中文摘要：

聚类是非监督学习的关键问题．本文在模糊最小一最大聚类网络（FMMCN）和分层聚类思想的基础上，提出一种分层模糊最小一最大聚类算法．与目前的常用聚类算法相比，该方法可以根据问题需要动态确定聚类数目，并克服FMMCN样本输入次序依赖性的缺陷．对相关数据集的实验结果表明该方法具有优良的聚类性能．

英文摘要：

Clustering is considered as the most important problem of unsupervised learning. A Hierarchical Fuzzy Min-Max Clustering Algorithm （HFMM） is presented based on the original Fuzzy Min- Max Clustering Neural Network （ FMMCN ） and hierarchical clustering . Compared with the existing methods for clustering, the proposed algorithm dynamically determines the number of clusters to meet the demands of the problem. Moreover it overcomes the shortcomings of FMMCN- order dependent. Experimental results on three databases demonstrate that HFMM has high clustering performance.