id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
ap-931	Haluzová, P.	Effective Data Mining for a Transportation Information System	2008	6	.pdf	application/pdf	4321	191	58	LnNo number line number PorNo number line order EvCislo text tram-car license number Vozovna number number of the depot (1–8), from which the tram departed A number ID of tram stop B number number of the stop post C number delay (overtake recorded as minus) D number reserve column, without data Table 2: Attributes of daytime traffic tables name of attribute Meaning name of category values of attributes weekday days in a week Monday to Sunday 1 to 7 hour hour interval (e.g. 7 means an interval from 7:00 to 7:59) morning 5, 6 morning rush hour 7, 8 before noon 9, 10 noon 11, 12 afternoon 13, 14 afternoon rush hour 15, 16 evening 17, 18, 19, 20 late evening 21, 22, 23 average delay average delay [s] under 1 min �0, 59� from 1 to 2 min �60, 119� from 2 to 3 min �120, 179� over 3 min > 179 Table 3: Meaning and categories of attributes for each month, generates about 20 000 duplicate records, this corresponds to 1.3 % of the total. The rules fall into the Ant~Suc form in the first class, and the so-called contingent rules fall into the 24 © Czech Technical University Publishing House http://ctn.cvut.cz/ap/ Acta Polytechnica Vol. 48 No. 1/2008 Effective Data Mining for a Transportation Information System P. Haluzová This paper describes the application of data mining methods in the database of the DORIS transportation information system, currently used by the Prague Public Transit Company.	cache/ap-931.pdf	txt/ap-931.txt