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Conference Paper: Mining circumstance-oriented association rules using singular value decomposition technique

TitleMining circumstance-oriented association rules using singular value decomposition technique
Authors
KeywordsData Mining
Information Analysis
Issue Date2004
Citation
Conference Proceedings - Ieee International Conference On Systems, Man And Cybernetics, 2004, v. 4, p. 3169-3174 How to Cite?
AbstractAssociation rule has evolved from the primitive form of single dimension intratransaction to the form of multi-dimension intertransaction. The challenge for mining multi-dimension intertransaction rules is the formidable search space. Researchers have proposed various methods to handle this problem, such as restricting the number of dimensions, confining search space in a small window, etc. These methods unavoidably have negative impact on mining result and they are less effective when the number of dimensions and the length of rule are really large. Moreover, all these methods are derived from the Apriori algorithm and have common weaknesses: time consuming and redundancy caused by the iterative nature of the Apriori algorithm. To approach this problem from a different angle, we propose to use the singular value decomposition technique(SVD). With SVD, the multi-dimension intertransaction rules can be easily identified. © 2004 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/151851
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorChen, Yen_US
dc.contributor.authorChan, KPen_US
dc.date.accessioned2012-06-26T06:30:05Z-
dc.date.available2012-06-26T06:30:05Z-
dc.date.issued2004en_US
dc.identifier.citationConference Proceedings - Ieee International Conference On Systems, Man And Cybernetics, 2004, v. 4, p. 3169-3174en_US
dc.identifier.issn1062-922Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/151851-
dc.description.abstractAssociation rule has evolved from the primitive form of single dimension intratransaction to the form of multi-dimension intertransaction. The challenge for mining multi-dimension intertransaction rules is the formidable search space. Researchers have proposed various methods to handle this problem, such as restricting the number of dimensions, confining search space in a small window, etc. These methods unavoidably have negative impact on mining result and they are less effective when the number of dimensions and the length of rule are really large. Moreover, all these methods are derived from the Apriori algorithm and have common weaknesses: time consuming and redundancy caused by the iterative nature of the Apriori algorithm. To approach this problem from a different angle, we propose to use the singular value decomposition technique(SVD). With SVD, the multi-dimension intertransaction rules can be easily identified. © 2004 IEEE.en_US
dc.languageengen_US
dc.relation.ispartofConference Proceedings - IEEE International Conference on Systems, Man and Cyberneticsen_US
dc.subjectData Miningen_US
dc.subjectInformation Analysisen_US
dc.titleMining circumstance-oriented association rules using singular value decomposition techniqueen_US
dc.typeConference_Paperen_US
dc.identifier.emailChan, KP:kpchan@cs.hku.hken_US
dc.identifier.authorityChan, KP=rp00092en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-15744389599en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-15744389599&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume4en_US
dc.identifier.spage3169en_US
dc.identifier.epage3174en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridChen, Y=7601437873en_US
dc.identifier.scopusauthoridChan, KP=7406032820en_US

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