Research on Selection Method of the Optimal Weighting Exponent and Clustering Number in Fuzzy C-Means Algorithm

摘要：

The Fuzzy C-Means (FCM) algorithm is commonly used for clustering. The weighting exponent m and the clustering number C are the important parameters in FCM algorithm. Conventional fuzzy clustering method must use both of the two prespecified parameters, so in this paper we analyses the original algorithm and studies on the optimal selection methods of the m and c by introducing the fuzzy decision theory and the validity index Vkwon basing on the geometric structure of the dataset. Experiment results with the IRIS dataset show that this algorithm can obtain the optimal weighting exponent m* and the optimal clustering number C* . Moreover, the fact that the best value scope of m achieved in practical applications indicates that the method is effective.

关键词： weighting exponent optimal clustering number fuzzy c-means fuzzy decision theory

作者: Jian Cui Qiang Li Jun Wang Da-Wei Zong

作者单位: Department of Early Warning Surveillance Intelligence Wuhan Radar Institute HuBei, WuHan, China, 430 Key Research Lab Wuhan Radar Institute HuBei, WuHan, China, 430019 Add Science & Technology Development CO., LTD Wuhan East Lake Development Zone HuBei, WuHan, China,

会议类型: 国际会议

会议名称: 2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

会议地点: 长沙

会议语种:英文

页码: 2466-2469

在线出版日期: 2010-05-11（万方平台首次上网日期，不代表论文的发表时间）