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�������պ����ѧѧ�� 2007, Vol. 33 Issue (09) :1121-1126    DOI:
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����ΰ1, ����֥2*
1. �������պ����ѧ ��ѧԺ, ���� 100083;
2. �廪��ѧ��ѧ��ѧϵ, ����100084
Bezdek type fuzzy attribute C-means clustering algorithm
Liu Jingwei1, Xu Meizhi2*
1. School of Science, Beijing University of Aeronautics and Astronautics, Beijing 100083, China;
2. Department of Mathematical Sciences, Tsinghua University, Beijing, 100084

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ժҪ �ƹ������Ծ�ֵ�����㷨,����˻���ģ���Ȫ�m����Bezdek��ģ������C��ֵ�����㷨(FAMC),������FAMC�㷨�ĵ����㷨,��������ģ���Ȫ�m�����㷨�����Ե�Ӱ��.�ڱ�׼Iris���ݼ�����������оƬ������ݵ�ģʽʶ��ʵ����,��֤�˸��㷨����ģ��C��ֵ�㷨�����Ծ�ֵ�����㷨.
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Abstract�� Bezdek type fuzzy attribute C-means clustering algorithm(FAMC) was proposed by extending attribute means clustering(AMC) algorithm based on fuzziness index (or weighting exponent) ��m��. The iterative algorithm was derived and the effect of fuzziness index ��m�� on objective function convergence was discussed. The experimental results of pattern recognition performances on standard Iris database and tumor/normal gene chip expression data demonstrate that FAMC is more effective than fuzzy C-means clustering(FCM) algorithm and AMC.
Keywords�� fuzzy C-means clustering   attribute means clustering   stable function   gene expression data     
Received 2006-08-23;


About author: ����ΰ(1970-),��,ɽ��������,������,jwliu@buaa.edu.cn.
����ΰ, ����֥.Bezdek��ģ������C��ֵ�����㷨[J]  �������պ����ѧѧ��, 2007,V33(09): 1121-1126
Liu Jingwei, Xu Meizhi.Bezdek type fuzzy attribute C-means clustering algorithm[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2007,V33(09): 1121-1126
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