Using Information Retrieval techniques for supporting data mining

Kouris, I; Makris, C; Tsakalidis, A

dc.contributor.author	Kouris, I	en
dc.contributor.author	Makris, C	en
dc.contributor.author	Tsakalidis, A	en
dc.date.accessioned	2014-03-01T01:54:22Z
dc.date.available	2014-03-01T01:54:22Z
dc.date.issued	2005	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27354
dc.subject	Association Rule	en
dc.subject	Data Mining	en
dc.subject	Data Type	en
dc.subject	E Commerce	en
dc.subject	Indexation	en
dc.subject	Information Retrieval	en
dc.subject	Knowledge Discovery	en
dc.subject	Search Engine	en
dc.subject	Search Space	en
dc.title	Using Information Retrieval techniques for supporting data mining	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.datak.2004.07.004	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.datak.2004.07.004	en
heal.publicationDate	2005	en
heal.abstract	The classic two-stepped approach of the Apriori algorithm and its descendants, which consisted of finding all large itemsets and then using these itemsets to generate all association rules has worked well for certain categories of data. Nevertheless for many other data types this approach shows highly degraded performance and proves rather inefficient.We argue that we need to search all the	en
heal.journalName	Data & Knowledge Engineering	en
dc.identifier.doi	10.1016/j.datak.2004.07.004	en