A Hybrid Clustering Algorithm Based on Fuzzy c-Means and Improved Particle Swarm Optimization

被引：0
|
作者：
Shouwen Chen
Zhuoming Xu
Yan Tang
机构：
[1] Hohai University,College of Computer and Information
[2] Chuzhou University,College of Mathematics and Information
来源：
Arabian Journal for Science and Engineering | 2014年 / 39卷
关键词：
Improved particle swarm optimization; Fuzzy ; -means; Population centroid; Exponential inertia weight; Clustering;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques. However, it is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global optimization model, which is used in many optimization problems. In this paper, a hybrid clustering algorithm based on an improved PSO and FCM is proposed, which makes use of the merits of both algorithms. Experimental results show that the proposed method has ability to escape local optima and can find more excellent optima than other nine state-of-the-art clustering methods.
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页码：8875 / 8887
页数：12
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