A fast parallel algorithm for frequent itemsets mining

Souliou, D; Pagourtzis, A; Tsanakas, P

dc.contributor.author	Souliou, D	en
dc.contributor.author	Pagourtzis, A	en
dc.contributor.author	Tsanakas, P	en
dc.date.accessioned	2014-03-01T02:44:22Z
dc.date.available	2014-03-01T02:44:22Z
dc.date.issued	2007	en
dc.identifier.issn	15715736	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31786
dc.subject	Association rules	en
dc.subject	Frequent itemsets	en
dc.subject	Parallel data mining	en
dc.subject	Partial support tree	en
dc.subject	Set-enumeration tree	en
dc.title	A fast parallel algorithm for frequent itemsets mining	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-0-387-74161-1_23	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-0-387-74161-1_23	en
heal.publicationDate	2007	en
heal.abstract	Mining frequent itemsets from large databases is an important computational task with a lot of applications. The most known among them is the market-basket problem which assumes that we have a large number of items and we want to know which items are bought together. A recent application is that of web pages (baskets) and linked pages (items). Pages with many common references may be about the same topic. In this paper we present a parallel algorithm for mining frequent itemsets. We provide experimental evidence that our algorithm scales quite well and we discuss the merits of parallelization for this problem. © 2007 International Federation for Information Processing.	en
heal.journalName	IFIP International Federation for Information Processing	en
dc.identifier.doi	10.1007/978-0-387-74161-1_23	en
dc.identifier.volume	247	en
dc.identifier.spage	213	en
dc.identifier.epage	220	en