A Family of Fuzzy and Defuzzified c-Means Algorithms

Authors:
Sadaaki Miyamoto;Takeshi Yasukochi;Ryo Inokuchi
Affiliations:
University of Tsukuba, Japan;University of Tsukuba, Japan;University of Tsukuba, Japan
Venue:
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
Year:
2005

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Postsupervised hard c-means classifier

RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing

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Abstract

This paper proposes a family of fuzzy and hard cmeans algorithms. The hard clustering algorithms are derived from defuzzifying a generalized entropy-based fuzzy c-means whereby cluster volume size variables and covariance variables are introduced into hard clustering algorithms. Sequential algorithms are also derived by using advanced formulas of matrix multiplication. Crisp c-means as well as c-regression models are studied. Moreover effectiveness and efficiency of the proposed algorithms are compared using artificial as well as real data sets.