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

摘要： 针对传统K均值聚类方法采用聚类前随机选择聚类个数K而导致的聚类结果不理想的问题，结合空间中的层次结构，提出一种改进的层次K均值聚类算法。该方法通过初步聚类，判断是否达到理想结果，从而决定是否继续进行更细层次的聚类，如此迭代执行，从而生成一棵层次型K均值聚类树，在该树形结构上可以自动地选择聚类的个数。标准数据集上的实验结果表明，与传统的K均值聚类方法相比，提出的改进的层次聚类方法的确能够取得较优秀的聚类效果。

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