K-means clustering algorithm based on coefficient of variation
Shuhua Ren, Alin Fan · 2011
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 similarity 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 do.