基于精细簇的K-Means文本聚类  

Refined cluster based K-Means text clustering method

作　　者：侯荣涛[1] 路郁[1] 王琴[1] 周彬[2] 

机构地区：[1]南京信息工程大学计算机与软件学院,江苏南京210044 [2]南京信息工程大学电子与信息工程学院,江苏南京210044

出　　处：《计算机工程与设计》2015年第7期1794-1799,共6页Computer Engineering and Design

基　　金：江苏省普通高校研究生科研创新计划基金项目(CXLX12_0515);南京信息工程大学教学改革基金项目(13JY001)

摘　　要：为解决K-Means算法对初始聚类中心的敏感性,提出基于精细簇的K-Means稳定算法。采用基于密度算法的预分类技术来获得高密度的核心类,确定能够代表数据集结构的类层次树;根据类层次树中具有较高代表性的子类中心进行K-Means聚类,获得精细簇;按照层次树中的类归属合并精细簇,获得精确稳定的聚类效果。实验结果表明,该方法能够克服K-Means由于随机初始质心造成的结果不稳定性,一定程度上提高了聚类效果。To decrease the sensitivity of initial centroids of K-Means,a stable method based on refined cluster was proposed.The density based algorithm was used for pre-classification to get CHT（cluster-hierarchy-tree）that contained the cluster structure of data set.The sub-clusters with high density in CHT were chosen as initial parameters of K-Means.According to the relationship in CHT,the small classes produced by K-Means were merged and the clustering result was gained.The experimental results show the stability and better clustering performance of the proposed method.

关 键 词：K-MEANS 密度 预分类 层次树 精细簇 

分 类 号：TP311.1[自动化与计算机技术—计算机软件与理论]