A genetic fuzzy kk-Modes algorithm for clustering categorical data

The fuzzy kk-Modes algorithm introduced by Huang and Ng [Huang, Z., & Ng, M. (1999). A fuzzy kk-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 kk-Modes algorithm in this paper. To circumvent the expensive crossover operator in genetic algorithms (GAs), we hybridize GA with the fuzzy kk-Modes algorithm and define the crossover operator as a one-step fuzzy kk-Modes algorithm. Experiments on two real data sets are carried out to illustrate the performance of the proposed algorithm.