Automatic discovery of locally frequent itemsets in the presence of highly frequent itemsets

Bodon, F; Kouris, I; Makris, C; Tsakalidis, A

dc.contributor.author	Bodon, F	en
dc.contributor.author	Kouris, I	en
dc.contributor.author	Makris, C	en
dc.contributor.author	Tsakalidis, A	en
dc.date.accessioned	2014-03-01T01:54:09Z
dc.date.available	2014-03-01T01:54:09Z
dc.date.issued	2005	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27214
dc.relation.uri	http://iospress.metapress.com/openurl.asp?genre=article&issn=1088-467X&volume=9&issue=1&spage=83	en
dc.relation.uri	http://www.informatik.uni-trier.de/~ley/db/journals/ida/ida9.html#BodonKMT05	en
dc.subject	Association Rule	en
dc.subject	Data Mining	en
dc.subject	frequent itemset	en
dc.title	Automatic discovery of locally frequent itemsets in the presence of highly frequent itemsets	en
heal.type	journalArticle	en
heal.publicationDate	2005	en
heal.abstract	Abstract. Many alternatives have been proposed,for the mining of association rules involving rare but ‘interesting’ itemsets in a dataset where there also exist highly frequent itemsets. Nevertheless, all the approaches thus far suggested that we knew which those interesting itemsets are, as well as which is the right support value for them. None of the approaches proposed a way,of automatically	en
heal.journalName	Intelligent Data Analysis	en