K-means Clustering Algorithm Based On Coefficient of Variation

摘要：

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 A-means clustering algorithm, which treats all features equally and does not accurately reflect the similarity among samples. A-means clustering algorithm based on coefficient of variation (CV-Ameans) is proposed in this paper to solve this problem. The CV-A-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 A-means algorithm do.

关键词： k-means clustering similarity metrics weighting coefficient of variation

作者: Shuhua Ren Alin Fan

作者单位: School of Information Science and Engineering Dalian Polytechnic University Dalian, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 2109-2112

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）