A novel chaotic particle swarm optimization based fuzzy clustering algorithm

Clustering is a popular data analysis and data mining technique. In this paper, a novel chaotic particle swarm fuzzy clustering (CPSFC) algorithm based on chaotic particle swarm (CPSO) and gradient method is proposed. Fuzzy clustering model optimization is challenging, in order to solve this problem, adaptive inertia weight factor (AIWF) and iterative chaotic map with infinite collapses (ICMIC) are introduced, and a new CPSO algorithm combined AIWF and ICMIC based chaotic local search is studied. The CPSFC algorithm utilizes CPSO to search the fuzzy clustering model, exploiting the searching capability of fuzzy c-means (FCM) and avoiding its major limitation of getting stuck at locally optimal values. Meanwhile, gradient operator is adopted to accelerate convergence of the proposed algorithm. Its superiority over the FCM algorithm and another two global optimization algorithm-based clustering methods is extensively demonstrated for several artificial and real life data sets in comparative experiments.