Several formulations for graded possibilistic approach to fuzzy clustering

Authors:
Katsuhiro Honda;Hidetomo Ichihashi;Akira Notsu;Francesco Masulli;Stefano Rovetta
Affiliations:
Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan;Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan;Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan;Department of Computer and Information Sciences, University of Genova and CNISM, Genova, Italy;Department of Computer and Information Sciences, University of Genova and CNISM, Genova, Italy
Venue:
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Year:
2006

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Abstract

Fuzzy clustering is a useful tool for capturing intrinsic structure of data sets. This paper proposes several formulations for soft transition of fuzzy memberships from probabilistic partition to possibilistic one. In the proposed techniques, the free memberships are given by introducing additional penalty term used in Possibilistic c-Means. The new features of the proposed techniques are demonstrated in several numerical experiments.