Evolutionary search for optimal fuzzy C-means clustering

Eduarde R. Hruschka*, Ricardo J.G.B. Campello, Leandro N. De Castro

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Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Abstract

This paper introduces an evolutionary approach to automatically determine the optimal number and location of prototypes for the well-known Fuzzy C-Means (FCM) clustering algorithm. This approach is based on a Clustering Genetic Algorithm (CGA) specially designed for clustering tasks. It uses context-sensitive genetic operators to globally explore the search space in such a way that the strong dependence of the FCM algorithm on adequate previous estimations of the number and initial positions of its cluster prototypes is avoided. In this case, FCM works as a local search engine to speed up convergence and improve accuracy of the overall evolutionary procedure. Two examples are presented to illustrate that the proposed algorithm is able to automatically find adequate clusterings either starting from underestimated or overestimated initial number of clusters.

OriginalsprogEngelsk
Titel2004 IEEE International Conference on Fuzzy Systems - Proceedings
ForlagIEEE
Publikationsdato2004
Sider685-690
ISBN (Trykt)0780383532
DOI
StatusUdgivet - 2004
Udgivet eksterntJa
Begivenhed2004 IEEE International Conference on Fuzzy Systems - Proceedings - Budapest, Ungarn
Varighed: 25. jul. 200429. jul. 2004

Konference

Konference2004 IEEE International Conference on Fuzzy Systems - Proceedings
Land/OmrådeUngarn
ByBudapest
Periode25/07/200429/07/2004
NavnIEEE International Conference on Fuzzy Systems
Vol/bind2
ISSN1098-7584

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