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Conference Paper: A decremental approach for mining frequent itemsets from uncertain data

TitleA decremental approach for mining frequent itemsets from uncertain data
Authors
Issue Date2008
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), 2008, v. 5012 LNAI, p. 64-75 How to Cite?
AbstractWe study the problem of mining frequent itemsets from uncertain data under a probabilistic model. We consider transactions whose items are associated with existential probabilities. A decremental pruning (DP) technique, which exploits the statistical properties of items' existential probabilities, is proposed. Experimental results show that DP can achieve significant computational cost savings compared with existing approaches, such as U-Apriori and LGS-Trimming. Also, unlike LGS-Trimming, DP does not require a user-specified trimming threshold and its performance is relatively insensitive to the population of low-probability items in the dataset. © 2008 Springer-Verlag Berlin Heidelberg.
Persistent Identifierhttp://hdl.handle.net/10722/93214
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.date.accessioned2010-09-25T14:54:21Z-
dc.date.available2010-09-25T14:54:21Z-
dc.date.issued2008en_HK
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2008, v. 5012 LNAI, p. 64-75en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93214-
dc.description.abstractWe study the problem of mining frequent itemsets from uncertain data under a probabilistic model. We consider transactions whose items are associated with existential probabilities. A decremental pruning (DP) technique, which exploits the statistical properties of items' existential probabilities, is proposed. Experimental results show that DP can achieve significant computational cost savings compared with existing approaches, such as U-Apriori and LGS-Trimming. Also, unlike LGS-Trimming, DP does not require a user-specified trimming threshold and its performance is relatively insensitive to the population of low-probability items in the dataset. © 2008 Springer-Verlag Berlin Heidelberg.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.titleA decremental approach for mining 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.doi10.1007/978-3-540-68125-0_8en_HK
dc.identifier.scopuseid_2-s2.0-44649091207en_HK
dc.identifier.hkuros141136en_HK
dc.identifier.hkuros200233-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-44649091207&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume5012 LNAIen_HK
dc.identifier.spage64en_HK
dc.identifier.epage75en_HK
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridChui, CK=21741958100en_HK
dc.identifier.scopusauthoridKao, B=35221592600en_HK

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