K-harmonic Means Data Clustering with Particle Swarm Optimization

被引：0
|
作者：
Lu, Kezhong [1 ]
Xu, Wenbo [2 ]
Xie, Guangqian [3 ]
机构：
[1] Chizhou Coll, Dept Comp Sci, Chizhou 247100, Peoples R China
[2] Southern Yangtze Univ, Sch Informat Technol, Wuxi 214122, Peoples R China
[3] Changzhou Inst Technol, Sch Comp Informat & Engn, Changzhou 213002, Peoples R China
来源：
DCABES 2008 PROCEEDINGS, VOLS I AND II | 2008年
关键词：
Clustering; K-Harmonic Means; Particle Swarm Optimization; Hybrid Clustering Algorithm;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
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.
引用
收藏
页码：339 / +
页数：3
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