A genetic fuzzy k-Modes algorithm for clustering categorical data

The fuzzy k-Modes algorithm introduced by Huang and Ng [Huang, Z., & Ng, M. (1999). A fuzzy k-modes algorithm for clustering categorical data. IEEE Transactions on Fuzzy Systems, 7(4), 446–452] is very effective for identifying cluster structures from categorical data sets. However, the algorith...

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Bibliographic Details
Published inExpert systems with applications Vol. 36; no. 2; pp. 1615 - 1620
Main Authors Gan, G., Wu, J., Yang, Z.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.03.2009
Subjects
[formula omitted]-Modes
Categorical data
Fuzzy logic
Genetic algorithm
Fuzzy logic
Categorical data
k-Modes
Genetic algorithm
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ISSN0957-4174
1873-6793
DOI10.1016/j.eswa.2007.11.045

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Summary:The fuzzy k-Modes algorithm introduced by Huang and Ng [Huang, Z., & Ng, M. (1999). A fuzzy k-modes algorithm for clustering categorical data. IEEE Transactions on Fuzzy Systems, 7(4), 446–452] is very effective for identifying cluster structures from categorical data sets. However, the algorithm may stop at locally optimal solutions. In order to search for appropriate fuzzy membership matrices which can minimize the fuzzy objective function, we present a hybrid genetic fuzzy k-Modes algorithm in this paper. To circumvent the expensive crossover operator in genetic algorithms (GAs), we hybridize GA with the fuzzy k-Modes algorithm and define the crossover operator as a one-step fuzzy k-Modes algorithm. Experiments on two real data sets are carried out to illustrate the performance of the proposed algorithm.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2007.11.045