Differential fuzzy clustering for categorical data

Differential evolution has emerged as one of the fast, robust, and efficient global search heuristics of current interest. Besides its good convergence properties and suitability for parallelization, Differential evolution's main assets are its conceptual simplicity and ease of use. This paper describes an application of differential evolution to the fuzzy clustering for categorical data sets. The performance of the proposed method has been compared with the simulated annealing based fuzzy c-medoids clustering algorithm, fuzzy c-medoids, fuzzy c-modes and average linkage hierarchical clustering algorithm for two artificial and two real life categorical data sets. Statistical significance test has been carried out to establish the statistical significance of the proposed method.

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