• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2008, Vol. 30 ›› Issue (8): 83-85.

• 论文 • 上一篇    下一篇

基于遗传算法的硬聚类算法改进

赵建民[1] 管国权[2] 王红艳[3]   

  • 出版日期:2008-08-01 发布日期:2010-05-19

  • Online:2008-08-01 Published:2010-05-19

摘要:

硬聚类算法HCM求解的结果通常都是局部的最优解,当模糊集合间的运算采用传统定义的时候,它的聚类结果中还会存在无意义的聚类集。本文通过研究表明,在HCM聚类算法中应用遗传算法,可以在一定程度上避免硬聚类算法收敛到局部最优解。因此,本文将遗传算法应用于硬聚类算法,并设计了相应的算法。但是,考虑到本算法实现时的开销  销和效率,又对该算法进行了改进,并最终提出一种新的算法——CHCM聚类算法。测试数据表明,采用改进后的聚类算法的结果90%以上能够取得全局的最优解,远远超过了采用硬聚类算法时所取得全局最优解的次数,证明了本算法的可推广性。

关键词: HCM聚类算法 CHCM聚类算法 遗传算法

Abstract:

The results of the HCM clustering algorithm are often local optimal solutions. There often exists insignificant clustering in the result of the HCM cl ustering algorithm when traditional definitions are adopted in the operations between fuzzy sets. Our research indicates that some local optimal solutio ns can be avoided by using genetic algorithms in the HCM clustering algorithm. Therefore this article applies genetic algorithms to the HCM clustering a  lgorithm and designs the corresponding algorithms. Considering the efficiency and overhead, this paper modifies this algorithm. Finally, this paper puts  forward a new algorithm named CHCM clustering. And then it compares the performance of CHCM with HCM using the test data. Experimental results show that the performance of CHCM is far better than that of HCM.

Key words: HCM clustering algorithm;CHCM clustering algorithm;GA