An Improved Binary Particle Swarm Optimization With Complementary Distribution Strategy for Feature Selection
Data clustering is a powerful technique for discerning the structure of and simplifying the complexity of large scale data. An improved technique combining Chaotic map Particle Swarm Optimization (CPSO) with an acceleration strategy is proposed in this paper. Accelerated Chaotic Particle Swarm Optimization (ACPSO) searches for cluster centers of an arbitrary data set and can effectively find the global optima. ACPSO is tested on six experimental data sets, and its performance is compared to the performance of PSO, NM-PSO, K-PSO, K-NM-PSO and K-means clustering. Results indicated that ACPSO is both robust and suitable for solving data clustering problem.