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Article: Automated Variable Weighting in k-Means Type Clustering

TitleAutomated Variable Weighting in k-Means Type Clustering
Authors
Issue Date2005
PublisherIEEE. The Journal's web site is located at http://www.computer.org/tpami
Citation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, v. 27 n. 5, p. 657-668 How to Cite?
AbstractThis paper proposes a k-means type clustering algorithm that can automatically calculate variable weights. A new step is introduced to the k-means clustering process to iteratively update variable weights based on the current partition of data and a formula for weight calculation is proposed. The convergency theorem of the new clustering process is given. The variable weights produced by the algorithm measure the importance of variables in clustering and can be used in variable selection in data mining applications where large and complex real data are often involved. Experimental results on both synthetic and real data have shown that the new algorithm outperformed the standard k-means type algorithms in recovering clusters in data.
Persistent Identifierhttp://hdl.handle.net/10722/222807
ISSN
2015 Impact Factor: 6.077
2015 SCImago Journal Rankings: 7.653

 

DC FieldValueLanguage
dc.contributor.authorHuang, JZ-
dc.contributor.authorNg, MK-
dc.contributor.authorRong, H-
dc.contributor.authorLi, Z-
dc.date.accessioned2016-02-01T04:06:12Z-
dc.date.available2016-02-01T04:06:12Z-
dc.date.issued2005-
dc.identifier.citationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, v. 27 n. 5, p. 657-668-
dc.identifier.issn0162-8828-
dc.identifier.urihttp://hdl.handle.net/10722/222807-
dc.description.abstractThis paper proposes a k-means type clustering algorithm that can automatically calculate variable weights. A new step is introduced to the k-means clustering process to iteratively update variable weights based on the current partition of data and a formula for weight calculation is proposed. The convergency theorem of the new clustering process is given. The variable weights produced by the algorithm measure the importance of variables in clustering and can be used in variable selection in data mining applications where large and complex real data are often involved. Experimental results on both synthetic and real data have shown that the new algorithm outperformed the standard k-means type algorithms in recovering clusters in data.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://www.computer.org/tpami-
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligence-
dc.rightsIEEE Transactions on Pattern Analysis and Machine Intelligence. Copyright © IEEE.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleAutomated Variable Weighting in k-Means Type Clustering-
dc.typeArticle-
dc.identifier.emailHuang, JZ: jhuang@eti.hku.hk-
dc.identifier.emailNg, MK: mng@maths.hku.hk-
dc.identifier.emailRong, H: hqrong@cs.hku.hk-
dc.identifier.volume27-
dc.identifier.issue5-
dc.identifier.spage657-
dc.identifier.epage668-
dc.publisher.placeUnited States-

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