计算机工程与应用2012，Vol.48Issue(35)：114-117,4.DOI:10.3778/j.issn.1002-8331.1105-0630
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
范阿琳 1任树华1
作者信息
- 1. 大连工业大学信息科学与工程学院,辽宁大连116034
- 折叠
摘要
Abstract
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 algorithm 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Key words
k-means clustering/ dissimilarity measure/ weighting/ coefficient of variation分类
信息技术与安全科学引用本文复制引用
范阿琳,任树华..一种融合变异系数的k-mean聚类分析方法[J].计算机工程与应用,2012,48(35):114-117,4.
