关键词: k-means 算法, 相异性度量, 权, 变异系数

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.

Key words: k-means clustering, dissimilarity measure, weighting, coefficient of variation