cet-4972	76	21	validity	validity	NOUN
cet-4972	76	22	criterion	criterion	NOUN
cet-4972	76	23	has	have	AUX
cet-4972	76	24	been	be	AUX
cet-4972	76	25	used	use	VERB
cet-4972	76	26	(	(	PUNCT
cet-4972	76	27	davies	davy	NOUN
cet-4972	76	28	and	and	CCONJ
cet-4972	76	29	bouldin	bouldin	NOUN
cet-4972	76	30	,	,	PUNCT
cet-4972	76	31	1979	1979	NUM
cet-4972	76	32	):	):	PUNCT
cet-4972	76	33	the	the	DET
cet-4972	76	34	minimum	minimum	NOUN
cet-4972	76	35	db	db	PROPN
cet-4972	76	36	value	value	NOUN
cet-4972	76	37	is	be	AUX
cet-4972	76	38	reached	reach	VERB
cet-4972	76	39	for	for	ADP
cet-4972	76	40	the	the	DET
cet-4972	76	41	number	number	NOUN
cet-4972	76	42	of	of	ADP
cet-4972	76	43	clusters	cluster	NOUN
cet-4972	76	44	which	which	PRON
cet-4972	76	45	gives	give	VERB
cet-4972	76	46	optimal	optimal	ADJ
cet-4972	76	47	separation	separation	NOUN
cet-4972	76	48	and	and	CCONJ
cet-4972	76	49	compactness	compactness	NOUN
cet-4972	76	50	(	(	PUNCT
cet-4972	76	51	davies	davy	NOUN
cet-4972	76	52	and	and	CCONJ
cet-4972	76	53	bouldin	bouldin	NOUN
cet-4972	76	54	,	,	PUNCT
cet-4972	76	55	1979	1979	NUM
cet-4972	76	56	)	)	PUNCT
cet-4972	76	57	.	.	PUNCT
cet-4972	77	1	table	table	NOUN
cet-4972	77	2	1	1	NUM
cet-4972	77	3	reports	report	VERB
cet-4972	77	4	the	the	DET
cet-4972	77	5	optimum	optimum	ADJ
cet-4972	77	6	number	number	NOUN
cet-4972	77	7	of	of	ADP
cet-4972	77	8	clusters	cluster	NOUN
cet-4972	77	9	j	j	PROPN
cet-4972	77	10	optc	optc	PROPN
cet-4972	77	11	obtained	obtain	VERB
cet-4972	77	12	for	for	ADP
cet-4972	77	13	each	each	DET
cet-4972	77	14	base	base	NOUN
cet-4972	77	15	clustering	cluster	VERB
cet-4972	77	16	.	.	PUNCT
cet-4972	78	1	for	for	ADP
cet-4972	78	2	validation	validation	NOUN
cet-4972	78	3	of	of	ADP
cet-4972	78	4	the	the	DET
cet-4972	78	5	db	db	PROPN
cet-4972	78	6	validity	validity	NOUN
cet-4972	78	7	criterion	criterion	NOUN
cet-4972	78	8	to	to	PART
cet-4972	78	9	decide	decide	VERB
cet-4972	78	10	j	j	PROPN
cet-4972	78	11	optc	optc	ADV
cet-4972	78	12	,	,	PUNCT
cet-4972	78	13	we	we	PRON
cet-4972	78	14	use	use	VERB
cet-4972	78	15	the	the	DET
cet-4972	78	16	information	information	NOUN
cet-4972	78	17	on	on	ADP
cet-4972	78	18	the	the	DET
cet-4972	78	19	real	real	ADJ
cet-4972	78	20	classes	class	NOUN
cet-4972	78	21	to	to	PART
cet-4972	78	22	which	which	PRON
cet-4972	78	23	the	the	DET
cet-4972	78	24	data	datum	NOUN
cet-4972	78	25	belong	belong	VERB
cet-4972	78	26	,	,	PUNCT
cet-4972	78	27	to	to	PART
cet-4972	78	28	calculate	calculate	VERB
cet-4972	78	29	the	the	DET
cet-4972	78	30	misclassification	misclassification	NOUN
cet-4972	78	31	rate	rate	NOUN
cet-4972	78	32	(	(	PUNCT
cet-4972	78	33	table	table	NOUN
cet-4972	78	34	1	1	NUM
cet-4972	78	35	)	)	PUNCT
cet-4972	78	36	(	(	PUNCT
cet-4972	78	37	it	it	PRON
cet-4972	78	38	is	be	AUX
cet-4972	78	39	worth	worth	ADJ
cet-4972	78	40	noticing	notice	VERB
cet-4972	78	41	that	that	SCONJ
cet-4972	78	42	in	in	ADP
cet-4972	78	43	real	real	ADJ
cet-4972	78	44	industrial	industrial	ADJ
cet-4972	78	45	applications	application	NOUN
cet-4972	78	46	the	the	DET
cet-4972	78	47	real	real	ADJ
cet-4972	78	48	class	class	NOUN
cet-4972	78	49	is	be	AUX
cet-4972	78	50	unknown	unknown	ADJ
cet-4972	78	51	)	)	PUNCT
cet-4972	78	52	.	.	PUNCT
cet-4972	79	1	table	table	NOUN
cet-4972	79	2	1	1	NUM
cet-4972	79	3	:	:	PUNCT
cet-4972	80	1	optimum	optimum	ADJ
cet-4972	80	2	numbers	number	NOUN
cet-4972	80	3	of	of	ADP
cet-4972	80	4	clusters	cluster	NOUN
cet-4972	80	5	and	and	CCONJ
cet-4972	80	6	misclassification	misclassification	NOUN
cet-4972	80	7	rates	rate	NOUN
cet-4972	80	8	of	of	ADP
cet-4972	80	9	clustering	cluster	VERB
cet-4972	80	10	for	for	ADP
cet-4972	80	11	the	the	DET
cet-4972	80	12	three	three	NUM
cet-4972	80	13	features	feature	NOUN
cet-4972	80	14	features	feature	VERB
cet-4972	80	15	j	j	PROPN
cet-4972	80	16	optc	optc	ADJ
cet-4972	80	17	misclassification	misclassification	NOUN
cet-4972	80	18	rate	rate	NOUN
cet-4972	80	19	j=1	j=1	NOUN
cet-4972	80	20	2	2	NUM
cet-4972	80	21	7.4	7.4	NUM
cet-4972	80	22	%	%	NOUN
cet-4972	80	23	j=2	j=2	NOUN
cet-4972	80	24	2	2	NUM
cet-4972	80	25	4.6	4.6	NUM
cet-4972	80	26	%	%	NOUN
cet-4972	80	27	j=3	j=3	NUM
cet-4972	80	28	4	4	NUM
cet-4972	80	29	6.8	6.8	NUM
cet-4972	80	30	%	%	NOUN
cet-4972	80	31	the	the	DET
cet-4972	80	32	obtained	obtain	VERB
cet-4972	80	33	base	base	NOUN
cet-4972	80	34	clustering	cluster	VERB
cet-4972	80	35	labels	label	NOUN
cet-4972	80	36	for	for	ADP
cet-4972	80	37	each	each	DET
cet-4972	80	38	feature	feature	NOUN
cet-4972	80	39	have	have	AUX
cet-4972	80	40	been	be	AUX
cet-4972	80	41	,	,	PUNCT
cet-4972	80	42	then	then	ADV
cet-4972	80	43	,	,	PUNCT
cet-4972	80	44	stored	store	VERB
cet-4972	80	45	in	in	ADP
cet-4972	80	46	a	a	DET
cet-4972	80	47	matrix	matrix	NOUN
cet-4972	80	48	y	y	NOUN
cet-4972	80	49	of	of	ADP
cet-4972	80	50	a	a	DET
cet-4972	80	51	size	size	NOUN
cet-4972	80	52	149x3	149x3	NUM
cet-4972	80	53	.	.	PUNCT
cet-4972	81	1	the	the	DET
cet-4972	81	2	application	application	NOUN
cet-4972	81	3	of	of	ADP
cet-4972	81	4	the	the	DET
cet-4972	81	5	clustering	clustering	ADJ
cet-4972	81	6	ensemble	ensemble	ADJ
cet-4972	81	7	approach	approach	NOUN
cet-4972	81	8	aims	aim	VERB
cet-4972	81	9	at	at	ADP
cet-4972	81	10	finding	find	VERB
cet-4972	81	11	the	the	DET
cet-4972	81	12	final	final	ADJ
cet-4972	81	13	ensemble	ensemble	ADJ
cet-4972	81	14	clustering	clustering	NOUN
cet-4972	81	15	of	of	ADP
cet-4972	81	16	the	the	DET
cet-4972	81	17	data	datum	NOUN
cet-4972	81	18	.	.	PUNCT
cet-4972	82	1	in	in	ADP
cet-4972	82	2	the	the	DET
cet-4972	82	3	following	follow	VERB
cet-4972	82	4	section	section	NOUN
cet-4972	82	5	,	,	PUNCT
cet-4972	82	6	our	our	PRON
cet-4972	82	7	novel	novel	ADJ
cet-4972	82	8	approach	approach	NOUN
cet-4972	82	9	is	be	AUX
cet-4972	82	10	applied	apply	VERB
cet-4972	82	11	and	and	CCONJ
cet-4972	82	12	compared	compare	VERB
cet-4972	82	13	with	with	ADP
cet-4972	82	14	cspa	cspa	NOUN
cet-4972	82	15	-	-	PUNCT
cet-4972	82	16	metis	metis	NOUN
cet-4972	82	17	approach	approach	NOUN
cet-4972	82	18	.	.	PUNCT
cet-4972	83	1	1	1	NUM
cet-4972	83	2	2	2	NUM
cet-4972	83	3	149	149	NUM
cet-4972	83	4	148	148	NUM
cet-4972	83	5	nc1	nc1	NOUN
cet-4972	83	6	nc2	nc2	NOUN
cet-4972	83	7	nc3	nc3	ADV
cet-4972	83	8	ac1	ac1	PROPN
cet-4972	83	9	ac2	ac2	PROPN
cet-4972	83	10	ac3	ac3	PROPN
cet-4972	83	11	outliers	outlier	NOUN
cet-4972	83	12	function	function	VERB
cet-4972	83	13	7	7	NUM
cet-4972	83	14	function	function	NOUN
cet-4972	83	15	8	8	NUM
cet-4972	83	16	function	function	NOUN
cet-4972	83	17	9	9	NUM
cet-4972	83	18	function	function	NOUN
cet-4972	83	19	10	10	NUM
cet-4972	83	20	1	1	NUM
cet-4972	83	21	2	2	NUM
cet-4972	83	22	3true	3true	NUM
cet-4972	83	23	label	label	NOUN
cet-4972	83	24	(	(	PUNCT
cet-4972	83	25	target	target	NOUN
cet-4972	83	26	)	)	PUNCT
cet-4972	83	27	function	function	NOUN
cet-4972	83	28	1	1	NUM
cet-4972	83	29	function	function	NOUN
cet-4972	83	30	2	2	NUM
cet-4972	83	31	function	function	NOUN
cet-4972	83	32	3	3	NUM
cet-4972	83	33	function	function	NOUN
cet-4972	83	34	4	4	NUM
cet-4972	83	35	function	function	NOUN
cet-4972	83	36	5	5	NUM
cet-4972	83	37	function	function	NOUN
cet-4972	83	38	6	6	NUM
cet-4972	83	39	1	1	NUM
cet-4972	83	40	30	30	NUM
cet-4972	83	41	60	60	NUM
cet-4972	83	42	31	31	NUM
cet-4972	83	43	61	61	NUM
cet-4972	83	44	99	99	NUM
cet-4972	83	45	100	100	NUM
cet-4972	83	46	119	119	NUM
cet-4972	83	47	120	120	NUM
cet-4972	83	48	134	134	NUM
cet-4972	83	49	135	135	NUM
cet-4972	83	50	146	146	NUM
cet-4972	83	51	147	147	NUM
cet-4972	83	52	149	149	NUM
cet-4972	83	53	features	feature	VERB
cet-4972	83	54	i	i	PRON
cet-4972	83	55	.	.	PUNCT
cet-4972	83	56	.	.	PUNCT
cet-4972	83	57	.	.	PUNCT
cet-4972	83	58	.	.	PUNCT
cet-4972	83	59	.	.	PUNCT
cet-4972	84	1	.	.	PUNCT
cet-4972	85	1	1228	1228	NUM
cet-4972	85	2	4	4	NUM
cet-4972	85	3	.	.	PUNCT
