基于模糊逻辑的K-means算法研究  

Research of K-means Algorithm by Fuzzy Logic

作　　者：陈苏蓉[1] 朱晓辉[1] 

机构地区：[1]南通大学计算机科学与技术学院,江苏南通226019

出　　处：《计算机工程与科学》2012年第12期155-159,共5页Computer Engineering & Science

基　　金：南通市基础应用研究项目(K2010067;BK2011072);江苏省科技厅应用研究项目(BC2010122);江苏省高校科研成果产业化推进项目(JHB2011-45);江苏省"六大人才高峰"项目(2010-WLW-006);南通市重大科技创新专项计划项目(XA2008004);江苏高校优势学科建设工程资助项目

摘　　要：K-means算法的基本思想是通过迭代方法把所有的元素都唯一聚类到不同的簇中,使得同一簇中的质点具有最小相异度,不同簇间的元素具有最大相异度。但是,这种聚类方法使得那些属于不同簇的交叉区域中的质点也被简单地聚类到了某个簇中,因此无法表达某些元素的跨簇特性。本文提出了基于模糊逻辑的K-means算法,利用模糊逻辑来计算不同簇交叉区域中质点属于某个簇的权重,在获得聚类结果的同时可以有效描述质点的跨簇特性。实验结果表明该算法是有效的。The basic idea for the K-means algorithm is to partition all elements to different clusters by iterative method so that the elements in the same cluster have the minimum dissimilarity and the elements in different clusters have the maximum dissimilarity. However, it may simply cluster these elements in an overlap which should be in different clusters to the same cluster, so the result of clustering cannot show the element＇s overlap characteristic. In the paper, a new K-means Algorithm based by fuzzy logic is proposed. It can not only obtain the same clustering result as original algorithm but also get element＇s overlap characteristic. Experiment shows that the new algorithm is efficiency.

关 键 词：模糊逻辑 K-MEANS算法 跨簇特性 

分 类 号：TP302[自动化与计算机技术—计算机系统结构]