Parameter selection for suppressed fuzzy c-means clustering algorithm based on fuzzy partition entropy

摘要：

　　Suppressed fuzzy c-means(S-FCM)could improve the convergence speed of FCM,also keep the good classification accuracy of fuzzy c-means clustering algorithm(FCM),it had been studied by many researchers and applied in many fields.The parameter selection is very important on the S-FCM algorithm performance.Hung proposed a modified S-FCM,named as MS-FCM,to determine the parameter α with prototype-driven learning.α is updated each iteration and successful used in MRI segmentation.In this paper,we give another method to select the parameter α based on the fuzzy partition entropy.The experimental results show that the proposed algorithm can be considered as a efficient algorithm for the self-adaption determined the suppressed rate α.

关键词： FCM clustering algorithm S-FCM clustering algorithm MS-FCM clustering algorithm Suppressed rate

作者: Jing Li Jiulun FAN

作者单位: School of Electronic engineering,Xidian University,Xian Shaanxi,china School of Communication and Information Engineering,Xian University of Posts and Telecommunications

会议类型: 国际会议

会议名称: The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

会议地点: 厦门

会议语种:英文

页码: 81-86

在线出版日期: 2014-08-19（万方平台首次上网日期，不代表论文的发表时间）