cet-4972	85	4	application	application	NOUN
cet-4972	85	5	of	of	ADP
cet-4972	85	6	the	the	DET
cet-4972	85	7	novel	novel	ADJ
cet-4972	85	8	approach	approach	NOUN
cet-4972	85	9	to	to	ADP
cet-4972	85	10	the	the	DET
cet-4972	85	11	artificial	artificial	ADJ
cet-4972	85	12	case	case	NOUN
cet-4972	85	13	study	study	NOUN
cet-4972	85	14	in	in	ADP
cet-4972	85	15	this	this	DET
cet-4972	85	16	section	section	NOUN
cet-4972	85	17	,	,	PUNCT
cet-4972	85	18	the	the	DET
cet-4972	85	19	application	application	NOUN
cet-4972	85	20	of	of	ADP
cet-4972	85	21	the	the	DET
cet-4972	85	22	novel	novel	ADJ
cet-4972	85	23	ensemble	ensemble	ADJ
cet-4972	85	24	clustering	clustering	NOUN
cet-4972	85	25	approach	approach	NOUN
cet-4972	85	26	is	be	AUX
cet-4972	85	27	described	describe	VERB
cet-4972	85	28	according	accord	VERB
cet-4972	85	29	to	to	ADP
cet-4972	85	30	the	the	DET
cet-4972	85	31	steps	step	NOUN
cet-4972	85	32	presented	present	VERB
cet-4972	85	33	in	in	ADP
cet-4972	85	34	section	section	NOUN
cet-4972	85	35	3	3	NUM
cet-4972	85	36	and	and	CCONJ
cet-4972	85	37	then	then	ADV
cet-4972	85	38	,	,	PUNCT
cet-4972	85	39	the	the	DET
cet-4972	85	40	results	result	NOUN
cet-4972	85	41	obtained	obtain	VERB
cet-4972	85	42	are	be	AUX
cet-4972	85	43	compared	compare	VERB
cet-4972	85	44	to	to	ADP
cet-4972	85	45	those	those	PRON
cet-4972	85	46	achieved	achieve	VERB
cet-4972	85	47	by	by	ADP
cet-4972	85	48	the	the	DET
cet-4972	85	49	cspa	cspa	NOUN
cet-4972	85	50	-	-	PUNCT
cet-4972	85	51	metis	metis	NOUN
cet-4972	85	52	approach	approach	NOUN
cet-4972	85	53	.	.	PUNCT
cet-4972	86	1	given	give	VERB
cet-4972	86	2	the	the	DET
cet-4972	86	3	similarity	similarity	NOUN
cet-4972	86	4	matrix	matrix	NOUN
cet-4972	86	5	,	,	PUNCT
cet-4972	86	6	s	s	AUX
cet-4972	86	7	we	we	PRON
cet-4972	86	8	calculate	calculate	VERB
cet-4972	86	9	rsl	rsl	NOUN
cet-4972	86	10	and	and	CCONJ
cet-4972	86	11	its	its	PRON
cet-4972	86	12	eigenvectors	eigenvector	NOUN
cet-4972	86	13	1	1	NUM
cet-4972	86	14	2	2	NUM
cet-4972	86	15	149	149	NUM
cet-4972	86	16	,	,	PUNCT
cet-4972	86	17	,	,	PUNCT
cet-4972	86	18	...	...	PUNCT
cet-4972	86	19	,	,	PUNCT
cet-4972	86	20	,	,	PUNCT
cet-4972	86	21	...	...	PUNCT
cet-4972	86	22	,	,	PUNCT
cet-4972	86	23	candidatecu	candidatecu	VERB
cet-4972	86	24	u	u	NOUN
cet-4972	86	25	u	u	X
cet-4972	86	26	u	u	NOUN
cet-4972	86	27	and	and	CCONJ
cet-4972	86	28	the	the	DET
cet-4972	86	29	corresponding	corresponding	ADJ
cet-4972	86	30	eigenvalues	eigenvalues	PROPN
cet-4972	86	31	1	1	NUM
cet-4972	86	32	2	2	NUM
cet-4972	86	33	149	149	NUM
cet-4972	86	34	,	,	PUNCT
cet-4972	86	35	,	,	PUNCT
cet-4972	86	36	...	...	PUNCT
cet-4972	86	37	,	,	PUNCT
cet-4972	86	38	,	,	PUNCT
cet-4972	86	39	...	...	PUNCT
cet-4972	86	40	,	,	PUNCT
cet-4972	86	41	.	.	PUNCT
cet-4972	87	1	candidatecλ	candidatecλ	PROPN
cet-4972	88	1	λ	λ	INTJ
cet-4972	88	2	λ	λ	X
cet-4972	88	3	λ	λ	X
cet-4972	88	4	the	the	DET
cet-4972	88	5	obtained	obtain	VERB
cet-4972	88	6	eigenvectors	eigenvector	NOUN
cet-4972	88	7	are	be	AUX
cet-4972	88	8	stored	store	VERB
cet-4972	88	9	in	in	ADP
cet-4972	88	10	the	the	DET
cet-4972	88	11	matrix	matrix	NOUN
cet-4972	88	12	u	u	NOUN
cet-4972	88	13	with	with	ADP
cet-4972	88	14	size	size	NOUN
cet-4972	88	15	149x149	149x149	NUM
cet-4972	88	16	.	.	PUNCT
cet-4972	89	1	the	the	DET
cet-4972	89	2	number	number	NOUN
cet-4972	89	3	m	m	VERB
cet-4972	89	4	of	of	ADP
cet-4972	89	5	clusters	cluster	NOUN
cet-4972	89	6	of	of	ADP
cet-4972	89	7	final	final	ADJ
cet-4972	89	8	ensemble	ensemble	ADJ
cet-4972	89	9	clustering	clustering	NOUN
cet-4972	89	10	is	be	AUX
cet-4972	89	11	selected	select	VERB
cet-4972	89	12	according	accord	VERB
cet-4972	89	13	to	to	ADP
cet-4972	89	14	the	the	DET
cet-4972	89	15	values	value	NOUN
cet-4972	89	16	of	of	ADP
cet-4972	89	17	silhouette	silhouette	NOUN
cet-4972	89	18	for	for	ADP
cet-4972	89	19	different	different	ADJ
cet-4972	89	20	numbers	number	NOUN
cet-4972	89	21	of	of	ADP
cet-4972	89	22	clusters	cluster	NOUN
cet-4972	89	23	ccandidate	ccandidate	ADJ
cet-4972	89	24	that	that	PRON
cet-4972	89	25	span	span	VERB
cet-4972	89	26	the	the	DET
cet-4972	89	27	interval	interval	NOUN
cet-4972	89	28	[	[	X
cet-4972	89	29	2,16	2,16	NUM
cet-4972	89	30	]	]	X
cet-4972	89	31	,	,	PUNCT
cet-4972	89	32	where	where	SCONJ
cet-4972	89	33	the	the	DET
cet-4972	89	34	lower	low	ADJ
cet-4972	89	35	bound	bind	VERB
cet-4972	89	36	(	(	PUNCT
cet-4972	89	37	2	2	NUM
cet-4972	89	38	)	)	PUNCT
cet-4972	89	39	is	be	AUX
cet-4972	89	40	the	the	DET
cet-4972	89	41	minimum	minimum	ADJ
cet-4972	89	42	number	number	NOUN
cet-4972	89	43	of	of	ADP
cet-4972	89	44	base	base	NOUN
cet-4972	89	45	clusters	cluster	NOUN
cet-4972	89	46	(	(	PUNCT
cet-4972	89	47	see	see	VERB
cet-4972	89	48	table	table	NOUN
cet-4972	89	49	1	1	NUM
cet-4972	89	50	)	)	PUNCT
cet-4972	89	51	,	,	PUNCT
cet-4972	89	52	whereas	whereas	SCONJ
cet-4972	89	53	the	the	DET
cet-4972	89	54	upper	upper	ADJ
cet-4972	89	55	bound	bind	VERB
cet-4972	89	56	(	(	PUNCT
cet-4972	89	57	16	16	NUM
cet-4972	89	58	)	)	PUNCT
cet-4972	89	59	is	be	AUX
cet-4972	89	60	the	the	DET
cet-4972	89	61	number	number	NOUN
cet-4972	89	62	of	of	ADP
cet-4972	89	63	the	the	DET
cet-4972	89	64	largest	large	ADJ
cet-4972	89	65	combination	combination	NOUN
cet-4972	89	66	of	of	ADP
cet-4972	89	67	the	the	DET
cet-4972	89	68	three	three	NUM
cet-4972	89	69	base	base	NOUN
cet-4972	89	70	clusters	cluster	NOUN
cet-4972	89	71	(	(	PUNCT
cet-4972	89	72	i.e.	i.e.	X
cet-4972	89	73	,	,	PUNCT
cet-4972	89	74	2x2x4	2x2x4	NOUN
cet-4972	89	75	):	):	PUNCT
cet-4972	89	76	the	the	DET
cet-4972	89	77	optimum	optimum	ADJ
cet-4972	89	78	number	number	NOUN
cet-4972	89	79	of	of	ADP
cet-4972	89	80	clusters	cluster	NOUN
cet-4972	89	81	c	c	PROPN
cet-4972	89	82	*	*	PUNCT
cet-4972	89	83	in	in	ADP
cet-4972	89	84	the	the	DET
cet-4972	89	85	final	final	ADJ
cet-4972	89	86	ensemble	ensemble	ADJ
cet-4972	89	87	clustering	clustering	NOUN
cet-4972	89	88	is	be	AUX
cet-4972	89	89	the	the	DET
cet-4972	89	90	value	value	NOUN
cet-4972	89	91	at	at	ADP
cet-4972	89	92	which	which	PRON
cet-4972	89	93	the	the	DET
cet-4972	89	94	silhouette	silhouette	NOUN
cet-4972	89	95	value	value	NOUN
cet-4972	89	96	is	be	AUX
cet-4972	89	97	maximized	maximize	VERB
cet-4972	89	98	,	,	PUNCT
cet-4972	89	99	i.e.	i.e.	X
cet-4972	89	100	,	,	PUNCT
cet-4972	89	101	c	c	X
cet-4972	89	102	*	*	PUNCT
cet-4972	90	1	=	=	NOUN
cet-4972	90	2	6	6	NUM
cet-4972	90	3	(	(	PUNCT
cet-4972	90	4	star	star	NOUN
cet-4972	90	5	in	in	ADP
cet-4972	90	6	figure	figure	NOUN
cet-4972	90	7	4	4	NUM
cet-4972	90	8	(	(	PUNCT
cet-4972	90	9	left	leave	VERB
cet-4972	90	10	)	)	PUNCT
cet-4972	90	11	)	)	PUNCT
cet-4972	90	12	.	.	PUNCT
cet-4972	91	1	figure	figure	VERB
cet-4972	91	2	4	4	NUM
cet-4972	91	3	(	(	PUNCT
cet-4972	91	4	right	right	ADJ
cet-4972	91	5	)	)	PUNCT
cet-4972	91	6	shows	show	VERB
cet-4972	91	7	the	the	DET
cet-4972	91	8	results	result	NOUN
cet-4972	91	9	of	of	ADP
cet-4972	91	10	the	the	DET
cet-4972	91	11	aggregation	aggregation	NOUN
cet-4972	91	12	obtained	obtain	VERB
cet-4972	91	13	by	by	ADP
cet-4972	91	14	using	use	VERB
cet-4972	91	15	the	the	DET
cet-4972	91	16	novel	novel	NOUN
cet-4972	91	17	and	and	CCONJ
cet-4972	91	18	the	the	DET
cet-4972	91	19	reference	reference	NOUN
cet-4972	91	20	approaches	approach	VERB
cet-4972	91	21	.	.	PUNCT
cet-4972	92	1	the	the	DET
cet-4972	92	2	figure	figure	NOUN
cet-4972	92	3	shows	show	VERB
cet-4972	92	4	the	the	DET
cet-4972	92	5	n=149	n=149	NOUN
cet-4972	92	6	data	datum	NOUN
cet-4972	92	7	in	in	ADP
cet-4972	92	8	chronological	chronological	ADJ
cet-4972	92	9	order	order	NOUN
cet-4972	92	10	from	from	ADP
cet-4972	92	11	top	top	NOUN
cet-4972	92	12	to	to	ADP
cet-4972	92	13	bottom	bottom	NOUN
cet-4972	92	14	with	with	ADP
cet-4972	92	15	the	the	DET
cet-4972	92	16	associated	associated	ADJ
cet-4972	92	17	true	true	ADJ
cet-4972	92	18	clustering	clustering	ADJ
