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Conference Paper: Mining frequent itemsets from uncertain data

TitleMining frequent itemsets from uncertain data
Authors
Issue Date2007
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2007, v. 4426 LNAI, p. 47-58 How to Cite?
AbstractWe study the problem of mining frequent itemsets from uncertain data under a probabilistic framework. We consider transactions whose items are associated with existential probabilities and give a formal definition of frequent patterns under such an uncertain data model. We show that traditional algorithms for mining frequent itemsets are either inapplicable or computationally inefficient under such a model. A data trimming framework is proposed to improve mining efficiency. Through extensive experiments, we show that the data trimming technique can achieve significant savings in both CPU cost and I/O cost. © Springer-Verlag Berlin Heidelberg 2007.
Persistent Identifierhttp://hdl.handle.net/10722/93484
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorChui, CKen_HK
dc.contributor.authorKao, Ben_HK
dc.contributor.authorHung, Een_HK
dc.date.accessioned2010-09-25T15:02:33Z-
dc.date.available2010-09-25T15:02:33Z-
dc.date.issued2007en_HK
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2007, v. 4426 LNAI, p. 47-58en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93484-
dc.description.abstractWe study the problem of mining frequent itemsets from uncertain data under a probabilistic framework. We consider transactions whose items are associated with existential probabilities and give a formal definition of frequent patterns under such an uncertain data model. We show that traditional algorithms for mining frequent itemsets are either inapplicable or computationally inefficient under such a model. A data trimming framework is proposed to improve mining efficiency. Through extensive experiments, we show that the data trimming technique can achieve significant savings in both CPU cost and I/O cost. © Springer-Verlag Berlin Heidelberg 2007.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_HK
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_HK
dc.titleMining frequent itemsets from uncertain dataen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailKao, B:kao@cs.hku.hken_HK
dc.identifier.authorityKao, B=rp00123en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-38049177468en_HK
dc.identifier.hkuros137096en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-38049177468&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4426 LNAIen_HK
dc.identifier.spage47en_HK
dc.identifier.epage58en_HK
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridChui, CK=21741958100en_HK
dc.identifier.scopusauthoridKao, B=35221592600en_HK
dc.identifier.scopusauthoridHung, E=7004256336en_HK

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