Abstract: In view of the advantages and disadvantages of K-harmonic means and chaos particle swarm optimization clustering algorithm，a chaos particle swarm optimization clustering algorithm merged with K-harmonic means is provided.Particle swarm is firstly divided into sub-groups，resulting in an iterative process for each particle based on its extreme value and location of the individual sub-populations in the global extremum to update their position.Secondly，the introduction of variable-scale chaotic mutation，inhibit the premature convergence and improve the calculation accuracy.The new algorithm can avoid the algorithm into a local optimum，guarantee the convergence speed while enhancing the capacity of global search algorithms，improve the clustering efficiency.

Key words: K-harmonic means, chaotic particle swarm optimization, cluster

摘要： 针对K-调和均值和混沌粒子群聚类算法的优缺点，提出了一种融合K-调和均值的混沌粒子群聚类算法。首先通过K-调和均值方法把粒子群分成若干个子群体，每个粒子根据其个体极值和所在子种群的全局极值来更新位置。其次，算法中引入变尺度混沌变异，抑制了早熟收敛，提高了计算精度。实验证明，该算法可以有效地避免算法陷入局部最优，在保证收敛速度的同时增强了算法的全局搜索能力，明显改善了聚类效果。　

关键词: K-调和均值, 混沌粒子群, 聚类