cet-4972	92	19	labels	label	NOUN
cet-4972	92	20	represented	represent	VERB
cet-4972	92	21	in	in	ADP
cet-4972	92	22	different	different	ADJ
cet-4972	92	23	shades	shade	NOUN
cet-4972	92	24	of	of	ADP
cet-4972	92	25	color	color	NOUN
cet-4972	92	26	.	.	PUNCT
cet-4972	93	1	it	it	PRON
cet-4972	93	2	is	be	AUX
cet-4972	93	3	worth	worth	ADJ
cet-4972	93	4	mentioning	mention	VERB
cet-4972	93	5	that	that	SCONJ
cet-4972	93	6	there	there	PRON
cet-4972	93	7	is	be	VERB
cet-4972	93	8	no	no	DET
cet-4972	93	9	correspondence	correspondence	NOUN
cet-4972	93	10	between	between	ADP
cet-4972	93	11	the	the	DET
cet-4972	93	12	similar	similar	ADJ
cet-4972	93	13	colors	color	NOUN
cet-4972	93	14	of	of	ADP
cet-4972	93	15	the	the	DET
cet-4972	93	16	true	true	ADJ
cet-4972	93	17	and	and	CCONJ
cet-4972	93	18	the	the	DET
cet-4972	93	19	aggregation	aggregation	NOUN
cet-4972	93	20	results	result	VERB
cet-4972	93	21	.	.	PUNCT
cet-4972	94	1	the	the	DET
cet-4972	94	2	application	application	NOUN
cet-4972	94	3	of	of	ADP
cet-4972	94	4	cspametis	cspametis	ADJ
cet-4972	94	5	approach	approach	NOUN
cet-4972	94	6	leads	lead	VERB
cet-4972	94	7	us	we	PRON
cet-4972	94	8	to	to	PART
cet-4972	94	9	distinguish	distinguish	VERB
cet-4972	94	10	clearly	clearly	ADV
cet-4972	94	11	only	only	ADV
cet-4972	94	12	three	three	NUM
cet-4972	94	13	clusters	cluster	NOUN
cet-4972	94	14	,	,	PUNCT
cet-4972	94	15	i.e.	i.e.	X
cet-4972	94	16	,	,	PUNCT
cet-4972	94	17	nc1	nc1	PROPN
cet-4972	94	18	,	,	PUNCT
cet-4972	94	19	nc2	nc2	NOUN
cet-4972	94	20	and	and	CCONJ
cet-4972	94	21	nc3	nc3	ADV
cet-4972	94	22	,	,	PUNCT
cet-4972	94	23	whereas	whereas	SCONJ
cet-4972	94	24	the	the	DET
cet-4972	94	25	remaining	remain	VERB
cet-4972	94	26	data	datum	NOUN
cet-4972	94	27	have	have	AUX
cet-4972	94	28	not	not	PART
cet-4972	94	29	been	be	AUX
cet-4972	94	30	correctly	correctly	ADV
cet-4972	94	31	clustered	cluster	VERB
cet-4972	94	32	.	.	PUNCT
cet-4972	95	1	comparing	compare	VERB
cet-4972	95	2	the	the	DET
cet-4972	95	3	obtained	obtain	VERB
cet-4972	95	4	clustering	clustering	ADJ
cet-4972	95	5	results	result	NOUN
cet-4972	95	6	with	with	ADP
cet-4972	95	7	the	the	DET
cet-4972	95	8	true	true	ADJ
cet-4972	95	9	clustering	clustering	NOUN
cet-4972	95	10	,	,	PUNCT
cet-4972	95	11	one	one	PRON
cet-4972	95	12	can	can	AUX
cet-4972	95	13	calculate	calculate	VERB
cet-4972	95	14	the	the	DET
cet-4972	95	15	misclassification	misclassification	NOUN
cet-4972	95	16	rate	rate	NOUN
cet-4972	95	17	to	to	PART
cet-4972	95	18	be	be	AUX
cet-4972	95	19	equal	equal	ADJ
cet-4972	95	20	to	to	ADP
cet-4972	95	21	34.9	34.9	NUM
cet-4972	95	22	%	%	NOUN
cet-4972	95	23	(	(	PUNCT
cet-4972	95	24	52	52	NUM
cet-4972	95	25	out	out	ADP
cet-4972	95	26	of	of	ADP
cet-4972	95	27	149	149	NUM
cet-4972	95	28	data	datum	NOUN
cet-4972	95	29	incorrectly	incorrectly	ADV
cet-4972	95	30	classified	classify	VERB
cet-4972	95	31	)	)	PUNCT
cet-4972	95	32	,	,	PUNCT
cet-4972	95	33	which	which	PRON
cet-4972	95	34	is	be	AUX
cet-4972	95	35	not	not	PART
cet-4972	95	36	a	a	DET
cet-4972	95	37	satisfactory	satisfactory	ADJ
cet-4972	95	38	result	result	NOUN
cet-4972	95	39	.	.	PUNCT
cet-4972	96	1	on	on	ADP
cet-4972	96	2	the	the	DET
cet-4972	96	3	other	other	ADJ
cet-4972	96	4	hand	hand	NOUN
cet-4972	96	5	,	,	PUNCT
cet-4972	96	6	the	the	DET
cet-4972	96	7	application	application	NOUN
cet-4972	96	8	of	of	ADP
cet-4972	96	9	the	the	DET
cet-4972	96	10	novel	novel	ADJ
cet-4972	96	11	approach	approach	NOUN
cet-4972	96	12	leads	lead	VERB
cet-4972	96	13	us	we	PRON
cet-4972	96	14	to	to	PART
cet-4972	96	15	recognize	recognize	VERB
cet-4972	96	16	clearly	clearly	ADV
cet-4972	96	17	six	six	NUM
cet-4972	96	18	out	out	ADP
cet-4972	96	19	of	of	ADP
cet-4972	96	20	seven	seven	NUM
cet-4972	96	21	operational	operational	ADJ
cet-4972	96	22	conditions	condition	NOUN
cet-4972	96	23	with	with	ADP
cet-4972	96	24	a	a	DET
cet-4972	96	25	reduced	reduce	VERB
cet-4972	96	26	misclassification	misclassification	NOUN
cet-4972	96	27	rate	rate	NOUN
cet-4972	96	28	3.4	3.4	NUM
cet-4972	96	29	%	%	NOUN
cet-4972	96	30	(	(	PUNCT
cet-4972	96	31	5	5	NUM
cet-4972	96	32	out	out	ADP
cet-4972	96	33	of	of	ADP
cet-4972	96	34	149	149	NUM
cet-4972	96	35	data	datum	NOUN
cet-4972	96	36	incorrectly	incorrectly	ADV
cet-4972	96	37	classified	classify	VERB
cet-4972	96	38	)	)	PUNCT
cet-4972	96	39	compared	compare	VERB
cet-4972	96	40	to	to	ADP
cet-4972	96	41	the	the	DET
cet-4972	96	42	cspa	cspa	NOUN
cet-4972	96	43	-	-	PUNCT
cet-4972	96	44	metis	metis	NOUN
cet-4972	96	45	approach	approach	NOUN
cet-4972	96	46	.	.	PUNCT
cet-4972	97	1	figure	figure	VERB
cet-4972	97	2	4	4	NUM
cet-4972	97	3	:	:	PUNCT
cet-4972	97	4	silhouette	silhouette	NOUN
cet-4972	97	5	values	value	NOUN
cet-4972	97	6	vs.	vs.	ADP
cet-4972	97	7	cluster	cluster	NOUN
cet-4972	97	8	numbers	number	NOUN
cet-4972	97	9	(	(	PUNCT
cet-4972	97	10	left	leave	VERB
cet-4972	97	11	)	)	PUNCT
cet-4972	97	12	and	and	CCONJ
cet-4972	97	13	the	the	DET
cet-4972	97	14	obtained	obtain	VERB
cet-4972	97	15	final	final	ADJ
cet-4972	97	16	ensemble	ensemble	ADJ
cet-4972	97	17	clusterings	clustering	NOUN
cet-4972	97	18	obtained	obtain	VERB
cet-4972	97	19	by	by	ADP
cet-4972	97	20	the	the	DET
cet-4972	97	21	novel	novel	NOUN
cet-4972	97	22	and	and	CCONJ
cet-4972	97	23	the	the	DET
cet-4972	97	24	reference	reference	NOUN
cet-4972	97	25	approaches	approach	VERB
cet-4972	97	26	vs.	vs.	ADP
cet-4972	97	27	the	the	DET
cet-4972	97	28	true	true	ADJ
cet-4972	97	29	clustering	clustering	NOUN
cet-4972	97	30	(	(	PUNCT
cet-4972	97	31	right	right	ADJ
cet-4972	97	32	)	)	PUNCT
cet-4972	97	33	as	as	ADP
cet-4972	97	34	last	last	ADJ
cet-4972	97	35	remark	remark	NOUN
cet-4972	97	36	,	,	PUNCT
cet-4972	97	37	it	it	PRON
cet-4972	97	38	is	be	AUX
cet-4972	97	39	worth	worth	ADJ
cet-4972	97	40	mentioning	mention	VERB
cet-4972	97	41	that	that	SCONJ
cet-4972	97	42	the	the	DET
cet-4972	97	43	outliers	outlier	NOUN
cet-4972	97	44	(	(	PUNCT
cet-4972	97	45	three	three	NUM
cet-4972	97	46	transients	transient	NOUN
cet-4972	97	47	–	–	PUNCT
cet-4972	97	48	class	class	NOUN
cet-4972	97	49	7	7	NUM
cet-4972	97	50	)	)	PUNCT
cet-4972	97	51	using	use	VERB
cet-4972	97	52	the	the	DET
cet-4972	97	53	proposed	propose	VERB
cet-4972	97	54	approach	approach	NOUN
cet-4972	97	55	have	have	AUX
cet-4972	97	56	not	not	PART
cet-4972	97	57	been	be	AUX
cet-4972	97	58	grouped	group	VERB
cet-4972	97	59	together	together	ADV
cet-4972	97	60	in	in	ADP
cet-4972	97	61	a	a	DET
cet-4972	97	62	separate	separate	ADJ
cet-4972	97	63	cluster	cluster	NOUN
cet-4972	97	64	:	:	PUNCT
cet-4972	97	65	this	this	PRON
cet-4972	97	66	depends	depend	VERB
cet-4972	97	67	on	on	ADP
cet-4972	97	68	the	the	DET
cet-4972	97	69	capability	capability	NOUN
cet-4972	97	70	of	of	ADP
cet-4972	97	71	the	the	DET
cet-4972	97	72	base	base	NOUN
cet-4972	97	73	clustering	cluster	VERB
cet-4972	97	74	algorithm	algorithm	NOUN
cet-4972	97	75	in	in	ADP
cet-4972	97	76	recognizing	recognize	VERB
cet-4972	97	77	the	the	DET
cet-4972	97	78	outliers	outlier	NOUN
cet-4972	97	79	(	(	PUNCT
cet-4972	97	80	topchy	topchy	NOUN
cet-4972	97	81	et	et	PROPN
cet-4972	97	82	al	al	PROPN
cet-4972	97	83	.	.	PROPN
cet-4972	97	84	2005	2005	NUM
cet-4972	97	85	)	)	PUNCT
cet-4972	97	86	.	.	PUNCT
cet-4972	98	1	for	for	ADP
cet-4972	98	2	example	example	NOUN
cet-4972	98	3	,	,	PUNCT
cet-4972	98	4	the	the	DET
cet-4972	98	5	optimum	optimum	ADJ
cet-4972	98	6	number	number	NOUN
cet-4972	98	7	of	of	ADP
cet-4972	98	8	clusters	cluster	NOUN
cet-4972	98	9	for	for	ADP
cet-4972	98	10	the	the	DET
cet-4972	98	11	feature	feature	NOUN
cet-4972	98	12	j=1	j=1	NOUN
cet-4972	98	13	is	be	AUX
cet-4972	98	14	1	1	NUM
cet-4972	98	15	2optc	2optc	NUM
cet-4972	98	16	=	=	SYM
cet-4972	98	17	(	(	PUNCT
cet-4972	98	18	see	see	VERB
cet-4972	98	19	table	table	NOUN
cet-4972	98	20	1	1	NUM
cet-4972	98	21	)	)	PUNCT
cet-4972	98	22	,	,	PUNCT
