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Article: Iterative projected clustering by subspace mining

TitleIterative projected clustering by subspace mining
Authors
KeywordsAssociation rules
Classification
Clustering
Database applications
Database management
Issue Date2005
PublisherI E E E. The Journal's web site is located at http://www.computer.org/tkde
Citation
Ieee Transactions On Knowledge And Data Engineering, 2005, v. 17 n. 2, p. 176-189 How to Cite?
AbstractIrrolevant attributes add noise to high-dimensional clusters and render traditional clustering techniques inappropriate. Recently, several algorithms that discover projected clusters and their associated subspaces have been proposed. In this paper, we realize the analogy between mining frequent itemsets and discovering dense projected clusters around random points. Based on this, we propose a technique that improves the efficiency of a projected clustering algorithm (DOC). Our method is an optimized adaptation of the frequent pattern tree growth method used for mining frequent itemsets. We propose several techniques that employ the branch and bound paradigm to efficiently discover the projected clusters. An experimental study with synthetic and real data demonstrates that our technique significantly improves on the accuracy and speed of previous techniques. α 2005 IEEE Published by the IEEE Computer Society.
Persistent Identifierhttp://hdl.handle.net/10722/43626
ISSN
2015 Impact Factor: 2.476
2015 SCImago Journal Rankings: 2.087
References

 

DC FieldValueLanguage
dc.contributor.authorYiu, MLen_HK
dc.contributor.authorMamoulis, Nen_HK
dc.date.accessioned2007-03-23T04:50:46Z-
dc.date.available2007-03-23T04:50:46Z-
dc.date.issued2005en_HK
dc.identifier.citationIeee Transactions On Knowledge And Data Engineering, 2005, v. 17 n. 2, p. 176-189en_HK
dc.identifier.issn1041-4347en_HK
dc.identifier.urihttp://hdl.handle.net/10722/43626-
dc.description.abstractIrrolevant attributes add noise to high-dimensional clusters and render traditional clustering techniques inappropriate. Recently, several algorithms that discover projected clusters and their associated subspaces have been proposed. In this paper, we realize the analogy between mining frequent itemsets and discovering dense projected clusters around random points. Based on this, we propose a technique that improves the efficiency of a projected clustering algorithm (DOC). Our method is an optimized adaptation of the frequent pattern tree growth method used for mining frequent itemsets. We propose several techniques that employ the branch and bound paradigm to efficiently discover the projected clusters. An experimental study with synthetic and real data demonstrates that our technique significantly improves on the accuracy and speed of previous techniques. α 2005 IEEE Published by the IEEE Computer Society.en_HK
dc.format.extent1373849 bytes-
dc.format.extent26624 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/msword-
dc.languageengen_HK
dc.publisherI E E E. The Journal's web site is located at http://www.computer.org/tkdeen_HK
dc.relation.ispartofIEEE Transactions on Knowledge and Data Engineeringen_HK
dc.rights©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectAssociation rulesen_HK
dc.subjectClassificationen_HK
dc.subjectClusteringen_HK
dc.subjectDatabase applicationsen_HK
dc.subjectDatabase managementen_HK
dc.titleIterative projected clustering by subspace miningen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1041-4347&volume=17&issue=2&spage=176&epage=189&date=2005&atitle=Iterative+projected+clustering+by+subspace+miningen_HK
dc.identifier.emailMamoulis, N:nikos@cs.hku.hken_HK
dc.identifier.authorityMamoulis, N=rp00155en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/TKDE.2005.29en_HK
dc.identifier.scopuseid_2-s2.0-14644404956en_HK
dc.identifier.hkuros103323-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-14644404956&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume17en_HK
dc.identifier.issue2en_HK
dc.identifier.spage176en_HK
dc.identifier.epage189en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridYiu, ML=8589889600en_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK
dc.identifier.citeulike3180441-

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