A New Cooperative Algorithm Based on PSO and K-Means for Data Clustering

摘要

Data clustering had been applied in multiple fields such as machine learning, data mining, wireless sensor networks and pattern recognition. One of the most famous clustering approaches is K-means which effectively has been used in many clustering problems, but this algorithm has some drawbacks such as local optimal convergence and sensitivity to initial points. Approach: Particle Swarm Optimization (PSO) algorithm is one of the swarm intelligence algorithms, which is applied in determining the optimal cluster centers. A cooperative algorithm based on PSO and k-means was presented. Result: The proposed algorithm utilizes both global search ability of PSO and local search ability of k-means. The proposed algorithm and also PSO, PSO with Contraction Factor (CF-PSO), k-means algorithms and KPSO hybrid algorithm had been used for clustering six datasets and their efficiencies were compared with each other. Conclusion: Experimental results show that the proposed algorithm has an acceptable efficiency and robustness.