关键词: K均值聚类, 聚类个数, 层次结构, 层次K均值聚类算法, 聚类树

Abstract: 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.

Key words: K-means clustering, clustering number, hierarchical structure, hierarchical K-means algorithm, clustering tree