【摘要】 聚类是非监督学习的关键问题.本文在模糊最小-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-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.更多还原

【Abstract】 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.更多还原