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dc.contributor.authorKoh, YS
dc.contributor.authorPears, R
dc.contributor.editorKennedy, P
dc.contributor.editorOng, K-L
dc.contributor.editorChristen, P
dc.date.accessioned2013-02-26T04:31:19Z
dc.date.available2013-02-26T04:31:19Z
dc.date.copyright2009
dc.date.issued2013-02-26
dc.identifier.citation2009 Australasian Data Mining Conference , Melbourne, Australia, published in: Proceeding of the 2009 Australasian Data Mining Conference, vol.101, pp.69 - 74 (6)
dc.identifier.urihttp://hdl.handle.net/10292/5180
dc.description.abstractRare itemsets are likely to be of great interest because they often relate to high-impact transactions which may give rise to rules of great practical signi cance. Research into the rare association rule mining problem has gained momentum in the recent past. In this paper, we propose a novel approach that captures such rare rules while ensuring that redundant rules are eliminated. Extensive testing on real-world datasets from the UCI repository con rm that our approach outperforms both the Apriori-Inverse(Koh et al. 2006) and Relative Support (Yun et al. 2003) algorithms.
dc.publisherAustralian Computer Society (ACS)
dc.relation.urihttp://crpit.com/confpapers/CRPITV101Koh.pdf
dc.rightsCopyright © 2009, Australian Computer Society, Inc. This paper appeared at the Eighth Australasian Data Mining Conference (AusDM 2009), Melbourne, Australia. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 101, Paul J. Kennedy, Kok-Leong Ong and Peter Christen, Ed. Reproduction for academic, not-for pro t purposes permitted provided this text is included.
dc.subjectRare Association Rule Mining
dc.subjectApriori-Inverse
dc.subjectNon-Redundant Itemset
dc.titleNon-redundant rare itemset generation
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess
dc.identifier.roid14244en_NZ
aut.conference.typePaper Published in Proceedings
aut.relation.endpage74
aut.relation.pages6
aut.relation.startpage69
aut.relation.volume101
pubs.elements-id4878


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