K-harmonic Means Data Clustering with Particle Swarm Optimization

摘要：

Unlike K-means,the K-Harmonic means (KHM) is less sensitive to initial conditions.However,KHM as a center-based clustering algorithm can only generate a local optimal solution.In this paper,we develop a new hybrid clustering algorithm combining Particle Swarm Optimization and K-Harmonic Means (HPSO) for solving this problem.This algorithm has been implemented and tested on several real datasets.The performance of this algorithm is compared with KHM and PSO.Our computational simulations reveal the HPSO clustering algorithm combines the ability of global searching of the PSO algorithm and the fast convergence and less sensitive to initial conditions of the KHM algorithm.The HPSO is a robust clustering algorithm.

关键词： Clustering K-Harmonic Means Particle Swarm Optimization Hybrid Clustering Algorithm

作者: Kezhong Lu Wenbo Xu Guangqian Xie

作者单位: Department of Computer Science,Chizhou College,247100,China School of Information Technology,Southern Yangtze University,Wuxi,214122,China School of Computer Information and Engineering,Changzhou Institute of Technology,213002,China

会议类型: 国际会议

会议名称: 2008年国际电子商务、工程及科学领域的分布式计算和应用学术研讨会(2008 International Symposium on Distributed Computing and Applications for Business Engineering and Science)

会议地点: 大连

会议语种:英文

页码: 339-344

在线出版日期: 2008-07-27（万方平台首次上网日期，不代表论文的发表时间）