一种融合变异系数的k-mean聚类分析方法  

K-means clustering algorithm based on coefficient of variation

作　　者：范阿琳[1] 任树华[1] 

机构地区：[1]大连工业大学信息科学与工程学院,辽宁大连116034

出　　处：《计算机工程与应用》2012年第35期114-117,共4页Computer Engineering and Applications

摘　　要：K-means聚类算法的性能依赖于距离度量的选择,k-means算法将欧几里德距离作为最常用的距离度量方法。欧氏距离认为所有属性在聚类中作用是相同的,但是这种距离度量方法并不能准确反映样本间的相异性。针对这种不足,提出了融合变异系数的k-means聚类分析方法(CV-k-means),利用变异系数权重向量来减少不相关属性的影响。实验结果表明,该方法的聚类结果优于k-means算法。The performance of k-means clustering algorithm depends on the selection of distance metrics. The Euclid distance is commonly chosen as the similarity measure in k-means clustering algorithm, which treats all features equally and does not accurately reflect the dissimilarity among samples. K-means clustering algorithm based on Coefficient of Variation（CV-k-means） is proposed in this paper to solve this problem. The CV-k-means clustering algo- rithm uses variation coefficient weight vector to decrease the affects of irrelevant features. The experimental results show that the proposed algorithm can generate better clustering results than k-means algorithm.

关 键 词：K-MEANS算法 相异性度量 权 变异系数 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]