Kmeans聚类分析算法中一个新的确定聚类个数有效性的指标  

New validity index for determining the number of clusters in K-means clustering

作　　者：李双虎[1] 王铁洪[2] 

机构地区：[1]河北省科学院应用数学研究所,石家庄050081 [2]河北省科学院自动化研究所,石家庄050081

出　　处：《河北省科学院学报》2003年第4期199-202,共4页Journal of The Hebei Academy of Sciences

摘　　要：　K means算法是聚类分析中使用最为广泛的算法之一。然而,该算法通常受到初始聚类条件的影响。关于这个问题的详细讨论可参看文献[1]。该算法的另一个不足之处是,聚类数目K必须作为参数由用户提供。笔者提出了一个新的有关聚类有效性的度量指标和优化的K means算法。它能自动确定最佳聚类个数。K-Means Clustering Algorithm is one of the most popular methods in cluster analysis. However, it is well known that K-means algorithm suffers from initial starting conditions effects(initial clustering and instance order effects). For more detailed discussion on initialization methods, see literature \. Another weakness of k-means algorithm is that the number of clusters, k, must be supplied as a parameter. In this paper, a new validity measure for k-means clustering is presented to allow the number of clusters to be determined automatically.

关 键 词：K-MEANS算法 聚类分析 聚类个数 有效性度量 指标 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]