%0 Journal Article
%T Novel Cluster Validity Index for FCM Algorithm
%A Jian Yu
%A Cui-Xia Li
%A <br>Jian
%A Yu
%A and
%A Cui-Xia
%A Li
%J 计算机科学技术学报
%D 2006
%I 
%X How to determine an appropriate number of clusters is very important when implementing a specific clustering algorithm, like c-means, fuzzy c-means (FCM). In the literature, most cluster validity indices are originated from partition or geometrical property of the data set. In this paper, the authors developed a novel cluster validity index for FCM, based on the optimality test of FCM. Unlike the previous cluster validity indices, this novel cluster validity index is inherent in FCM itself. Comparison experiments show that the stability index can be used as cluster validity index for the fuzzy c-means.
%K cluster validity
%K optimality test
%K FCM<br>聚类有效性
%K 最优性测试
%K FCM算法
%K 聚类算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=F57FEF5FAEE544283F43708D560ABF1B&aid=E48D9BB801C855684686A6D0C7293362&yid=37904DC365DD7266&vid=659D3B06EBF534A7&iid=CA4FD0336C81A37A&sid=205BE674D84A456D&eid=B0EBA60720995721&journal_id=1000-9000&journal_name=计算机科学技术学报&referenced_num=5&reference_num=18