A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
International Journal of Hybrid Intelligent Systems
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Most of the algorithms designed for categorical data clustering optimize a single measure of the clustering good- ness. Such a single measure may not be appropriate for different kinds of data sets. Therefore, consideration of multiple, often conflicting, objectives appears to be natu- ral for this problem. In this article a multiobjective genetic algorithm based approach for fuzzy clustering of categor- ical data is proposed. The performance of the proposed technique has been compared with that of the other well known categorical data clustering algorithms. For this pur- pose, various synthetic and real life categorical data sets have been considered. Statistical significance test has been conducted to establish the significant superiority of the pro- posed multiobjective approach.