A novel fuzzy clustering algorithm with between-cluster information for categorical data

Authors:
Liang Bai;Jiye Liang;Chuangyin Dang;Fuyuan Cao
Affiliations:
Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, 030006 Shanxi, ...;Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, 030006 Shanxi, ...;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong;Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, 030006 Shanxi, ...
Venue:
Fuzzy Sets and Systems
Year:
2013

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Abstract

In this paper, we present a new fuzzy clustering algorithm for categorical data. In the algorithm, the objective function of the fuzzy k-modes algorithm is modified by adding the between-cluster information so that we can simultaneously minimize the within-cluster dispersion and enhance the between-cluster separation. For obtaining the local optimal solutions of the modified objective function, the corresponding update formulas of the membership matrix and the cluster prototypes are strictly derived. The convergence of the proposed algorithm under the optimization framework is proved. On several real data sets from UCI, the performance of the proposed algorithm is studied. The experimental results illustrate that the algorithm is effective and suitable for categorical data sets.