%0 Journal Article
%T Application of text categorization based on improved maximum entropy means clustering algorithm<br>基于改进的最大熵均值聚类方法在文本分类中的应用*
%A ZHANG Ai-ke
%A <br>张爱科
%J 计算机应用研究
%D 2012
%I 
%X In view of the traditional text classification algorithm has the problems of the characteristics having same influence on classification results,the low rate of classification accuracy,and the increasing of the algorithm time complexity,this paper presented an improved maximum entropy C-means clustering text classification methods.This method combined the C-means clustering algorithm and the maximum entropy algorithm,set Shannon entropy as a maximum entropy model in the target function,simplified classifier forms of expression,and then used the C-means clustering algorithm to the optimal features for classification.The simulation results show that,compared with traditional text classification methods,the proposed method can fast obtain the optimal classification feature subset,greatly improve the accuracy of text classification.
%K text classification
%K maximum entropy
%K C-means clustering
%K feature selection<br>文本分类
%K 最大熵
%K C-均值聚类
%K 特征选择
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=CF88F5976DB5FA5B015E73B4B8106370&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=E158A972A605785F&sid=F8085F090F1A1511&eid=74FE4F3839E9253E&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=11