%0 Journal Article
%T Improved Fuzzy Clustering Algorithm Based on Data Weighted Approach<br>基于数据加权策略的模糊聚类改进算法
%A Tang Cheng-long
%A Wang Shi-gang
%A Xu Wei
%A <br>唐成龙
%A 王石刚
%A 徐威
%J 电子与信息学报
%D 2010
%I 
%X A new data exponent weighted fuzzy clustering approach is proposed by introducing a set of exponent weighting factors and influence exponent, the new approach makes it possible to treat the data points discriminatively. The new approach is combined with the existing Gustafson-Kessel (G-K) algorithm and a new algorithm, DWG-K is presented. Numerical experiments show that the DWG-K is better than G-K in improving the quality of clustering, and in the outliers mining, DWG-K detects the outliers with the global view and the physical meaning of outliers is clearer, and moreover, the computational efficiency is significantly higher than the current widely used density-based method.
%K Fuzzy clustering
%K Data weighted approach
%K Data weighted G-K
%K Outliers mining<br>模糊聚类
%K 数据加权策略
%K 数据加权G-K
%K 离群点挖掘
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=FD1E6EF1E1F9B14D14EE2C6CFC449599&yid=140ECF96957D60B2&vid=9971A5E270697F23&iid=B31275AF3241DB2D&sid=7671EBF56E8E19A5&eid=2F8F471CEC23CD85&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=13