CPSO: Chaotic Particle Swarm Optimization for Cluster Analysis

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