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| Hierarchical Fuzzy MinMax Clustering Algorithm |
| YANG Jing1, GAO Jun1,2, XU XiaoHong1, LIU Xu1 |
1.Laboratory of Image Information Processing, School of Computer and Information, Hefei University of Technology, Hefei 230009
2.Center of Biomimetic Sensing and Control Research, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031 |
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Abstract Clustering is considered as the most important problem of unsupervised learning. A Hierarchical Fuzzy MinMax Clustering Algorithm (HFMM) is presented based on the original Fuzzy MinMax 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 FMMCNorder dependent. Experimental results on three databases demonstrate that HFMM has high clustering performance.
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Received: 21 March 2006
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2007, Vol. 20 
