File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: An optimization algorithm for clustering using weighted dissimilarity measures

TitleAn optimization algorithm for clustering using weighted dissimilarity measures
Authors
Chan, EYChing, WKNg, MKHuang, JZ
KeywordsAttributes weights
Clustering
Data mining
Optimization
Issue Date2004
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pr
Citation
Pattern Recognition, 2004, v. 37 n. 5, p. 943-952 How to Cite?
DOI: http://dx.doi.org/10.1016/j.patcog.2003.11.003
AbstractOne of the main problems in cluster analysis is the weighting of attributes so as to discover structures that may be present. By using weighted dissimilarity measures for objects, a new approach is developed, which allows the use of the k-means-type paradigm to efficiently cluster large data sets. The optimization algorithm is presented and the effectiveness of the algorithm is demonstrated with both synthetic and real data sets. © 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/75220
ISSN
0031-3203
2021 Impact Factor: 8.518
2020 SCImago Journal Rankings: 1.492
ISI Accession Number ID
WOS:000220677200007
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorChan, EYen_HK
dc.contributor.authorChing, WKen_HK
dc.contributor.authorNg, MKen_HK
dc.contributor.authorHuang, JZen_HK
dc.date.accessioned2010-09-06T07:09:05Z-
dc.date.available2010-09-06T07:09:05Z-
dc.date.issued2004en_HK
dc.identifier.citationPattern Recognition, 2004, v. 37 n. 5, p. 943-952en_HK
dc.identifier.issn0031-3203en_HK
dc.identifier.urihttp://hdl.handle.net/10722/75220-
dc.description.abstractOne of the main problems in cluster analysis is the weighting of attributes so as to discover structures that may be present. By using weighted dissimilarity measures for objects, a new approach is developed, which allows the use of the k-means-type paradigm to efficiently cluster large data sets. The optimization algorithm is presented and the effectiveness of the algorithm is demonstrated with both synthetic and real data sets. © 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pren_HK
dc.relation.ispartofPattern Recognitionen_HK
dc.subjectAttributes weightsen_HK
dc.subjectClusteringen_HK
dc.subjectData miningen_HK
dc.subjectOptimizationen_HK
dc.titleAn optimization algorithm for clustering using weighted dissimilarity measuresen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0031-3203&volume=37&spage=943&epage=952&date=2004&atitle=An+Optimization+Algorithm+for+Clustering+Using+Weighted+Dissimilarity+Measuresen_HK
dc.identifier.emailChing, WK:wching@hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.patcog.2003.11.003en_HK
dc.identifier.scopuseid_2-s2.0-1842762839en_HK
dc.identifier.hkuros88731en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-1842762839&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume37en_HK
dc.identifier.issue5en_HK
dc.identifier.spage943en_HK
dc.identifier.epage952en_HK
dc.identifier.isiWOS:000220677200007-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridChan, EY=16038954500en_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridNg, MK=7202076432en_HK
dc.identifier.scopusauthoridHuang, JZ=36107803800en_HK
dc.identifier.issnl0031-3203-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats