DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, DW | ko |
dc.contributor.author | Lee, KY | ko |
dc.contributor.author | Lee, Doheon | ko |
dc.contributor.author | Lee, Kwang-Hyung | ko |
dc.date.accessioned | 2010-06-10T01:43:34Z | - |
dc.date.available | 2010-06-10T01:43:34Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2005-04 | - |
dc.identifier.citation | PATTERN RECOGNITION, v.38, pp.607 - 611 | - |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.uri | http://hdl.handle.net/10203/18799 | - |
dc.description.abstract | By using a kernel function, data that are not easily separable in the original space can be clustered into homogeneous groups in the implicitly transformed high-dimensional feature space. Kernel k-means algorithms have recently been shown to perform better than conventional k-means algorithms in unsupervised classification. However. few reports have examined the benefits of using a kernel function and the relative merits of the various kernel clustering algorithms with regard to the data distribution. In this study, we reformulated four representative clustering algorithms based on a kernel function and evaluated their performances for various data sets. The results indicate that each kernel clustering algorithm gives markedly better performance than its conventional counterpart for almost all data sets. Of the kernel clustering algorithms studied in the present work, the kernel average linkage algorithm gives the most accurate clustering results. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. | - |
dc.description.sponsorship | This work was supported by the Korean Systems Biology Research Grant (M1-0309-02-0002) from the Ministry of Science and Technology. We would like to thank Chung Moon Soul Center for BioInformation and BioElectronics and the IBM SUR program for providing research and computing facilities. | en |
dc.language | English | - |
dc.language.iso | en_US | en |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.title | Evaluation of the performance of clustering algorithms in kernel-induced feature space | - |
dc.type | Article | - |
dc.identifier.wosid | 000226434500015 | - |
dc.identifier.scopusid | 2-s2.0-10644261327 | - |
dc.type.rims | ART | - |
dc.citation.volume | 38 | - |
dc.citation.beginningpage | 607 | - |
dc.citation.endingpage | 611 | - |
dc.citation.publicationname | PATTERN RECOGNITION | - |
dc.identifier.doi | 10.1016/j.patcog.2004.09.006 | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Lee, Doheon | - |
dc.contributor.localauthor | Lee, Kwang-Hyung | - |
dc.contributor.nonIdAuthor | Kim, DW | - |
dc.contributor.nonIdAuthor | Lee, KY | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | clustering | - |
dc.subject.keywordAuthor | kernel | - |
dc.subject.keywordAuthor | k-rneans | - |
dc.subject.keywordAuthor | Fuzzy c-means | - |
dc.subject.keywordAuthor | average linkage | - |
dc.subject.keywordAuthor | mountain algorithm | - |
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