Clustering Algorithm Combining CPSO with K-Means

A clustering algorithm combining particle swarm optimization (CPSO) with K-Means (KM-CPSO) is proposed, which features better search efficiency than K-Means, PSO and CPSO. The K-Means algorithms cannot guarantee convergence to global optima and suffer in local optimal cluster center because they are sensitive to initial cluster centers. Chaotic particle swarm optimization (CPSO) can find global optimal solution; meanwhile K-Means can achieve local optima. The CPSO-KM algorithm utilizes both global search capability of CPSO and local search capability of K-Means. CPSO-KM algorithm has been tested with two synthetic datasets and three classical data sets from UCI. Experimental results show better performance of the CPSO-KM as compared to K-Means, PSO and CPSO.