cet-4972	98	23	whereas	whereas	SCONJ
cet-4972	98	24	it	it	PRON
cet-4972	98	25	should	should	AUX
cet-4972	98	26	be	be	AUX
cet-4972	98	27	equal	equal	ADJ
cet-4972	98	28	to	to	ADP
cet-4972	98	29	3	3	NUM
cet-4972	98	30	(	(	PUNCT
cet-4972	98	31	see	see	VERB
cet-4972	98	32	figure	figure	NOUN
cet-4972	98	33	3	3	NUM
cet-4972	98	34	)	)	PUNCT
cet-4972	98	35	.	.	PUNCT
cet-4972	99	1	5	5	X
cet-4972	99	2	.	.	X
cet-4972	99	3	conclusions	conclusion	NOUN
cet-4972	99	4	in	in	ADP
cet-4972	99	5	this	this	DET
cet-4972	99	6	work	work	NOUN
cet-4972	99	7	,	,	PUNCT
cet-4972	99	8	a	a	DET
cet-4972	99	9	novel	novel	ADJ
cet-4972	99	10	unsupervised	unsupervised	ADJ
cet-4972	99	11	ensemble	ensemble	ADJ
cet-4972	99	12	clustering	clustering	NOUN
cet-4972	99	13	approach	approach	NOUN
cet-4972	99	14	is	be	AUX
cet-4972	99	15	proposed	propose	VERB
cet-4972	99	16	to	to	PART
cet-4972	99	17	construct	construct	VERB
cet-4972	99	18	a	a	DET
cet-4972	99	19	final	final	ADJ
cet-4972	99	20	ensemble	ensemble	ADJ
cet-4972	99	21	clustering	clustering	NOUN
cet-4972	99	22	p	p	X
cet-4972	99	23	*	*	PUNCT
cet-4972	99	24	from	from	ADP
cet-4972	99	25	h	h	PROPN
cet-4972	99	26	individual	individual	ADJ
cet-4972	99	27	base	base	NOUN
cet-4972	99	28	clustering	cluster	VERB
cet-4972	99	29	outcomes	outcome	NOUN
cet-4972	99	30	.	.	PUNCT
cet-4972	100	1	the	the	DET
cet-4972	100	2	method	method	NOUN
cet-4972	100	3	is	be	AUX
cet-4972	100	4	based	base	VERB
cet-4972	100	5	on	on	ADP
cet-4972	100	6	spectral	spectral	ADJ
cet-4972	100	7	clustering	clustering	NOUN
cet-4972	100	8	,	,	PUNCT
cet-4972	100	9	embedding	embed	VERB
cet-4972	100	10	an	an	DET
cet-4972	100	11	unsupervised	unsupervised	ADJ
cet-4972	100	12	k	k	ADJ
cet-4972	100	13	-	-	PUNCT
cet-4972	100	14	means	means	NOUN
cet-4972	100	15	algorithm	algorithm	NOUN
cet-4972	100	16	that	that	PRON
cet-4972	100	17	is	be	AUX
cet-4972	100	18	fed	feed	VERB
cet-4972	100	19	by	by	ADP
cet-4972	100	20	a	a	DET
cet-4972	100	21	pairwise	pairwise	NOUN
cet-4972	100	22	similarity	similarity	NOUN
cet-4972	100	23	computation	computation	NOUN
cet-4972	100	24	,	,	PUNCT
cet-4972	100	25	so	so	SCONJ
cet-4972	100	26	that	that	SCONJ
cet-4972	100	27	a	a	DET
cet-4972	100	28	coassociation	coassociation	NOUN
cet-4972	100	29	matrix	matrix	NOUN
cet-4972	100	30	summarizes	summarize	VERB
cet-4972	100	31	the	the	DET
cet-4972	100	32	similarity	similarity	NOUN
cet-4972	100	33	among	among	ADP
cet-4972	100	34	the	the	DET
cet-4972	100	35	data	datum	NOUN
cet-4972	100	36	and	and	CCONJ
cet-4972	100	37	the	the	DET
cet-4972	100	38	clusters	cluster	NOUN
cet-4972	100	39	are	be	AUX
cet-4972	100	40	formed	form	VERB
cet-4972	100	41	by	by	ADP
cet-4972	100	42	the	the	DET
cet-4972	100	43	most	most	ADV
cet-4972	100	44	similar	similar	ADJ
cet-4972	100	45	data	datum	NOUN
cet-4972	100	46	.	.	PUNCT
cet-4972	101	1	the	the	DET
cet-4972	101	2	optimum	optimum	ADJ
cet-4972	101	3	number	number	NOUN
cet-4972	101	4	of	of	ADP
cet-4972	101	5	clusters	cluster	NOUN
cet-4972	101	6	is	be	AUX
cet-4972	101	7	selected	select	VERB
cet-4972	101	8	among	among	ADP
cet-4972	101	9	several	several	ADJ
cet-4972	101	10	candidates	candidate	NOUN
cet-4972	101	11	based	base	VERB
cet-4972	101	12	on	on	ADP
cet-4972	101	13	silhouette	silhouette	NOUN
cet-4972	101	14	validity	validity	NOUN
cet-4972	101	15	index	index	NOUN
cet-4972	101	16	that	that	PRON
cet-4972	101	17	quantifies	quantify	VERB
cet-4972	101	18	the	the	DET
cet-4972	101	19	morphology	morphology	NOUN
cet-4972	101	20	of	of	ADP
cet-4972	101	21	the	the	DET
cet-4972	101	22	obtained	obtain	VERB
cet-4972	101	23	clusters	cluster	NOUN
cet-4972	101	24	and	and	CCONJ
cet-4972	101	25	gives	give	VERB
cet-4972	101	26	reason	reason	NOUN
cet-4972	101	27	of	of	ADP
cet-4972	101	28	the	the	DET
cet-4972	101	29	similarity	similarity	NOUN
cet-4972	101	30	of	of	ADP
cet-4972	101	31	data	datum	NOUN
cet-4972	101	32	belonging	belong	VERB
cet-4972	101	33	to	to	ADP
cet-4972	101	34	the	the	DET
cet-4972	101	35	same	same	ADJ
cet-4972	101	36	cluster	cluster	NOUN
cet-4972	101	37	and	and	CCONJ
cet-4972	101	38	,	,	PUNCT
cet-4972	101	39	at	at	ADP
cet-4972	101	40	the	the	DET
cet-4972	101	41	same	same	ADJ
cet-4972	101	42	time	time	NOUN
cet-4972	101	43	,	,	PUNCT
cet-4972	101	44	of	of	ADP
cet-4972	101	45	dissimilarity	dissimilarity	NOUN
cet-4972	101	46	with	with	ADP
cet-4972	101	47	those	those	PRON
cet-4972	101	48	in	in	ADP
cet-4972	101	49	the	the	DET
cet-4972	101	50	other	other	ADJ
cet-4972	101	51	clusters	cluster	NOUN
cet-4972	101	52	:	:	PUNCT
cet-4972	101	53	a	a	DET
cet-4972	101	54	large	large	ADJ
cet-4972	101	55	silhouette	silhouette	NOUN
cet-4972	101	56	value	value	NOUN
cet-4972	101	57	indicates	indicate	VERB
cet-4972	101	58	that	that	SCONJ
cet-4972	101	59	the	the	DET
cet-4972	101	60	obtained	obtain	VERB
cet-4972	101	61	clusters	cluster	NOUN
cet-4972	101	62	of	of	ADP
cet-4972	101	63	the	the	DET
cet-4972	101	64	final	final	ADJ
cet-4972	101	65	ensemble	ensemble	ADJ
cet-4972	101	66	clustering	clustering	NOUN
cet-4972	101	67	are	be	AUX
cet-4972	101	68	well	well	ADV
cet-4972	101	69	separated	separate	VERB
cet-4972	101	70	and	and	CCONJ
cet-4972	101	71	compacted	compact	VERB
cet-4972	101	72	.	.	PUNCT
cet-4972	102	1	the	the	DET
cet-4972	102	2	proposed	propose	VERB
cet-4972	102	3	approach	approach	NOUN
cet-4972	102	4	has	have	AUX
cet-4972	102	5	been	be	AUX
cet-4972	102	6	successfully	successfully	ADV
cet-4972	102	7	tested	test	VERB
cet-4972	102	8	with	with	ADP
cet-4972	102	9	respect	respect	NOUN
cet-4972	102	10	to	to	ADP
cet-4972	102	11	an	an	DET
cet-4972	102	12	artificial	artificial	ADJ
cet-4972	102	13	case	case	NOUN
cet-4972	102	14	study	study	NOUN
cet-4972	102	15	properly	properly	ADV
cet-4972	102	16	designed	design	VERB
cet-4972	102	17	to	to	PART
cet-4972	102	18	reproduce	reproduce	VERB
cet-4972	102	19	the	the	DET
cet-4972	102	20	signal	signal	ADJ
cet-4972	102	21	trend	trend	NOUN
cet-4972	102	22	behavior	behavior	NOUN
cet-4972	102	23	of	of	ADP
cet-4972	102	24	industrial	industrial	ADJ
cet-4972	102	25	equipment	equipment	NOUN
cet-4972	102	26	during	during	ADP
cet-4972	102	27	shut	shut	NOUN
cet-4972	102	28	-	-	PUNCT
cet-4972	102	29	down	down	ADP
cet-4972	102	30	transients	transient	NOUN
cet-4972	102	31	.	.	PUNCT
cet-4972	103	1	the	the	DET
cet-4972	103	2	results	result	NOUN
cet-4972	103	3	obtained	obtain	VERB
cet-4972	103	4	have	have	AUX
cet-4972	103	5	been	be	AUX
cet-4972	103	6	compared	compare	VERB
cet-4972	103	7	to	to	ADP
cet-4972	103	8	those	those	PRON
cet-4972	103	9	achieved	achieve	VERB
cet-4972	103	10	by	by	ADP
cet-4972	103	11	the	the	DET
cet-4972	103	12	cspa	cspa	NOUN
cet-4972	103	13	-	-	PUNCT
cet-4972	103	14	metis	metis	NOUN
cet-4972	103	15	approach	approach	NOUN
cet-4972	103	16	of	of	ADP
cet-4972	103	17	literature	literature	NOUN
cet-4972	103	18	.	.	PUNCT
cet-4972	104	1	the	the	DET
cet-4972	104	2	continuation	continuation	NOUN
cet-4972	104	3	of	of	ADP
cet-4972	104	4	this	this	DET
cet-4972	104	5	work	work	NOUN
cet-4972	104	6	will	will	AUX
cet-4972	104	7	consider	consider	VERB
cet-4972	104	8	the	the	DET
cet-4972	104	9	application	application	NOUN
cet-4972	104	10	of	of	ADP
cet-4972	104	11	the	the	DET
cet-4972	104	12	method	method	NOUN
cet-4972	104	13	to	to	ADP
cet-4972	104	14	real	real	ADJ
cet-4972	104	15	datasets	dataset	NOUN
cet-4972	104	16	collected	collect	VERB
cet-4972	104	17	during	during	ADP
cet-4972	104	18	past	past	ADJ
cet-4972	104	19	operation	operation	NOUN
cet-4972	104	20	of	of	ADP
cet-4972	104	21	industrial	industrial	ADJ
cet-4972	104	22	equipment	equipment	NOUN
cet-4972	104	23	such	such	ADJ
cet-4972	104	24	as	as	ADP
cet-4972	104	25	,	,	PUNCT
cet-4972	104	26	for	for	ADP
cet-4972	104	27	example	example	NOUN
cet-4972	104	28	,	,	PUNCT
cet-4972	104	29	a	a	DET
cet-4972	104	30	nuclear	nuclear	ADJ
cet-4972	104	31	power	power	NOUN
