Improved hierarchical K-means clustering algorithm

This paper presents an improved hierarchical K-means clustering algorithm combining hierarchical structure of space,in order to solve the problem that bad result of traditional K-means clustering method by selecting the number of categories randomly before clustering.By primary K-means clustering,it determines whether re-clustering in the more fine level by the result of initial clustering.By repeated execution,a hierarchical K-means clustering tree is produced,and the number of clusters is selected automatically on this tree structure.Simulation results on UCI datasets demonstrate that comparing with traditional K-means clustering means,the better clustering results are obtained by the hierarchical K-means clustering model.