Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (26): 7-10.DOI: 10.3778/j.issn.1002-8331.2010.26.003

• 博士论坛 • Previous Articles     Next Articles

Adaptive rough k-means clustering algorithm

ZHOU Tao1,2   

  1. 1.School of Science,Ningxia Medical University,Yinchuan 750004,China
    2.Department of Mathematics,Shaanxi University of Technology,Hanzhong,Shaanxi 723000,China
  • Received:2010-05-04 Revised:2010-06-21 Online:2010-09-11 Published:2010-09-11
  • Contact: ZHOU Tao

具有自适应参数的粗糙k-means聚类算法

周 涛1,2   

  1. 1.宁夏医科大学 理学院,银川 750004
    2.陕西理工学院 数学系,陕西 汉中 723000
  • 通讯作者: 周 涛

Abstract: Rough clustering is one of valid clustering algorithms in indeterminate clustering.Through analyzing rough k-means algorithm,its shortcoming about the parameters adjustment about wlwu and ε is pointed out.Rough k-means clustering algorithm with adaptive parameters is presented.This algorithm can optimize clustering result of rough k-means,and decrease sensitivity about noise.Finally,this algorithm’s validity is proved by experiments.

Key words: rough set, k-means clustering algorithm, adaptive

摘要: 粗糙聚类是不确定聚类算法中一种有效的聚类算法,这里通过分析粗糙k-means算法,指出了其中3个参数wlwu和ε设置时存在的缺点,提出了一种自适应粗糙k-means聚类算法,该算法能进一步优化粗糙k-means的聚类效果,降低对“噪声”的敏感程度,最后通过实验验证了算法的有效性。

关键词: 粗糙集, k-means聚类算法, 自适应

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