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dc.contributor.authorRavi, V.
dc.contributor.authorSrinivas, E.
dc.contributor.authorKasabov, N
dc.date.accessioned2009-05-27T22:18:50Z
dc.date.available2009-05-27T22:18:50Z
dc.date.copyright2007
dc.date.created2007
dc.date.issued2009-05-27T22:18:50Z
dc.identifier.urihttp://hdl.handle.net/10292/598
dc.description.abstractIn this paper, a novel on-line evolving fuzzy clustering method that extends the evolving clustering method (ECM) of Kasabov and Song (2002) is presented, called EFCM. Since it is an on-line algorithm, the fuzzy membership matrix of the data is updated whenever the existing cluster expands, or a new cluster is formed. EFCM does not need the numbers of the clusters to be pre-defined. The algorithm is tested on several benchmark data sets, such as Iris, Wine, Glass, E-Coli, Yeast and Italian Olive oils. EFCM results in the least objective function value compared to the ECM and Fuzzy C-Means. It is significantly faster (by several orders of magnitude) than any of the off-line batch-mode clustering algorithms. A methodology is also proposed for using theXie-Beni cluster validity measure to optimize the number of clusters. © 2007 IEEE.
dc.publisherIEEE
dc.rights©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.sourceInternational Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2007, 1, 347-351
dc.titleOn-line evolving fuzzy clustering
dc.typeConference Proceedings
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1109/ICCIMA.2007.111


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