HEAL DSpace

Using Information Retrieval techniques for supporting data mining

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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 http://hdl.handle.net/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


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