cet-4972	104	32	plant	plant	NOUN
cet-4972	104	33	turbine	turbine	NOUN
cet-4972	104	34	.	.	PUNCT
cet-4972	105	1	2	2	NUM
cet-4972	105	2	4	4	NUM
cet-4972	105	3	6	6	NUM
cet-4972	105	4	8	8	NUM
cet-4972	105	5	10	10	NUM
cet-4972	105	6	12	12	NUM
cet-4972	105	7	14	14	NUM
cet-4972	105	8	16	16	NUM
cet-4972	105	9	0.35	0.35	NUM
cet-4972	105	10	0.4	0.4	NUM
cet-4972	105	11	0.45	0.45	NUM
cet-4972	105	12	0.5	0.5	NUM
cet-4972	105	13	0.55	0.55	NUM
cet-4972	105	14	0.6	0.6	NUM
cet-4972	105	15	0.65	0.65	NUM
cet-4972	105	16	0.7	0.7	NUM
cet-4972	105	17	0.75	0.75	NUM
cet-4972	105	18	0.8	0.8	NUM
cet-4972	105	19	number	number	NOUN
cet-4972	105	20	of	of	ADP
cet-4972	105	21	clusters	cluster	NOUN
cet-4972	105	22	s	s	PART
cet-4972	105	23	ilh	ilh	PROPN
cet-4972	105	24	ou	ou	ADP
cet-4972	105	25	et	et	NOUN
cet-4972	105	26	te	te	PROPN
cet-4972	105	27	in	in	ADP
cet-4972	105	28	d	d	PROPN
cet-4972	105	29	ex	ex	PRON
cet-4972	105	30	1229	1229	NUM
cet-4972	105	31	acknowledgements	acknowledgement	NOUN
cet-4972	105	32	the	the	DET
cet-4972	105	33	participation	participation	NOUN
cet-4972	105	34	of	of	ADP
cet-4972	105	35	sameer	sameer	PROPN
cet-4972	105	36	al	al	PROPN
cet-4972	105	37	-	-	PUNCT
cet-4972	105	38	dahidi	dahidi	PROPN
cet-4972	105	39	and	and	CCONJ
cet-4972	105	40	piero	piero	PROPN
cet-4972	105	41	baraldi	baraldi	PROPN
cet-4972	105	42	to	to	ADP
cet-4972	105	43	this	this	DET
cet-4972	105	44	research	research	NOUN
cet-4972	105	45	is	be	AUX
cet-4972	105	46	supported	support	VERB
cet-4972	105	47	by	by	ADP
cet-4972	105	48	the	the	DET
cet-4972	105	49	european	european	PROPN
cet-4972	105	50	union	union	PROPN
cet-4972	105	51	project	project	NOUN
cet-4972	105	52	innovation	innovation	NOUN
cet-4972	105	53	through	through	ADP
cet-4972	105	54	human	human	ADJ
cet-4972	105	55	factors	factor	NOUN
cet-4972	105	56	in	in	ADP
cet-4972	105	57	risk	risk	NOUN
cet-4972	105	58	analysis	analysis	NOUN
cet-4972	105	59	and	and	CCONJ
cet-4972	105	60	management	management	NOUN
cet-4972	105	61	(	(	PUNCT
cet-4972	105	62	innhf	innhf	NOUN
cet-4972	105	63	,	,	PUNCT
cet-4972	105	64	www.innhf.eu	www.innhf.eu	PROPN
cet-4972	105	65	)	)	PUNCT
cet-4972	105	66	funded	fund	VERB
cet-4972	105	67	by	by	ADP
cet-4972	105	68	the	the	DET
cet-4972	105	69	7th	7th	ADJ
cet-4972	105	70	framework	framework	NOUN
cet-4972	105	71	program	program	NOUN
cet-4972	105	72	fp7	fp7	PROPN
cet-4972	105	73	-	-	PROPN
cet-4972	105	74	people-2011initial	people-2011initial	ADJ
cet-4972	105	75	training	training	NOUN
cet-4972	105	76	network	network	NOUN
cet-4972	105	77	:	:	PUNCT
cet-4972	105	78	marie	marie	PROPN
cet-4972	105	79	-	-	PUNCT
cet-4972	105	80	curie	curie	PROPN
cet-4972	105	81	action	action	NOUN
cet-4972	105	82	.	.	PUNCT
cet-4972	106	1	the	the	DET
cet-4972	106	2	participation	participation	NOUN
cet-4972	106	3	of	of	ADP
cet-4972	106	4	enrico	enrico	PROPN
cet-4972	106	5	zio	zio	PROPN
cet-4972	106	6	to	to	ADP
cet-4972	106	7	this	this	DET
cet-4972	106	8	research	research	NOUN
cet-4972	106	9	is	be	AUX
cet-4972	106	10	partially	partially	ADV
cet-4972	106	11	supported	support	VERB
cet-4972	106	12	by	by	ADP
cet-4972	106	13	the	the	DET
cet-4972	106	14	china	china	PROPN
cet-4972	106	15	nsfc	nsfc	PROPN
cet-4972	106	16	under	under	ADP
cet-4972	106	17	grant	grant	NOUN
cet-4972	106	18	number	number	NOUN
cet-4972	106	19	71231001	71231001	NUM
cet-4972	106	20	.	.	PUNCT
cet-4972	107	1	references	reference	NOUN
cet-4972	107	2	al	al	PROPN
cet-4972	107	3	-	-	PUNCT
cet-4972	107	4	dahidi	dahidi	PROPN
cet-4972	107	5	s.	s.	PROPN
cet-4972	107	6	,	,	PUNCT
cet-4972	107	7	2014	2014	NUM
cet-4972	107	8	,	,	PUNCT
cet-4972	107	9	the	the	DET
cet-4972	107	10	use	use	NOUN
cet-4972	107	11	of	of	ADP
cet-4972	107	12	self	self	NOUN
cet-4972	107	13	organizing	organize	VERB
cet-4972	107	14	maps	map	NOUN
cet-4972	107	15	for	for	ADP
cet-4972	107	16	diagnosing	diagnose	VERB
cet-4972	107	17	faults	fault	NOUN
cet-4972	107	18	in	in	ADP
cet-4972	107	19	motor	motor	NOUN
cet-4972	107	20	bearings	bearing	NOUN
cet-4972	107	21	,	,	PUNCT
cet-4972	107	22	safety	safety	NOUN
cet-4972	107	23	and	and	CCONJ
cet-4972	107	24	reliability	reliability	NOUN
cet-4972	107	25	:	:	PUNCT
cet-4972	107	26	methodology	methodology	NOUN
cet-4972	107	27	and	and	CCONJ
cet-4972	107	28	applications	application	NOUN
cet-4972	107	29	proceedings	proceeding	NOUN
cet-4972	107	30	of	of	ADP
cet-4972	107	31	the	the	DET
cet-4972	107	32	european	european	ADJ
cet-4972	107	33	safety	safety	NOUN
cet-4972	107	34	and	and	CCONJ
cet-4972	107	35	reliability	reliability	NOUN
cet-4972	107	36	conference	conference	NOUN
cet-4972	107	37	,	,	PUNCT
cet-4972	107	38	esrel	esrel	NOUN
cet-4972	107	39	2014	2014	NUM
cet-4972	107	40	,	,	PUNCT
cet-4972	107	41	wroclaw	wroclaw	NOUN
cet-4972	107	42	,	,	PUNCT
cet-4972	107	43	poland	poland	PROPN
cet-4972	107	44	,	,	PUNCT
cet-4972	107	45	september	september	PROPN
cet-4972	107	46	2014	2014	NUM
cet-4972	107	47	,	,	PUNCT
cet-4972	107	48	895	895	NUM
cet-4972	107	49	-	-	SYM
cet-4972	107	50	902	902	NUM
cet-4972	107	51	.	.	PUNCT
cet-4972	108	1	al	al	PROPN
cet-4972	108	2	-	-	PUNCT
cet-4972	108	3	dahidi	dahidi	PROPN
cet-4972	108	4	s.	s.	PROPN
cet-4972	108	5	,	,	PUNCT
cet-4972	108	6	baraldi	baraldi	PROPN
cet-4972	108	7	p.	p.	PROPN
cet-4972	108	8	,	,	PUNCT
cet-4972	108	9	di	di	PROPN
cet-4972	108	10	maio	maio	PROPN
cet-4972	108	11	f.	f.	PROPN
cet-4972	108	12	,	,	PUNCT
cet-4972	108	13	zio	zio	PROPN
cet-4972	108	14	e.	e.	PROPN
cet-4972	108	15	,	,	PUNCT
cet-4972	108	16	2014	2014	NUM
cet-4972	108	17	,	,	PUNCT
cet-4972	108	18	a	a	DET
cet-4972	108	19	novel	novel	ADJ
cet-4972	108	20	fault	fault	NOUN
cet-4972	108	21	detection	detection	NOUN
cet-4972	108	22	system	system	NOUN
cet-4972	108	23	taking	take	VERB
cet-4972	108	24	into	into	ADP
cet-4972	108	25	account	account	NOUN
cet-4972	108	26	uncertainties	uncertainty	NOUN
cet-4972	108	27	in	in	ADP
cet-4972	108	28	the	the	DET
cet-4972	108	29	reconstructed	reconstructed	ADJ
cet-4972	108	30	signals	signal	NOUN
cet-4972	108	31	,	,	PUNCT
cet-4972	108	32	annals	annal	NOUN
cet-4972	108	33	of	of	ADP
cet-4972	108	34	nuclear	nuclear	ADJ
cet-4972	108	35	energy	energy	NOUN
cet-4972	108	36	,	,	PUNCT
cet-4972	108	37	73	73	NUM
cet-4972	108	38	,	,	PUNCT
cet-4972	108	39	131–144	131–144	NUM
cet-4972	108	40	.	.	PUNCT
cet-4972	109	1	baraldi	baraldi	PROPN
cet-4972	109	2	p.	p.	PROPN
cet-4972	109	3	,	,	PUNCT
cet-4972	109	4	di	di	PROPN
cet-4972	109	5	maio	maio	PROPN
cet-4972	109	6	f.	f.	PROPN
cet-4972	109	7	,	,	PUNCT
cet-4972	109	8	zio	zio	PROPN
cet-4972	109	9	e.	e.	PROPN
cet-4972	109	10	,	,	PUNCT
cet-4972	109	11	sauco	sauco	PROPN
cet-4972	109	12	s.	s.	PROPN
cet-4972	109	13	,	,	PUNCT
cet-4972	109	14	droguett	droguett	PROPN
cet-4972	109	15	e.	e.	PROPN
cet-4972	109	16	,	,	PUNCT
cet-4972	109	17	magno	magno	PROPN
cet-4972	109	18	c.	c.	PROPN
cet-4972	109	19	,	,	PUNCT
cet-4972	109	20	2012	2012	NUM
cet-4972	109	21	,	,	PUNCT
cet-4972	109	22	sensitivity	sensitivity	NOUN
cet-4972	109	23	analysis	analysis	NOUN
cet-4972	109	24	of	of	ADP
cet-4972	109	25	the	the	DET
cet-4972	109	26	scale	scale	NOUN
cet-4972	109	27	deposition	deposition	NOUN
cet-4972	109	28	on	on	ADP
cet-4972	109	29	equipment	equipment	NOUN
cet-4972	109	30	of	of	ADP
cet-4972	109	31	oil	oil	NOUN
cet-4972	109	32	wells	well	NOUN
cet-4972	109	33	plants	plant	NOUN
cet-4972	109	34	,	,	PUNCT
cet-4972	109	35	chemical	chemical	NOUN
cet-4972	109	36	engineering	engineering	NOUN
cet-4972	109	37	transactions	transaction	NOUN
cet-4972	109	38	,	,	PUNCT
cet-4972	109	39	26	26	NUM
cet-4972	109	40	,	,	PUNCT
cet-4972	109	41	327	327	NUM
cet-4972	109	42	-	-	SYM
cet-4972	109	43	332	332	NUM
cet-4972	109	44	.	.	PUNCT
cet-4972	110	1	baraldi	baraldi	PROPN
cet-4972	110	2	p.	p.	PROPN
cet-4972	110	3	,	,	PUNCT
cet-4972	110	4	di	di	PROPN
cet-4972	110	5	maio	maio	PROPN
cet-4972	110	6	f.	f.	PROPN
cet-4972	110	7	,	,	PUNCT
