Integrating Fuzzy K-Means, Particle Swarm Optimization, and Imperialist Competitive Algorithm for Data Clustering

Abstract

In this paper, we proposed two hybrid data clustering algorithms that are called ICAFKM and PSOFKM. ICAFKM combined the advantageous aspects of Fuzzy K-Means (FKM) and Imperialist Competitive Algorithm (ICA), and PSOFKM makes full use of the merits of both Particle Swarm Optimization (PSO) and FKM algorithms. FKM is one of the most popular data clustering methods. However, this algorithm solves the problem of sensitivity to initial states of K-Means (KM) algorithm, but like KM, it often converges to local optima. The proposed ICAFKM and PSOFKM algorithms aim to help the FKM to escape from local optima and increase the convergence speed of the ICA and PSO algorithms in clustering process. In order to evaluate the performance of ICAFKM and PSOFKM methods, we evaluate these algorithms on five datasets and compared them with FKM, ICA, PSO, PSOKHM, and HABC algorithms. The experimental results indicate that the ICAFKM carries out better results than the other methods.