基于密度和最近邻的K-means文本聚类算法  

K-means text clustering algorithm based on density and nearest neighbor

作　　者：张文明[1] 吴江[1] 袁小蛟[1] 

机构地区：[1]西北大学信息科学与技术学院,西安710127

出　　处：《计算机应用》2010年第7期1933-1935,共3页journal of Computer Applications

基　　金：西北大学科研启动基金资助项目(PR08067);西北大学研究生自主创新基金资助项目(08YZZ35)

摘　　要：初始中心点的选择对于传统的K-means算法聚类结果影响较大,容易使聚类陷入局部最优解。针对这个问题,引入密度和最近邻思想,提出了生成初始聚类中心的算法Initial。将所选聚类中心用于K-means算法,得到了更好的应用于文本聚类的DN-K-means算法。实验结果表明,该算法可以生成聚类质量较高并且稳定性较好的结果。The selection of initial focal point has great influence on the clustering results of traditional K-means algorithm,for it tends to get a local optimal solution when inappropriately assigned.In view of this issue,initial algorithm that can generate the initial cluster center was proposed,through introducing the density and nearest neighbor idea.These selected centers were used for K-means algorithm;a better text clustering algorithm called DN-K-means was put forward.The results of experiments indicate that the algorithm can lead to results with high and steady clustering quality.

关 键 词：文本聚类 密度 最近邻 F度量 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]