Computing frequent itemsets in parallel using partial support trees

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dc.contributor.author Souliou, D en
dc.contributor.author Pagourtzis, A en
dc.contributor.author Drosinos, N en
dc.date.accessioned 2014-03-01T02:43:11Z
dc.date.available 2014-03-01T02:43:11Z
dc.date.issued 2005 en
dc.identifier.issn 0302-9743 en
dc.identifier.uri http://hdl.handle.net/123456789/31276
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.subject.classification Computer Science, Theory & Methods en
dc.subject.other Algorithms en
dc.subject.other Data mining en
dc.subject.other Database systems en
dc.subject.other Set theory en
dc.subject.other Trees (mathematics) en
dc.subject.other Association rules en
dc.subject.other Frequent itemsets en
dc.subject.other Parallel data mining en
dc.subject.other Partial support tree en
dc.subject.other Parallel processing systems en
dc.title Computing frequent itemsets in parallel using partial support trees en
heal.type conferenceItem en
heal.identifier.primary 10.1007/11557265_9 en
heal.identifier.secondary http://dx.doi.org/10.1007/11557265_9 en
heal.language English en
heal.publicationDate 2005 en
heal.abstract A key process in association rules mining, which has attracted a lot of interest during the last decade, is the discovery of frequent sets of items in a database of transactions. A number of sequential algorithms have been proposed that accomplish this task. In this paper we study the parallelization of the partial-support-tree approach (Goulbourne, Coenen, Leng, 2000). Results show that this method achieves a generally satisfactory speedup, while it is particularly adequate for certain types of datasets. © Springer-Verlag Berlin Heidelberg 2005. en
heal.publisher SPRINGER-VERLAG BERLIN en
heal.journalName Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) en
dc.identifier.doi 10.1007/11557265_9 en
dc.identifier.isi ISI:000233236700009 en
dc.identifier.volume 3666 LNCS en
dc.identifier.spage 28 en
dc.identifier.epage 37 en

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