一种改进的k-means中文文本聚类算法  

Clustering AlgorithmofOneImproved k-Means Chinese Text

作　　者：龚静[1] 李安民[1] 

机构地区：[1]湖南环境生物职业技术学院信息技术系,湖南衡阳421005

出　　处：《湖南工业大学学报》2008年第2期52-54,共3页Journal of Hunan University of Technology

基　　金：湖南省教育厅基金资助项目(07D036)

摘　　要：提出了k-means聚类算法中选取初始聚类中心及处理孤立点的新方法,改进了k-means算法对初始聚类中心和孤立点文本很敏感的不足之处,并将改进后的算法应用于中文文本聚类中。实验结果表明,改进的算法较原算法在准确率上有较大提高,并且具有更好的稳定性。This paper proposes a new way that selects initial cluster center and processes isolated points in the k-means clustering algorithm. And this method improves the deficiency that the k-means algorithm is very sensitive to the initial cluster center and the isolated point text. It applies the improved algorithm in Chinese text clustering. The experimental result indicates the improved algorithm has a higher accuracy compared with the original algorithm, and has a better stability.

关 键 词：K-MEANS算法 文本聚类 中文文本 层次聚类 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]