关键词: 聚类分析, 多子群粒子群, 全局优化, K-means, PSOK-means

Abstract: Combining particle swarm with K-means algorithm is one of the important methods in data mining, but all methods almost ignore the empty class problem which the particle update causes. This paper proposes a PK-means clustering algorithm based on multi-subswarms particle swarm and pseudo means, then is compared with both PSOK-means and K-means. The theory analysis and experiments show that the algorithm not only avoids empty clustering class but also has well global convergence and the local optimization, overcomes local minimum better, has a great effect on isolated data.

Key words: clustering analysis, multi-subswarms particle swarm, global optimization, K-means, PSOK-means