关键词: K-means聚类, 聚类数, 聚类有效性指标, 聚类分析

Abstract: K-means clustering algorithm clusters datasets according to the certain clustering number k. However,k cannot be confirmed beforehand. A new clustering validity index was designed from the standpoint of sample geometry. Based on the index, a new method for determining the optimal clustering number in K-means clustering algorithm was proposed. Theoretical research and experimental results demonstrate the validity and good performance of the above-mentioned algorithm.

Key words: K-means clustering, number of clusters, clustering validity index, cluster analysis