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计算机工程 ›› 2006, Vol. 32 ›› Issue (6): 201-202,217.

• 人工智能及识别技术 • 上一篇    下一篇

基于粒子群优化算法的聚类分析

刘向东1,2,沙秋夫2,刘勇奎1,段晓东1   

  1. 1. 大连民族学院非线性信息技术研究所,大连 116600;2. 鞍山科技大学理学院,鞍山 114004
  • 出版日期:2006-03-20 发布日期:2006-03-20

Analysis of Classification Using Particle Swarm Optimization

LIU Xiangdong1,2 , SHA Qiufu2, LIU Yongkui 1, DUAN Xiaodong1   

  1. 1. Research Institute of Nonlinear Information Technology, Dalian Nationalities University, Dalian 116600;2. Faculty of Science, Anshan University of Science and Technology, Anshan 114044
  • Online:2006-03-20 Published:2006-03-20

摘要: 基于求解实优化问题时粒子群算法优于遗传算法这一事实,在基于遗传算法的K-均值聚类算法的基础上,给出了一种基于粒子群优化算法的聚类方法。实验结果显示,基于粒子群优化算法的聚类方法在收敛速度方面明显优于基于遗传算法的聚类方法。

关键词: 粒子群优化算法;聚类分析;K-均值算法

Abstract: It is proved by experiments that the particle swarm optimization is superior to the genetic algorithm while solving the problems of real optimization. A kind of particle swarm optimization cluster method is provided on the basis of the genetic algorithm K-means cluster method. The experimental result shows that the particle swarm optimization cluster method is obviously superior to the genetic algorithm K-means cluster method since it has faster convergence rate.

Key words: Particle swarm optimization; Clustering; K-means algorithm