cet-4972	110	8	zio	zio	PROPN
cet-4972	110	9	e.	e.	PROPN
cet-4972	110	10	,	,	PUNCT
cet-4972	110	11	2013a	2013a	NUM
cet-4972	110	12	,	,	PUNCT
cet-4972	110	13	unsupervised	unsupervised	ADJ
cet-4972	110	14	clustering	clustering	NOUN
cet-4972	110	15	for	for	ADP
cet-4972	110	16	fault	fault	NOUN
cet-4972	110	17	diagnosis	diagnosis	NOUN
cet-4972	110	18	in	in	ADP
cet-4972	110	19	nuclear	nuclear	ADJ
cet-4972	110	20	power	power	NOUN
cet-4972	110	21	plant	plant	NOUN
cet-4972	110	22	components	component	NOUN
cet-4972	110	23	,	,	PUNCT
cet-4972	110	24	international	international	ADJ
cet-4972	110	25	journal	journal	NOUN
cet-4972	110	26	of	of	ADP
cet-4972	110	27	computational	computational	ADJ
cet-4972	110	28	intelligence	intelligence	NOUN
cet-4972	110	29	systems	system	NOUN
cet-4972	110	30	,	,	PUNCT
cet-4972	110	31	6	6	NUM
cet-4972	110	32	(	(	PUNCT
cet-4972	110	33	4	4	NUM
cet-4972	110	34	)	)	PUNCT
cet-4972	110	35	,	,	PUNCT
cet-4972	110	36	764	764	NUM
cet-4972	110	37	-	-	SYM
cet-4972	110	38	777	777	NUM
cet-4972	110	39	.	.	PUNCT
cet-4972	111	1	baraldi	baraldi	PROPN
cet-4972	111	2	p.	p.	PROPN
cet-4972	111	3	,	,	PUNCT
cet-4972	111	4	di	di	PROPN
cet-4972	111	5	maio	maio	PROPN
cet-4972	111	6	f.	f.	PROPN
cet-4972	111	7	,	,	PUNCT
cet-4972	111	8	rigamonti	rigamonti	PROPN
cet-4972	111	9	m.	m.	PROPN
cet-4972	111	10	,	,	PUNCT
cet-4972	111	11	zio	zio	PROPN
cet-4972	111	12	e.	e.	PROPN
cet-4972	111	13	,	,	PUNCT
cet-4972	111	14	seraoui	seraoui	PROPN
cet-4972	111	15	r.	r.	PROPN
cet-4972	111	16	,	,	PUNCT
cet-4972	111	17	2013b	2013b	NUM
cet-4972	111	18	,	,	PUNCT
cet-4972	111	19	transients	transient	VERB
cet-4972	111	20	analysis	analysis	NOUN
cet-4972	111	21	of	of	ADP
cet-4972	111	22	a	a	DET
cet-4972	111	23	nuclear	nuclear	ADJ
cet-4972	111	24	power	power	NOUN
cet-4972	111	25	plant	plant	NOUN
cet-4972	111	26	component	component	NOUN
cet-4972	111	27	for	for	ADP
cet-4972	111	28	fault	fault	NOUN
cet-4972	111	29	diagnosis	diagnosis	NOUN
cet-4972	111	30	,	,	PUNCT
cet-4972	111	31	chemical	chemical	ADJ
cet-4972	111	32	engineering	engineering	NOUN
cet-4972	111	33	transactions	transaction	NOUN
cet-4972	111	34	,	,	PUNCT
cet-4972	111	35	33	33	NUM
cet-4972	111	36	,	,	PUNCT
cet-4972	111	37	895	895	NUM
cet-4972	111	38	-	-	SYM
cet-4972	111	39	900	900	NUM
cet-4972	111	40	.	.	PUNCT
cet-4972	112	1	baraldi	baraldi	PROPN
cet-4972	112	2	p.	p.	PROPN
cet-4972	112	3	,	,	PUNCT
cet-4972	112	4	di	di	PROPN
cet-4972	112	5	maio	maio	PROPN
cet-4972	112	6	f.	f.	PROPN
cet-4972	112	7	,	,	PUNCT
cet-4972	112	8	rigamonti	rigamonti	PROPN
cet-4972	112	9	m.	m.	PROPN
cet-4972	112	10	,	,	PUNCT
cet-4972	112	11	zio	zio	PROPN
cet-4972	112	12	e.	e.	PROPN
cet-4972	112	13	,	,	PUNCT
cet-4972	112	14	seraoui	seraoui	PROPN
cet-4972	112	15	r.	r.	PROPN
cet-4972	112	16	,	,	PUNCT
cet-4972	112	17	2014	2014	NUM
cet-4972	112	18	,	,	PUNCT
cet-4972	112	19	unsupervised	unsupervised	ADJ
cet-4972	112	20	clustering	clustering	NOUN
cet-4972	112	21	of	of	ADP
cet-4972	112	22	vibration	vibration	NOUN
cet-4972	112	23	signals	signal	NOUN
cet-4972	112	24	for	for	ADP
cet-4972	112	25	identifying	identify	VERB
cet-4972	112	26	anomalous	anomalous	ADJ
cet-4972	112	27	conditions	condition	NOUN
cet-4972	112	28	in	in	ADP
cet-4972	112	29	a	a	DET
cet-4972	112	30	nuclear	nuclear	ADJ
cet-4972	112	31	turbine	turbine	NOUN
cet-4972	112	32	,	,	PUNCT
cet-4972	112	33	accepted	accept	VERB
cet-4972	112	34	,	,	PUNCT
cet-4972	112	35	journal	journal	NOUN
cet-4972	112	36	of	of	ADP
cet-4972	112	37	intelligent	intelligent	ADJ
cet-4972	112	38	and	and	CCONJ
cet-4972	112	39	fuzzy	fuzzy	ADJ
cet-4972	112	40	systems	system	NOUN
cet-4972	112	41	.	.	PUNCT
cet-4972	113	1	chakaravathy	chakaravathy	PROPN
cet-4972	113	2	s.	s.	PROPN
cet-4972	113	3	v.	v.	PROPN
cet-4972	113	4	,	,	PUNCT
cet-4972	113	5	ghosh	ghosh	PROPN
cet-4972	113	6	j.	j.	PROPN
cet-4972	113	7	,	,	PUNCT
cet-4972	113	8	1996	1996	NUM
cet-4972	113	9	,	,	PUNCT
cet-4972	113	10	scale	scale	NOUN
cet-4972	113	11	based	base	VERB
cet-4972	113	12	clustering	clustering	NOUN
cet-4972	113	13	using	use	VERB
cet-4972	113	14	a	a	DET
cet-4972	113	15	radial	radial	ADJ
cet-4972	113	16	basis	basis	NOUN
cet-4972	113	17	function	function	NOUN
cet-4972	113	18	network	network	NOUN
cet-4972	113	19	,	,	PUNCT
cet-4972	113	20	ieee	ieee	NOUN
cet-4972	113	21	transactions	transaction	NOUN
cet-4972	113	22	on	on	ADP
cet-4972	113	23	neural	neural	ADJ
cet-4972	113	24	networks	network	NOUN
cet-4972	113	25	,	,	PUNCT
cet-4972	113	26	2(5	2(5	NUM
cet-4972	113	27	)	)	PUNCT
cet-4972	113	28	,	,	PUNCT
cet-4972	113	29	1250–61	1250–61	NUM
cet-4972	113	30	.	.	PUNCT
cet-4972	114	1	chen	chen	PROPN
cet-4972	114	2	k.	k.	PROPN
cet-4972	114	3	,	,	PUNCT
cet-4972	114	4	2007	2007	NUM
cet-4972	114	5	,	,	PUNCT
cet-4972	114	6	trends	trend	NOUN
cet-4972	114	7	in	in	ADP
cet-4972	114	8	neural	neural	ADJ
cet-4972	114	9	computation	computation	NOUN
cet-4972	114	10	,	,	PUNCT
cet-4972	114	11	springer	springer	NOUN
cet-4972	114	12	.	.	PUNCT
cet-4972	115	1	davies	davies	PROPN
cet-4972	115	2	d.l	d.l	PROPN
cet-4972	115	3	.	.	PROPN
cet-4972	115	4	,	,	PUNCT
cet-4972	115	5	bouldin	bouldin	PROPN
cet-4972	115	6	d.w	d.w	PROPN
cet-4972	115	7	.	.	PROPN
cet-4972	115	8	,	,	PUNCT
cet-4972	115	9	1979	1979	NUM
cet-4972	115	10	,	,	PUNCT
cet-4972	115	11	a	a	DET
cet-4972	115	12	cluster	cluster	NOUN
cet-4972	115	13	separation	separation	NOUN
cet-4972	115	14	measure	measure	NOUN
cet-4972	115	15	,	,	PUNCT
cet-4972	115	16	ieee	ieee	PROPN
cet-4972	115	17	trans	tran	NOUN
cet-4972	115	18	.	.	PUNCT
cet-4972	116	1	pattern	pattern	NOUN
cet-4972	116	2	analysis	analysis	NOUN
cet-4972	116	3	and	and	CCONJ
cet-4972	116	4	machine	machine	NOUN
cet-4972	116	5	intelligence	intelligence	NOUN
cet-4972	116	6	,	,	PUNCT
cet-4972	116	7	1	1	NUM
cet-4972	116	8	,	,	PUNCT
cet-4972	116	9	224	224	NUM
cet-4972	116	10	-	-	SYM
cet-4972	116	11	227	227	NUM
cet-4972	116	12	.	.	PUNCT
cet-4972	117	1	demichela	demichela	PROPN
cet-4972	117	2	,	,	PUNCT
cet-4972	117	3	m.	m.	NOUN
cet-4972	117	4	,	,	PUNCT
cet-4972	117	5	camuncoli	camuncoli	NOUN
cet-4972	117	6	,	,	PUNCT
cet-4972	117	7	g.	g.	PROPN
cet-4972	117	8	,	,	PUNCT
cet-4972	117	9	2014	2014	NUM
cet-4972	117	10	,	,	PUNCT
cet-4972	117	11	risk	risk	NOUN
cet-4972	117	12	based	base	VERB
cet-4972	117	13	decision	decision	NOUN
cet-4972	117	14	making	making	NOUN
cet-4972	117	15	.	.	PUNCT
cet-4972	118	1	discussion	discussion	NOUN
cet-4972	118	2	on	on	ADP
cet-4972	118	3	two	two	NUM
cet-4972	118	4	methodological	methodological	ADJ
cet-4972	118	5	milestones	milestone	NOUN
cet-4972	118	6	,	,	PUNCT
cet-4972	118	7	journal	journal	NOUN
cet-4972	118	8	of	of	ADP
cet-4972	118	9	loss	loss	NOUN
cet-4972	118	10	prevention	prevention	NOUN
cet-4972	118	11	in	in	ADP
cet-4972	118	12	the	the	DET
cet-4972	118	13	process	process	NOUN
cet-4972	118	14	industries	industry	NOUN
cet-4972	118	15	,	,	PUNCT
cet-4972	118	16	28	28	NUM
cet-4972	118	17	(	(	PUNCT
cet-4972	118	18	1	1	NUM
cet-4972	118	19	)	)	PUNCT
cet-4972	118	20	,	,	PUNCT
cet-4972	118	21	01	01	NUM
cet-4972	118	22	-	-	SYM
cet-4972	118	23	108	108	NUM
cet-4972	118	24	.	.	PUNCT
cet-4972	118	25	di	di	PROPN
cet-4972	118	26	maio	maio	PROPN
cet-4972	118	27	f.	f.	PROPN
cet-4972	118	28	,	,	PUNCT
cet-4972	118	29	secchi	secchi	PROPN
cet-4972	118	30	p.	p.	PROPN
cet-4972	118	31	,	,	PUNCT
cet-4972	118	32	vantini	vantini	PROPN
cet-4972	118	33	s.	s.	PROPN
cet-4972	118	34	,	,	PUNCT
cet-4972	118	35	zio	zio	PROPN
cet-4972	118	36	e.	e.	PROPN
cet-4972	118	37	,	,	PUNCT
cet-4972	118	38	2011	2011	NUM
cet-4972	118	39	,	,	PUNCT
cet-4972	118	40	fuzzy	fuzzy	ADJ
cet-4972	118	41	c	c	NOUN
cet-4972	118	42	-	-	PUNCT
cet-4972	118	43	means	means	NOUN
cet-4972	118	44	clustering	clustering	NOUN
cet-4972	118	45	of	of	ADP
cet-4972	118	46	signal	signal	ADJ
cet-4972	118	47	functional	functional	ADJ
cet-4972	118	48	principal	principal	ADJ
