%0 Journal Article
%T Research of Chinese spectral clustering with LSA<br>结合LSA的中文谱聚类算法研究
%A XIONG Zhong-yang
%A BAO Zi-qiang
%A LI Zhi-xing
%A ZHANG Yu-fang
%A <br>熊忠阳
%A 暴自强
%A 李智星
%A 张玉芳
%J 计算机应用研究
%D 2010
%I 
%X Traditional text samples similarity matrix for spectral cluster heavily rely on the vector space model which ignores the semantic relationship among terms. It will give rise to problems such as curse of dimensionality, feature redundancy and high computing cost. To solve the problems above, this paper proposed a new method based on LSA to solve it, which used SVD to lowering rank of matrices. The experimental results turn out that the new method enhances the cluster accuracy and less the data-process elapsed time.
%K text clustering
%K LSA
%K SVD
%K spectral cluster<br>文本聚类
%K 潜在语义分析
%K 奇异值分解
%K 谱聚类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=764CD1193D464617E6C6A0F7EB11459D&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=38B194292C032A66&sid=3D9E2C3DB640307A&eid=C45B504FED793340&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=5