%0 Journal Article
%T A Fast Clustering Algorithm for Large-scale and High Dimensional Data<br>一种大规模高维数据快速聚类算法
%A LIU Ming WANG Xiao-Long LIU Yuan-Chao School of Computer Science
%A Technology
%A Harbin Institute of Technology
%A Harbin
%A <br>刘铭
%A 王晓龙
%A 刘远超
%J 自动化学报
%D 2009
%I 
%X A novel self-organizing-mapping algorithm for large-scale and high dimensional data is proposed in this paper. By compressing neurons' feature sets and only selecting relative features to construct neurons' feature vectors, the clustering time can be dramatically decreased. Simultaneously, because the selected features can effectively distinguish different documents which are mapped to different neurons, the algorithm can avoid interferences of irrelative features and improve clustering precision. Experiments results demonstrate that this methodology can accelerate clustering speed and improve clustering precision significantly and can reach relatively ideal clustering effect.
%K Vector compression
%K neuron combination
%K intra-cluster similarity
%K inter-cluster distinctness<br>向量压缩
%K 神经元合并
%K 类内相似度
%K 类间区分度
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=8455D55176E75FD0C0560001366306F7&yid=DE12191FBD62783C&vid=6209D9E8050195F5&iid=DF92D298D3FF1E6E&sid=EE7D0B10C851F35D&eid=9C230FD2B3A7F308&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=0