A kernel-based subtractive clustering method

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dc.contributor.authorKim, DWko
dc.contributor.authorLee, Kko
dc.contributor.authorLee, Doheonko
dc.contributor.authorLee, Kwang-Hyungko
dc.date.accessioned2013-03-08T02:26:14Z-
dc.date.available2013-03-08T02:26:14Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2005-05-
dc.identifier.citationPATTERN RECOGNITION LETTERS, v.26, pp.879 - 891-
dc.identifier.issn0167-8655-
dc.identifier.urihttp://hdl.handle.net/10203/91837-
dc.description.abstractIn this paper the conventional subtractive clustering method is extended by calculating the mountain value of each data point based on a kernel-induced distance instead of the conventional sum-of-squares distance. The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space. Use of the kernel function makes it possible to cluster data that is linearly non-separable in the original space into homogeneous groups in the transformed high dimensional space. Application of the conventional subtractive method and the kernel-based subtractive method to well-known data sets showed the superiority of the proposed approach. (c) 2004 Elsevier B.V. All rights reserved.-
dc.languageEnglish-
dc.publisherELSEVIER SCIENCE BV-
dc.subjectMOUNTAIN METHOD-
dc.subjectALGORITHM-
dc.subjectVALIDITY-
dc.titleA kernel-based subtractive clustering method-
dc.typeArticle-
dc.identifier.wosid000228940700003-
dc.identifier.scopusid2-s2.0-17444369435-
dc.type.rimsART-
dc.citation.volume26-
dc.citation.beginningpage879-
dc.citation.endingpage891-
dc.citation.publicationnamePATTERN RECOGNITION LETTERS-
dc.identifier.doi10.1016/j.patrec.2004.10.001-
dc.contributor.localauthorLee, Doheon-
dc.contributor.localauthorLee, Kwang-Hyung-
dc.contributor.nonIdAuthorKim, DW-
dc.contributor.nonIdAuthorLee, K-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorclustering-
dc.subject.keywordAuthormountain method-
dc.subject.keywordAuthorsubtractive method-
dc.subject.keywordAuthorkernel function-
dc.subject.keywordPlusMOUNTAIN METHOD-
dc.subject.keywordPlusALGORITHM-
dc.subject.keywordPlusVALIDITY-
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