%0 Journal Article
%T Highly effective fuzzy clustering algorithm based on improved network<br>一种基于改进神经网络的高效模糊聚类算法
%A CHEN Xiu-min
%A ZOU Kai-qin
%A YAN Zhong-wen
%A ZHU Mei-ning
%A FU Chang-qing
%A YANG Yan-ping
%A YAN Dan-dan
%A <br>陈秀敏
%A 邹开其
%A 闫忠文
%A 祝美宁
%A 闫娟娟
%A 杨艳萍
%A 阎丹丹
%J 计算机应用
%D 2008
%I 
%X In order to solve the problems in fuzzy clustering by using Self-Organizing Feature Map (SOFM) network, this paper introduced an improved structural self-organizing feature map network and adopted self-adapting initial condition. It can handle the clustering problem of high dimensional data and the clusters with arbitrary shapes. Compared with other algorithms with the same clustering effect, it has lower clustering time complexity. Experiments indicate this algorithm has better clustering effect compared to single SOFM network and other kin algorithms.
%K clustering
%K self-organizing feature map (SOFM)
%K topological similarity
%K self-adaptive<br>聚类
%K 自组织特征映射
%K 拓扑相似度
%K 自适应
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=D6F0A1C9BD3F9F65FFA13795B4299EE7&yid=67289AFF6305E306&vid=D3E34374A0D77D7F&iid=94C357A881DFC066&sid=C9D6A9952042973F&eid=8339CA1DD1F3C6AB&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=9