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Clustering algorithms are classified into two categories hard clustering algorithms and fuzzy clustering algorithms. A hard clustering algorithm allocates each pattern to a single cluster during its operation and in its output. A fuzzy clustering method assigns degrees of membership in several clusters to each input pattern. In this paper, a hybrid fuzzy particle swarm optimization (FPSO) and fuzzy k-modes (FK-Modes) algorithm for clustering categorical data is proposed. It integrates concepts of FK-Modes algorithm to handle the uncertainty phenomena and FPSO to reach global optimal solution of clustering optimization problem. The proposed FPSO-FK-Modes algorithm is implemented and evaluated using slandered benchmark data sets and performance measures. Experimental results showed that the proposed FPSO-FK-Modes algorithm performed well compared with FK-modes and Genetic FK-modes (GA-FK-modes) algorithm using adjusted rand index.
Date of Conference: 14-16 May 2012