关键词: 粒子群优化, [k]均值聚类, 混沌搜索, 自适应调整因子

Abstract: The [k]-means cluster algorithm based on Improved PSO（IPK-means） is proposed for [k]-means algorithm’s disadvantage that it is vulnerable to the influence of the initial center, adding chaotic search process to the Particle Swarm Optimization（PSO） algorithm in order to increase the PSO iteration late particle swarm diversity, and in the process of particle update, it proposes a dynamic adjustment factor formula, which makes the adjustment factor related to the fitness value of the particle size, different particles in the same iteration also have different adjustment factors. Finally, the improved PSO algorithm is applied to [k]-means clustering to find a better initial center for it. The experimental results show that this algorithm can achieve better clustering results.

Key words: particle swarm optimization, [k]-means cluster, chaotic searching, adaptive adjustment factor