%0 Journal Article
%T Text Clustering Research on the Max Term Contribution Dimension Reduction and Simulated Annealing Algorithm<br>最大词重降维算法与模拟退火算法相结合的文本聚类方法研究
%A Lu Guoli Wang Xiaohua Wang Rongbo
%A <br>陆国丽
%A 王小华
%A 王荣波
%J 现代图书情报技术
%D 2008
%I 
%X This paper presents a new algorithm for text character extraction and dimension reduction based on the Max Term Contribution. Its main idea is computing the contribution of each term in the high dimension document-base and extracting the maximum contribution terms to construct a low dimension document-base from the high dimension document-base using the search algorithm. Then a modified K-means clustering method based on the Simulated Annealing (SA) is presented to cluster the low dimension document datum which is obtained by MTC. Finally, some experiments show that the new method can improve the cluster precision.
%K Text clustering
%K Max term contribution
%K Character extraction
%K Simulated annealing<br>文本聚类
%K 最大词重
%K 特征提取
%K 模拟退火
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=B5EDD921F3D863E289B22F36E70174A7007B5F5E43D63598017D41BB67247657&cid=E46382710BF131B2&jid=24AADBCD0D5373C73F37F78D10E2F717&aid=A5FB2AD4BC2A491F8EECEFF9ADE35AFD&yid=67289AFF6305E306&vid=B91E8C6D6FE990DB&iid=59906B3B2830C2C5&sid=BE33CC7147FEFCA4&eid=F4B561950EE1D31A&journal_id=1003-3513&journal_name=现代图书情报技术&referenced_num=1&reference_num=10