%0 Journal Article
%T Fuzzy Fisher Criterion Based Semi-Fuzzy Clustering Algorithm<br>基于模糊Fisher准则的半模糊聚类算法
%A Cao Su-qun Wang Shi-tong Chen Xiao-feng Xie Zhen-ping Deng Zhao-Hong
%A <br>曹苏群
%A 王士同
%A 陈晓峰
%A 谢振平
%A 邓赵红
%J 电子与信息学报
%D 2008
%I 
%X The robust Fuzzy Fisher Criterion based Semi-Fuzzy Clustering Algorithm (FFC-SFCA) for linearly separable data is presented in this paper. FFC-SFCA incorporates Fisher discrimination method with fuzzy theory using fuzzy scatter matrix. By iteratively optimizing the fuzzy Fisher criterion function, the final clustering results are obtained. FFC-SFCA exhibits its robustness and capability to obtain well separable clustering results. In addition, optimal discriminant vector and threshold of classifier can also be figured out. The experimental results for artificial and real datasets demonstrate its validity and distinctive superiority over the two conventional clustering algorithms.
%K Fisher criterion
%K Semi-fuzzy clustering
%K Optimal discriminant vector<br>Fisher准则
%K 半模糊聚类
%K 最优鉴别矢量
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=23B0CD020A9A281AA9E0CC8061303A7E&yid=67289AFF6305E306&vid=340AC2BF8E7AB4FD&iid=9CF7A0430CBB2DFD&sid=7ABC4D8D4954AF61&eid=411FEAF47D74703B&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=5&reference_num=10