%0 Journal Article
%T Clustering-based Improved K-means Text Feature Selection<br>聚类模式下一种优化的K-means文本特征选择
%A LIU Hai-feng
%A LIU Shou-sheng
%A ZHANG Xue-ren
%A <br>刘海峰
%A 刘守生
%A 张学仁
%J 计算机科学
%D 2011
%I 
%X Text feature reduction is the key technology in text categorization. In addition, K-means is an partitioning method which usually be used. With regards to this arithmetic excessively incentive to the initial centers and the isolated points, the improved K-means arithmetic was put forward which is used in text feature selection. Text feature clustering was improved by optimizing primitive class center's options and the elimination of isolated point Following text classification test shows that the K-means arithmetic put forward in this paper has a good feature selection ability and high efficiency in text categorization.
%K Feature selection
%K Clustering
%K K-means
%K Text categorization<br>特征选择，聚类，K均值，文本分类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=2A4CB76728C5160952769362231E608C&yid=9377ED8094509821&vid=16D8618C6164A3ED&iid=CA4FD0336C81A37A&sid=64963996248CBF47&eid=2BA123C6EB9D54C2&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=10