LUO HuiLan1,2, KONG FanSheng1, YANG XiaoBing1, LIU BiHong1
1.Institute of Artificial Intelligence, Zhejiang University, Hangzhou 310027 2.Institute of Information Engineering, Jiangxi University of Science and Technology, Gangzhou 341000
Abstract：Mathematical morphology has been widely used in image analysis. Classical clustering methods often fail to deliver satisfactory results, especially when clusters have arbitrary shapes. Through some techniques for selecting discretization parameters and structural elements, a new approach to cluster analysis is proposed, which is based on the mathematical morphology operations. Clusters are well separated by means of hierarchical mathematical morphology procedures. Experimental results demonstrate that the proposed clustering algorithm clusters data better than the classical clustering algorithms, and an optimal number of clusters could be found.
罗会兰，孔繁胜，杨小兵，刘必红. 基于数学形态学的聚类分析*[J]. 模式识别与人工智能, 2006, 19(6): 727-733.
LUO HuiLan, KONG FanSheng, YANG XiaoBing, LIU BiHong. Cluster Analysis Based on Mathematical Morphology. , 2006, 19(6): 727-733.
[1] Han J W, Kamber M. Data Mining: Concepts and Techniques. New York, USA: Morgan-Kanufman, 2000 [2] Gonzalez R C, Woods R E. Digital Image Processing. 2nd Edition. Upper Saddle River, USA: Prentice Hall, 2002 [3] Postaire J G,Zhang R D, Lecocq-Botte C. Cluster Analysis by Binary Morphology. IEEE Trans on Pattern Analysis and Machine Intelligence, 1993, 15(2): 170-180 [4] Starovoitov V. A Clustering Technique Based on the Distance Transform. Pattern Recognition Letters, 1996, 17(3): 231-239 [5] Yoda I, Yamamoto K, Yamada H. An Automatic Acquisition of Hierarchical Mathematical Morphology Procedures by Genetic Algorithm // Proc of the 12th International Conference on Pattern Recognition. Jerusalem, Israel, 1994, Ⅱ: 421-425 [6] Theodoridis S, Koutroumbas K. Pattern Recognition. 2nd Edition. Amsterdam, Netherland: Elsevier Science, 2004 [7] Guha S, Rastogi R, Shim K. CURE: An Efficient Clustering Algorithm for Large Database. Information Systems, 2001, 26(1): 35-58 [8] Zhang T, Ramakrishnan R, Livny M. Birch: An Efficient Data Clustering Method for Very Large Database // Proc of the ACM SIGMOD Conference on Management of Data. Montreal, Canada, 1996: 103-114 [9] Ester M, Kriegel H P, Sander J, et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise // Proc of the 2nd International Conference on Knowledge Discovery and Data Mining. Portland, USA, 1996: 226-231 [10] Karypis G, Han E H, Kumar V. Chameleon: Hierarchical Clustering Using Dynamic Modeling. IEEE Computer, 1999, 32(8): 68-75 [11] Costa J A F, de Netto M L A. Clustering of Complex Shaped Data Sets via Kohonen Maps and Mathematical Morphology. Proc of SPIE, 2001, 4384: 16-27 [12] Wu S, Chow T W S. Clustering of the Self-Organizing Map Using a Clustering Validity Index Based on Inter-Cluster and Intra-Cluster Density. Pattern Recognition, 2004, 37(2): 175-188 [13] Matheron G. Random Sets and Integral Geometry. New York, USA: Wiley, 1975 [14] Serra J. Image Analysis and Mathematical Morphology. London, UK: Academic Press, 1982 [15] Breen E J, Jones R, Talbot H. Mathematical Morphology: A Useful Set of Tools for Image Analysis. Statistics and Computing, 2000, 10(2):105-120
2006, Vol. 19 
