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Article: On the impact of dissimilarity measure in κ-modes clustering algorithm

TitleOn the impact of dissimilarity measure in κ-modes clustering algorithm
Authors
Ng, Michael K.Li, Mark JunjieHuang, Joshua ZhexueHe, Zengyou
Keywordsκ-modes algorithm
Data mining
Clustering
Categorical data
Issue Date2007
Citation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, v. 29, n. 3, p. 503-507 How to Cite?
DOI: http://dx.doi.org/10.1109/TPAMI.2007.53
AbstractThis correspondence describes extensions to the κ-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical objects, a heuristic approach was developed in [4], [12] which allows the use of the k-modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k-modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework. © 2007 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/276799
ISSN
0162-8828
2021 Impact Factor: 24.314
2020 SCImago Journal Rankings: 3.811
ISI Accession Number ID
WOS:000243420500012

 

DC FieldValueLanguage
dc.contributor.authorNg, Michael K.-
dc.contributor.authorLi, Mark Junjie-
dc.contributor.authorHuang, Joshua Zhexue-
dc.contributor.authorHe, Zengyou-
dc.date.accessioned2019-09-18T08:34:42Z-
dc.date.available2019-09-18T08:34:42Z-
dc.date.issued2007-
dc.identifier.citationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, v. 29, n. 3, p. 503-507-
dc.identifier.issn0162-8828-
dc.identifier.urihttp://hdl.handle.net/10722/276799-
dc.description.abstractThis correspondence describes extensions to the κ-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical objects, a heuristic approach was developed in [4], [12] which allows the use of the k-modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k-modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework. © 2007 IEEE.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligence-
dc.subjectκ-modes algorithm-
dc.subjectData mining-
dc.subjectClustering-
dc.subjectCategorical data-
dc.titleOn the impact of dissimilarity measure in κ-modes clustering algorithm-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TPAMI.2007.53-
dc.identifier.pmid17224620-
dc.identifier.scopuseid_2-s2.0-33847349252-
dc.identifier.volume29-
dc.identifier.issue3-
dc.identifier.spage503-
dc.identifier.epage507-
dc.identifier.isiWOS:000243420500012-
dc.identifier.issnl0162-8828-

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