cet-4972	118	49	components	component	NOUN
cet-4972	118	50	for	for	ADP
cet-4972	118	51	post	post	ADJ
cet-4972	118	52	-	-	ADJ
cet-4972	118	53	processing	processing	ADJ
cet-4972	118	54	dynamic	dynamic	ADJ
cet-4972	118	55	scenarios	scenario	NOUN
cet-4972	118	56	of	of	ADP
cet-4972	118	57	a	a	DET
cet-4972	118	58	nuclear	nuclear	ADJ
cet-4972	118	59	power	power	NOUN
cet-4972	118	60	plant	plant	NOUN
cet-4972	118	61	digital	digital	ADJ
cet-4972	118	62	instrumentation	instrumentation	NOUN
cet-4972	118	63	and	and	CCONJ
cet-4972	118	64	control	control	NOUN
cet-4972	118	65	system	system	NOUN
cet-4972	118	66	,	,	PUNCT
cet-4972	118	67	ieee	ieee	NOUN
cet-4972	118	68	–	–	PUNCT
cet-4972	118	69	transactions	transaction	NOUN
cet-4972	118	70	on	on	ADP
cet-4972	118	71	reliability	reliability	NOUN
cet-4972	118	72	,	,	PUNCT
cet-4972	118	73	60(2	60(2	NUM
cet-4972	118	74	)	)	PUNCT
cet-4972	118	75	,	,	PUNCT
cet-4972	118	76	june	june	PROPN
cet-4972	118	77	2011	2011	NUM
cet-4972	118	78	,	,	PUNCT
cet-4972	118	79	415	415	NUM
cet-4972	118	80	-	-	SYM
cet-4972	118	81	425	425	NUM
cet-4972	118	82	.	.	PUNCT
cet-4972	118	83	di	di	PROPN
cet-4972	118	84	maio	maio	PROPN
cet-4972	118	85	f.	f.	PROPN
cet-4972	118	86	,	,	PUNCT
cet-4972	118	87	hu	hu	PROPN
cet-4972	118	88	j.	j.	PROPN
cet-4972	118	89	,	,	PUNCT
cet-4972	118	90	tse	tse	PROPN
cet-4972	118	91	p.	p.	PROPN
cet-4972	118	92	,	,	PUNCT
cet-4972	118	93	pecht	pecht	NOUN
cet-4972	118	94	m.	m.	NOUN
cet-4972	118	95	,	,	PUNCT
cet-4972	118	96	tsui	tsui	PROPN
cet-4972	118	97	k.	k.	PROPN
cet-4972	118	98	,	,	PUNCT
cet-4972	118	99	zio	zio	PROPN
cet-4972	118	100	e.	e.	PROPN
cet-4972	118	101	,	,	PUNCT
cet-4972	118	102	2012	2012	NUM
cet-4972	118	103	,	,	PUNCT
cet-4972	118	104	ensemble	ensemble	NOUN
cet-4972	118	105	-	-	PUNCT
cet-4972	118	106	approaches	approach	NOUN
cet-4972	118	107	for	for	ADP
cet-4972	118	108	clustering	cluster	VERB
cet-4972	118	109	health	health	NOUN
cet-4972	118	110	status	status	NOUN
cet-4972	118	111	of	of	ADP
cet-4972	118	112	oil	oil	NOUN
cet-4972	118	113	sand	sand	NOUN
cet-4972	118	114	pumps	pump	NOUN
cet-4972	118	115	,	,	PUNCT
cet-4972	118	116	expert	expert	NOUN
cet-4972	118	117	systems	system	NOUN
cet-4972	118	118	with	with	ADP
cet-4972	118	119	applications	application	NOUN
cet-4972	118	120	39	39	NUM
cet-4972	118	121	,	,	PUNCT
cet-4972	118	122	5	5	NUM
cet-4972	118	123	(	(	PUNCT
cet-4972	118	124	2012	2012	NUM
cet-4972	118	125	)	)	PUNCT
cet-4972	118	126	4847	4847	NUM
cet-4972	118	127	-	-	SYM
cet-4972	118	128	4859	4859	NUM
cet-4972	118	129	.	.	PUNCT
cet-4972	119	1	dimitriadou	dimitriadou	PROPN
cet-4972	119	2	e.	e.	PROPN
cet-4972	119	3	,	,	PUNCT
cet-4972	119	4	weingessel	weingessel	PROPN
cet-4972	119	5	a.	a.	NOUN
cet-4972	119	6	,	,	PUNCT
cet-4972	119	7	homik	homik	VERB
cet-4972	119	8	k.	k.	PROPN
cet-4972	119	9	,	,	PUNCT
cet-4972	119	10	2001	2001	NUM
cet-4972	119	11	,	,	PUNCT
cet-4972	119	12	voting	voting	NOUN
cet-4972	119	13	-	-	PUNCT
cet-4972	119	14	merging	merging	NOUN
cet-4972	119	15	:	:	PUNCT
cet-4972	119	16	an	an	DET
cet-4972	119	17	ensemble	ensemble	ADJ
cet-4972	119	18	method	method	NOUN
cet-4972	119	19	for	for	ADP
cet-4972	119	20	clustering	clustering	NOUN
cet-4972	119	21	,	,	PUNCT
cet-4972	119	22	in	in	ADP
cet-4972	119	23	proc	proc	NOUN
cet-4972	119	24	.	.	PUNCT
cet-4972	120	1	2001	2001	NUM
cet-4972	120	2	int	int	NOUN
cet-4972	120	3	.	.	PUNCT
cet-4972	120	4	conf	conf	NOUN
cet-4972	120	5	.	.	PUNCT
cet-4972	121	1	artificial	artificial	ADJ
cet-4972	121	2	neural	neural	ADJ
cet-4972	121	3	networks	network	NOUN
cet-4972	121	4	(	(	PUNCT
cet-4972	121	5	icann'01	icann'01	PROPN
cet-4972	121	6	)	)	PUNCT
cet-4972	121	7	,	,	PUNCT
cet-4972	121	8	217	217	NUM
cet-4972	121	9	-	-	SYM
cet-4972	121	10	224	224	NUM
cet-4972	121	11	.	.	PUNCT
cet-4972	122	1	fern	fern	PROPN
cet-4972	122	2	x.	x.	PROPN
cet-4972	122	3	z.	z.	PROPN
cet-4972	122	4	,	,	PUNCT
cet-4972	122	5	lin	lin	PROPN
cet-4972	122	6	w.	w.	PROPN
cet-4972	122	7	,	,	PUNCT
cet-4972	122	8	2008	2008	NUM
cet-4972	122	9	,	,	PUNCT
cet-4972	122	10	cluster	cluster	NOUN
cet-4972	122	11	ensemble	ensemble	ADJ
cet-4972	122	12	selection	selection	NOUN
cet-4972	122	13	,	,	PUNCT
cet-4972	122	14	statistical	statistical	ADJ
cet-4972	122	15	analysis	analysis	NOUN
cet-4972	122	16	and	and	CCONJ
cet-4972	122	17	data	datum	NOUN
cet-4972	122	18	mining	mining	NOUN
cet-4972	122	19	,	,	PUNCT
cet-4972	122	20	1(3	1(3	NUM
cet-4972	122	21	)	)	PUNCT
cet-4972	122	22	,	,	PUNCT
cet-4972	122	23	128	128	NUM
cet-4972	122	24	-	-	SYM
cet-4972	122	25	141	141	NUM
cet-4972	122	26	.	.	PUNCT
cet-4972	123	1	fred	fred	PROPN
cet-4972	123	2	a.	a.	PROPN
cet-4972	123	3	l.	l.	PROPN
cet-4972	123	4	,	,	PUNCT
cet-4972	123	5	jain	jain	PROPN
cet-4972	123	6	a.	a.	PROPN
cet-4972	123	7	k.	k.	PROPN
cet-4972	123	8	,	,	PUNCT
cet-4972	123	9	2005	2005	NUM
cet-4972	123	10	,	,	PUNCT
cet-4972	123	11	combining	combine	VERB
cet-4972	123	12	multiple	multiple	ADJ
cet-4972	123	13	clusterings	clustering	NOUN
cet-4972	123	14	using	use	VERB
cet-4972	123	15	evidence	evidence	NOUN
cet-4972	123	16	accumulation	accumulation	NOUN
cet-4972	123	17	,	,	PUNCT
cet-4972	123	18	pattern	pattern	NOUN
cet-4972	123	19	analysis	analysis	NOUN
cet-4972	123	20	and	and	CCONJ
cet-4972	123	21	machine	machine	NOUN
cet-4972	123	22	intelligence	intelligence	NOUN
cet-4972	123	23	,	,	PUNCT
cet-4972	123	24	ieee	ieee	NOUN
cet-4972	123	25	transactions	transaction	NOUN
cet-4972	123	26	on	on	ADP
cet-4972	123	27	,	,	PUNCT
cet-4972	123	28	27(6	27(6	NUM
cet-4972	123	29	)	)	PUNCT
cet-4972	123	30	,	,	PUNCT
cet-4972	123	31	835	835	NUM
cet-4972	123	32	-	-	SYM
cet-4972	123	33	850	850	NUM
cet-4972	123	34	.	.	PUNCT
cet-4972	124	1	karypis	karypis	PROPN
cet-4972	124	2	g.	g.	PROPN
cet-4972	124	3	,	,	PUNCT
cet-4972	124	4	kumar	kumar	PROPN
cet-4972	124	5	v.	v.	PROPN
cet-4972	124	6	,	,	PUNCT
cet-4972	124	7	1995	1995	NUM
cet-4972	124	8	,	,	PUNCT
cet-4972	124	9	metis	metis	NOUN
cet-4972	124	10	unstructured	unstructured	ADJ
cet-4972	124	11	graph	graph	NOUN
cet-4972	124	12	partitioning	partition	VERB
cet-4972	124	13	and	and	CCONJ
cet-4972	124	14	sparse	sparse	ADJ
cet-4972	124	15	matrix	matrix	NOUN
cet-4972	124	16	ordering	ordering	NOUN
cet-4972	124	17	system	system	NOUN
cet-4972	124	18	,	,	PUNCT
cet-4972	124	19	version	version	NOUN
cet-4972	124	20	2.0	2.0	NUM
cet-4972	124	21	(	(	PUNCT
cet-4972	124	22	technical	technical	ADJ
cet-4972	124	23	report	report	NOUN
cet-4972	124	24	)	)	PUNCT
cet-4972	124	25	.	.	PUNCT
cet-4972	125	1	liao	liao	PROPN
cet-4972	125	2	t.	t.	PROPN
cet-4972	125	3	,	,	PUNCT
cet-4972	125	4	bolt	bolt	PROPN
cet-4972	125	5	b.	b.	PROPN
cet-4972	125	6	,	,	PUNCT
cet-4972	125	7	2002	2002	NUM
cet-4972	125	8	,	,	PUNCT
cet-4972	125	9	understanding	understanding	NOUN
cet-4972	125	10	and	and	CCONJ
cet-4972	125	11	projecting	project	VERB
cet-4972	125	12	the	the	DET
cet-4972	125	13	battle	battle	NOUN
cet-4972	125	14	state	state	NOUN
cet-4972	125	15	,	,	PUNCT
cet-4972	125	16	23rd	23rd	ADJ
cet-4972	125	17	army	army	NOUN
cet-4972	125	18	science	science	NOUN
cet-4972	125	19	conf	conf	PROPN
cet-4972	125	20	.	.	PROPN
cet-4972	125	21	,	,	PUNCT
cet-4972	125	22	orlando	orlando	PROPN
cet-4972	125	23	,	,	PUNCT
cet-4972	125	24	fl	fl	PROPN
cet-4972	125	25	.	.	PUNCT
cet-4972	125	26	piccinini	piccinini	PROPN
cet-4972	125	27	,	,	PUNCT
cet-4972	125	28	n.	n.	NOUN
cet-4972	125	29	,	,	PUNCT
cet-4972	125	30	demichela	demichela	PROPN
cet-4972	125	31	,	,	PUNCT
cet-4972	125	32	m.	m.	NOUN
cet-4972	125	33	,	,	PUNCT
cet-4972	125	34	2008	2008	NUM
cet-4972	125	35	,	,	PUNCT
cet-4972	125	36	risk	risk	NOUN
cet-4972	125	37	based	base	VERB
cet-4972	125	38	decision	decision	NOUN
cet-4972	125	39	-	-	PUNCT
cet-4972	125	40	making	making	NOUN
cet-4972	125	41	in	in	ADP
cet-4972	125	42	plant	plant	NOUN
cet-4972	125	43	design	design	NOUN
cet-4972	125	44	,	,	PUNCT
cet-4972	125	45	canadian	canadian	ADJ
cet-4972	125	46	journal	journal	NOUN
cet-4972	125	47	of	of	ADP
cet-4972	125	48	chemical	chemical	PROPN
cet-4972	125	49	engineering	engineering	NOUN
cet-4972	125	50	,	,	PUNCT
cet-4972	125	51	86	86	NUM
cet-4972	125	52	(	(	PUNCT
cet-4972	125	53	3	3	NUM
cet-4972	125	54	)	)	PUNCT
cet-4972	125	55	,	,	PUNCT
cet-4972	125	56	pp	pp	PROPN
cet-4972	125	57	.	.	PUNCT
cet-4972	126	1	316	316	NUM
cet-4972	126	2	-	-	SYM
cet-4972	126	3	322	322	NUM
cet-4972	126	4	.	.	PUNCT
cet-4972	127	1	rousseeuw	rousseeuw	PROPN
cet-4972	127	2	p.	p.	PROPN
cet-4972	127	3	,	,	PUNCT
cet-4972	127	4	1987	1987	NUM
cet-4972	127	5	,	,	PUNCT
cet-4972	127	6	silhouettes	silhouette	NOUN
cet-4972	127	7	:	:	PUNCT
cet-4972	127	8	a	a	DET
cet-4972	127	9	graphical	graphical	ADJ
cet-4972	127	10	aid	aid	NOUN
cet-4972	127	11	to	to	ADP
cet-4972	127	12	the	the	DET
cet-4972	127	13	interpretation	interpretation	NOUN
cet-4972	127	14	and	and	CCONJ
cet-4972	127	15	validation	validation	NOUN
cet-4972	127	16	of	of	ADP
cet-4972	127	17	cluster	cluster	NOUN
cet-4972	127	18	analysis	analysis	NOUN
cet-4972	127	19	.	.	PUNCT
cet-4972	128	1	journal	journal	NOUN
cet-4972	128	2	of	of	ADP
cet-4972	128	3	computational	computational	ADJ
cet-4972	128	4	and	and	CCONJ
cet-4972	128	5	applied	applied	ADJ
cet-4972	128	6	mathematics	mathematic	NOUN
cet-4972	128	7	53–65	53–65	NUM
cet-4972	128	8	.	.	PUNCT
cet-4972	129	1	salvador	salvador	PROPN
cet-4972	129	2	a.	a.	PROPN
cet-4972	129	3	,	,	PUNCT
cet-4972	129	4	2002	2002	NUM
cet-4972	129	5	,	,	PUNCT
cet-4972	129	6	faults	fault	VERB
cet-4972	129	7	diagnosis	diagnosis	NOUN
cet-4972	129	8	in	in	ADP
cet-4972	129	9	industrial	industrial	ADJ
cet-4972	129	10	processes	process	NOUN
cet-4972	129	11	with	with	ADP
cet-4972	129	12	a	a	DET
cet-4972	129	13	hybrid	hybrid	ADJ
cet-4972	129	14	diagnostic	diagnostic	ADJ
cet-4972	129	15	system	system	NOUN
cet-4972	129	16	,	,	PUNCT
cet-4972	129	17	in	in	ADP
cet-4972	129	18	micai	micai	NOUN
cet-4972	129	19	2002	2002	NUM
cet-4972	129	20	:	:	PUNCT
cet-4972	129	21	advances	advance	NOUN
cet-4972	129	22	in	in	ADP
cet-4972	129	23	artificial	artificial	ADJ
cet-4972	129	24	intelligence	intelligence	NOUN
cet-4972	129	25	,	,	PUNCT
cet-4972	129	26	536	536	NUM
cet-4972	129	27	-	-	SYM
cet-4972	129	28	545	545	NUM
cet-4972	129	29	.	.	PUNCT
cet-4972	130	1	springer	springer	PROPN
cet-4972	130	2	berlin	berlin	PROPN
cet-4972	130	3	heidelberg	heidelberg	PROPN
cet-4972	130	4	.	.	PUNCT
cet-4972	131	1	strehl	strehl	PROPN
cet-4972	131	2	a.	a.	PROPN
cet-4972	131	3	,	,	PUNCT
cet-4972	131	4	ghosh	ghosh	PROPN
cet-4972	131	5	j.	j.	PROPN
cet-4972	131	6	,	,	PUNCT
cet-4972	131	7	2002	2002	NUM
cet-4972	131	8	,	,	PUNCT
cet-4972	131	9	cluster	cluster	NOUN
cet-4972	131	10	ensembles	ensemble	NOUN
cet-4972	131	11	-	-	PUNCT
cet-4972	131	12	a	a	DET
cet-4972	131	13	knowledge	knowledge	NOUN
cet-4972	131	14	reuse	reuse	NOUN
cet-4972	131	15	framework	framework	NOUN
cet-4972	131	16	for	for	ADP
cet-4972	131	17	combining	combine	VERB
cet-4972	131	18	partitionings	partitioning	NOUN
cet-4972	131	19	,	,	PUNCT
cet-4972	131	20	in	in	ADP
cet-4972	131	21	aaai	aaai	PROPN
cet-4972	131	22	/	/	SYM
cet-4972	131	23	iaai	iaai	PROPN
cet-4972	131	24	,	,	PUNCT
cet-4972	131	25	93	93	NUM
cet-4972	131	26	-	-	SYM
cet-4972	131	27	99	99	NUM
cet-4972	131	28	.	.	PUNCT
cet-4972	132	1	su	su	PROPN
cet-4972	132	2	m.	m.	PROPN
cet-4972	132	3	c.	c.	PROPN
cet-4972	132	4	,	,	PUNCT
cet-4972	132	5	chou	chou	PROPN
cet-4972	132	6	c.	c.	PROPN
cet-4972	132	7	h.	h.	PROPN
cet-4972	132	8	,	,	PUNCT
cet-4972	132	9	2001	2001	NUM
cet-4972	132	10	,	,	PUNCT
cet-4972	132	11	a	a	DET
cet-4972	132	12	modified	modify	VERB
cet-4972	132	13	version	version	NOUN
cet-4972	132	14	of	of	ADP
cet-4972	132	15	the	the	DET
cet-4972	132	16	k	k	NOUN
cet-4972	132	17	-	-	PUNCT
cet-4972	132	18	means	means	NOUN
cet-4972	132	19	algorithm	algorithm	NOUN
cet-4972	132	20	with	with	ADP
cet-4972	132	21	a	a	DET
cet-4972	132	22	distance	distance	NOUN
cet-4972	132	23	based	base	VERB
cet-4972	132	24	on	on	ADP
cet-4972	132	25	cluster	cluster	NOUN
cet-4972	132	26	symmetry	symmetry	NOUN
cet-4972	132	27	,	,	PUNCT
cet-4972	132	28	ieee	ieee	NOUN
cet-4972	132	29	transactions	transaction	NOUN
cet-4972	132	30	on	on	ADP
cet-4972	132	31	pattern	pattern	NOUN
cet-4972	132	32	analysis	analysis	NOUN
cet-4972	132	33	and	and	CCONJ
cet-4972	132	34	machine	machine	NOUN
cet-4972	132	35	intelligence	intelligence	NOUN
cet-4972	132	36	,	,	PUNCT
cet-4972	132	37	23(6	23(6	NUM
cet-4972	132	38	)	)	PUNCT
cet-4972	132	39	,	,	PUNCT
cet-4972	132	40	674	674	NUM
cet-4972	132	41	-	-	SYM
cet-4972	132	42	680	680	NUM
cet-4972	132	43	.	.	PUNCT
cet-4972	133	1	iso	iso	PROPN
cet-4972	133	2	690	690	NUM
cet-4972	133	3	.	.	PUNCT
cet-4972	134	1	topchy	topchy	PROPN
cet-4972	134	2	a.	a.	PROPN
cet-4972	134	3	,	,	PUNCT
cet-4972	134	4	jain	jain	PROPN
cet-4972	134	5	a.	a.	PROPN
cet-4972	134	6	k.	k.	PROPN
cet-4972	134	7	,	,	PUNCT
cet-4972	134	8	punch	punch	VERB
cet-4972	134	9	w.	w.	NOUN
cet-4972	134	10	,	,	PUNCT
cet-4972	134	11	2005	2005	NUM
cet-4972	134	12	,	,	PUNCT
cet-4972	134	13	clustering	clustering	ADJ
cet-4972	134	14	ensembles	ensemble	NOUN
cet-4972	134	15	:	:	PUNCT
cet-4972	134	16	models	model	NOUN
cet-4972	134	17	of	of	ADP
cet-4972	134	18	consensus	consensus	NOUN
cet-4972	134	19	and	and	CCONJ
cet-4972	134	20	weak	weak	ADJ
cet-4972	134	21	partitions	partition	NOUN
cet-4972	134	22	,	,	PUNCT
cet-4972	134	23	pattern	pattern	NOUN
cet-4972	134	24	analysis	analysis	NOUN
cet-4972	134	25	and	and	CCONJ
cet-4972	134	26	machine	machine	NOUN
cet-4972	134	27	intelligence	intelligence	NOUN
cet-4972	134	28	,	,	PUNCT
cet-4972	134	29	ieee	ieee	NOUN
cet-4972	134	30	transactions	transaction	NOUN
cet-4972	134	31	on	on	ADP
cet-4972	134	32	,	,	PUNCT
cet-4972	134	33	27(12	27(12	NUM
cet-4972	134	34	)	)	PUNCT
cet-4972	134	35	,	,	PUNCT
cet-4972	134	36	1866	1866	NUM
cet-4972	134	37	-	-	SYM
cet-4972	134	38	1881	1881	NUM
cet-4972	134	39	.	.	PUNCT
cet-4972	135	1	vega	vega	NOUN
cet-4972	135	2	-	-	PUNCT
cet-4972	135	3	pons	pon	NOUN
cet-4972	135	4	s.	s.	PROPN
cet-4972	135	5	,	,	PUNCT
cet-4972	135	6	ruiz	ruiz	NOUN
cet-4972	135	7	-	-	PUNCT
cet-4972	135	8	shulcloper	shulcloper	NOUN
cet-4972	135	9	j.	j.	PROPN
cet-4972	135	10	,	,	PUNCT
cet-4972	135	11	2011	2011	NUM
cet-4972	135	12	,	,	PUNCT
cet-4972	135	13	a	a	DET
cet-4972	135	14	survey	survey	NOUN
cet-4972	135	15	of	of	ADP
cet-4972	135	16	clustering	cluster	VERB
cet-4972	135	17	ensemble	ensemble	ADJ
cet-4972	135	18	algorithms	algorithm	NOUN
cet-4972	135	19	,	,	PUNCT
cet-4972	135	20	international	international	ADJ
cet-4972	135	21	journal	journal	NOUN
cet-4972	135	22	of	of	ADP
cet-4972	135	23	pattern	pattern	NOUN
cet-4972	135	24	recognition	recognition	NOUN
cet-4972	135	25	and	and	CCONJ
cet-4972	135	26	artificial	artificial	ADJ
cet-4972	135	27	intelligence	intelligence	NOUN
cet-4972	135	28	,	,	PUNCT
cet-4972	135	29	25(03	25(03	NOUN
cet-4972	135	30	)	)	PUNCT
cet-4972	135	31	,	,	PUNCT
cet-4972	135	32	337	337	NUM
cet-4972	135	33	-	-	SYM
cet-4972	135	34	372	372	NUM
cet-4972	135	35	.	.	PUNCT
cet-4972	136	1	von	von	PROPN
cet-4972	136	2	luxburg	luxburg	PROPN
cet-4972	136	3	u.	u.	PROPN
cet-4972	136	4	,	,	PUNCT
cet-4972	136	5	2007	2007	NUM
cet-4972	136	6	,	,	PUNCT
cet-4972	136	7	a	a	DET
cet-4972	136	8	tutorial	tutorial	NOUN
cet-4972	136	9	on	on	ADP
cet-4972	136	10	spectral	spectral	ADJ
cet-4972	136	11	clustering	clustering	NOUN
cet-4972	136	12	,	,	PUNCT
cet-4972	136	13	statistics	statistic	NOUN
cet-4972	136	14	and	and	CCONJ
cet-4972	136	15	computing	computing	NOUN
cet-4972	136	16	,	,	PUNCT
cet-4972	136	17	17(4	17(4	NUM
cet-4972	136	18	)	)	PUNCT
cet-4972	136	19	,	,	PUNCT
cet-4972	136	20	395	395	NUM
cet-4972	136	21	-	-	SYM
cet-4972	136	22	416	416	NUM
cet-4972	136	23	.	.	PUNCT
cet-4972	136	24	1230	1230	NUM