CPSO: Chaotic Particle Swarm Optimization for Cluster Analysis

Abstract

Background: To solve the cluster analysis better, we propose a new method based on the chaotic particle swarm optimization (CPSO) algorithm. Methods: In order to enhance the performance in clustering, we propose a novel method based on CPSO. We first evaluate the clustering performance of this model using the variance ratio criterion (VRC) as the evaluation metric. The effectiveness of the CPSO algorithm is compared with that of the traditional particle swarm optimization (PSO) algorithm. The CPSO aims to improve the VRC value while avoiding local optimal solutions. The simulated dataset is set at three levels of overlapping: non-overlapping, partial overlapping, and severe overlapping. Finally, we compare CPSO with two other methods. Results: By observing the comparative results, our proposed CPSO method performs outstandingly. In the conditions of non-overlapping, partial overlapping, and severe overlapping, our method has the best VRC values of 1683.2, 620.5, and 275.6, respectively. The mean VRC values in these three cases are 1683.2, 617.8, and 222.6. Conclusion: The CPSO performed better than other methods for cluster analysis problems. CPSO is effective for cluster analysis.