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计算机工程 ›› 2008, Vol. 34 ›› Issue (16): 201-203. doi: 10.3969/j.issn.1000-3428.2008.16.069

• 人工智能及识别技术 • 上一篇    下一篇

基于粒群优化的K均值算法及其应用

宋 凌,李枚毅,李孝源   

  1. (湘潭大学信息工程学院,湘潭 411105)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-08-20 发布日期:2008-08-20

PSO-based K-means Algorithm and Its Application

SONG Ling, LI Mei-yi, LI Xiao-yuan   

  1. (Institute of Information Engineering, Xiangtan University, Xiangtan 411105)
  • Received:1900-01-01 Revised:1900-01-01 Online:2008-08-20 Published:2008-08-20

摘要: 针对K均值聚类算法依赖于初始值的选择,且容易收敛于局部极值的缺点,提出一种基于粒群优化的K均值算法。利用粒群优化指导K均值算法的初始值选择,使其容易收敛到全局极值。将该算法应用到入侵检测中,实验结果表明该算法聚类效果好、收敛快、容易实现。

关键词: 粒群优化, 入侵检测, K均值

Abstract: Local optimality and initialization dependence disadvantages of K-means are analyzed and a PSO-based K-means algorithm is proposed. The PSO is used to instruct the initialization of the cluster’s centers and it is made very easy to converge toward global optimality. The algorithm is applied to intrusion detection system, the results of tests elucidate that it clusters effectively, converges quickly and is realized easily.

Key words: Particle Swarm Optimization(PSO), intrusion detection, K-means

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