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Conference Paper: Frequent-pattern based iterative projected clustering

TitleFrequent-pattern based iterative projected clustering
Authors
Yiu, MLMamoulis, N
Issue Date2003
PublisherIEEE, Computer Society.
Citation
3rd IEEE International Conference on Data Mining (ICDM '03), Melbourne, FL, 19-22 November 2003. In Third IEEE International Conference on Data Mining, 2003, p. 689-692 How to Cite?
DOI: http://dx.doi.org/10.1109/ICDM.2003.1251009
AbstractIrrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Projected clustering algorithms have been proposed to find the clusters in hidden subspaces. We realize the analogy between mining frequent itemsets and discovering the relevant subspace for a given cluster. We propose a methodology for finding projected clusters by mining frequent itemsets and present heuristics that improve its quality. Our techniques are evaluated with synthetic and real data; they are scalable and discover projected clusters accurately. © 2003 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/45532
ISSN
1550-4786
2020 SCImago Journal Rankings: 0.545
References
References in Scopus

 

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dc.contributor.authorYiu, MLen_HK
dc.contributor.authorMamoulis, Nen_HK
dc.date.accessioned2007-10-30T06:28:36Z-
dc.date.available2007-10-30T06:28:36Z-
dc.date.issued2003en_HK
dc.identifier.citation3rd IEEE International Conference on Data Mining (ICDM '03), Melbourne, FL, 19-22 November 2003. In Third IEEE International Conference on Data Mining, 2003, p. 689-692en_HK
dc.identifier.issn1550-4786en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45532-
dc.description.abstractIrrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Projected clustering algorithms have been proposed to find the clusters in hidden subspaces. We realize the analogy between mining frequent itemsets and discovering the relevant subspace for a given cluster. We propose a methodology for finding projected clusters by mining frequent itemsets and present heuristics that improve its quality. Our techniques are evaluated with synthetic and real data; they are scalable and discover projected clusters accurately. © 2003 IEEE.en_HK
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dc.languageengen_HK
dc.publisherIEEE, Computer Society.en_HK
dc.relation.ispartofThird IEEE International Conference on Data Miningen_HK
dc.rights©2003 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.-
dc.titleFrequent-pattern based iterative projected clusteringen_HK
dc.typeConference_Paperen_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/ICDM.2003.1251009-
dc.identifier.scopuseid_2-s2.0-20844440247en_HK
dc.identifier.hkuros103382-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-20844440247&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage689en_HK
dc.identifier.epage692en_HK
dc.identifier.scopusauthoridYiu, ML=8589889600en_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK
dc.identifier.issnl1550-4786-

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