A Family of Fuzzy and Defuzzified c-Means Algorithms

Author

Miyamoto, Sadaaki ; Yasukochi, Takeshi ; Inokuchi, Ryo

Author_Institution

Dept. of Risk Eng., Tsukuba Univ., Ibaraki

Volume

2

fYear

2005

fDate

28-30 Nov. 2005

Firstpage

170

Lastpage

176

Abstract

This paper proposes a family of fuzzy and hard c-means 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

Keywords

entropy; fuzzy set theory; pattern clustering; regression analysis; c-regression model; defuzzified c-means algorithms; fuzzy c-means algorithms; generalized entropy-based fuzzy c-means; hard c-means algorithm; hard clustering algorithm; matrix multiplication; sequential algorithm; Automation; Business; Clustering algorithms; Computational intelligence; Computational modeling; Covariance matrix; Intelligent agent; Internet; Minimization methods; Virtual colonoscopy;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on

Conference_Location

Vienna

Print_ISBN

0-7695-2504-0

Type

conf

DOI

10.1109/CIMCA.2005.1631463

Filename

1631463