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计算机系统应用英文版:2016,25(3):21-27
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隶属度修正类模糊C-均值聚类算法的对比分析
(1.浙江工贸职业技术学院 信息传媒学院, 温州 325003;2.江南大学 物联网工程学院 智能系统与网络计算研究所, 无锡 214122)
Comparison of Membership Correction Fuzzy C-Means Clustering Algorithms
(1.College of Information and Communications, Zhejiang Industry & Trade Vocational College, Wenzhou 325003, China;2.Institute of Intelligent Systems and Network Computing, School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China)
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Received:July 05, 2015    Revised:September 06, 2015
中文摘要: 为了更深入的对模糊C-均值聚类算法进行研究,从提高算法的收敛速度角度着手,总结归纳了以RCFCM、S-FCM、PIM和FCMα等算法为代表的隶属度修正类模糊C-均值聚类算法,跟踪阐述了其研究进展.为了展现算法的全貌,从不同参数和不同模糊指数等角度实验分析了各算法的性质和特点.根据实验分析结果,为其后续研究指明了方向.上述工作将为FCM算法的进一步研究提供有益的参考.
Abstract:In order to study on the fuzzy C-means clustering algorithm deeply, starting from the angle of improving convergence speed of the algorithm, the membership correction fuzzy C-means clustering algorithms which are represented by RCFCM, S-FCM, PIM and FCMα algorithm etc. are summarized and the research progress is tracked. To show the panorama of the algorithms, the nature and characteristics of each algorithm are analyzed by the experiments with different parameters and different fuzzy index. According to the experimental results, the direction of further research of the algorithms is pointed out. The above work can provide a valuable reference for further research on FCM algorithm.
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基金项目:浙江省高校访问学者教师专业发展项目(FX2014175);温州市公益性科技计划项目(G20140049);浙江工贸职业技术学院教师科技创新项目(X140203)
引用文本:
郭华峰,梁久祯,潘修强.隶属度修正类模糊C-均值聚类算法的对比分析.计算机系统应用,2016,25(3):21-27
GUO Hua-Feng,LIANG Jiu-Zhen,PAN Xiu-Qiang.Comparison of Membership Correction Fuzzy C-Means Clustering Algorithms.COMPUTER SYSTEMS APPLICATIONS,2016,25(3):21-27