计算机工程与应用 ›› 2014, Vol. 50 ›› Issue (21): 125-128.

• 数据库、数据挖掘、机器学习 • 上一篇    下一篇

粒子群K-means聚类算法的改进

沈  艳,余冬华,王昊雷   

  1. 哈尔滨工程大学 理学院,哈尔滨 150001
  • 出版日期:2014-11-01 发布日期:2014-10-28

Improvement of K-means based on particle swarm clustering algorithm

SHEN Yan, YU Donghua, WANG Haolei   

  1. College of Science, Harbin Engineering University, Harbin 150001, China
  • Online:2014-11-01 Published:2014-10-28

摘要: 粒子群(PSO)与K-means结合是聚类分析中的重要方法之一,但都未考虑粒子更新导致的空类问题。提出基于多子群粒子群伪均值(PK-means)聚类算法,为该问题的解决提供一种有效途径,并与粒子群K均值(PSOK-means),K-means算法进行比较。理论分析和实验表明,该算法不但可以防止空类出现,而且同时还具有非常好的全局收敛性和局部寻优能力,并且在孤立点问题的处理上也具有很好的效果。

关键词: 聚类分析, 多子群粒子群, 全局优化, K-means, PSOK-means

Abstract: Combining particle swarm with K-means algorithm is one of the important methods in data mining, but all methods almost ignore the empty class problem which the particle update causes. This paper proposes a PK-means clustering algorithm based on multi-subswarms particle swarm and pseudo means, then is compared with both PSOK-means and K-means. The theory analysis and experiments show that the algorithm not only avoids empty clustering class but also has well global convergence and the local optimization, overcomes local minimum better, has a great effect on isolated data.

Key words: clustering analysis, multi-subswarms particle swarm, global optimization, K-means, PSOK-means