Cluster Algorithm Based on K-Means and Improved Particle Swarm Optimization

Abstract

To deal with the problem of premature convergence of the traditional K-means algorithm, a novel K-means cluster method based on the enhanced Particle Swarm Optimization (PSO) algorithm is presented. The algorithm can be had in the course of the introduction of adaptive Gaussian global extremum mutation operator, enhancing particle diversity of the population, thereby increasing the ability of global optimization algorithm. The experimental results show the proposed method can not only effectively solve the premature convergence problem, but also significantly speed up the convergence.