Comparison of Membership Correction Fuzzy C-Means Clustering Algorithms
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    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.

    Reference
    1 刘金尧,纪则轩.鲁棒空间约束的模糊聚类图像分割.中国图象图形学报,2014,19(10):1438-1448.
    2 张江丰,樊臻,张森林.基于核模糊聚类的机织物组织自动识别.纺织学报,2013,34(12):131-137.
    3 李兰,刘洋,马振等.利用图像特性的模糊聚类图像检索方法. 清华大学学报:自然科学版,2014,54(7):929-934.
    4 王海军,孙宝元,魏小鹏.基于模糊聚类的产品模块化形成过程分析.计算机集成制造系统,2003,9:123-126.
    5 蔡红昌,奉思.空间天气事件的模糊聚类.中国科学:A辑,2000,30:95-98.
    6 Havens TC, Bezdek JC, Leckie C, Hall LO, Palaniswami M. Fuzzy C-means algorithms for very large data. IEEE Trans.on Fuzzy Systems, 2012, 20(6):1130-1146.
    7 Huang HC, Chuang YY, Chen CS. Multiple kernel fuzzy clustering. IEEE Trans. on Fuzzy Systems, 2012, 20(1):120-134.
    8 Wu JJ, Xiong H, Liu C, Chen J. A generalization of distance functions for fuzzy-means clustering with centroids of arithmetic means. IEEE Trans. on Fuzzy Systems, 2012, 20(3):557-571.
    9 Zhao ZX, Cheng LZ, Cheng GQ. Neighbourhood weighted fuzzy C-means clustering algorithm for image segmentation. IET Image Processing, 2014, 8(3):150-161.
    10 Pal NR, Sarkar K. What and when can we gain from the kernel versions of C-means algorithm? IEEE Trans. on Fuzzy Systems, 2014, 22(2):363-379.
    11 魏立梅,谢维信.对手抑制式模糊C-均值算法.电子学报,2000,28(7):63-66.
    12 Özdemir D, Akarun L. A fuzzy algorithm for color quantization of images. Pattern Recognition, 2002, 35:1785-1791.
    13 Fan JL, Zhen WZ, Xie WX. Suppressed fuzzy C-means clustering algorithm. Pattern Recognition Letters, 2003, 24:1607-1612.
    14 Yang MS, Wu KL, Hsieh JN, et al. Alpha-cut implemented fuzzy clustering algorithms and switching regressions. IEEE Trans. on Systems, Man, and Cybernetics, 2008, 38(3):588-603.
    15 黄建军,谢维信.半抑制式模糊C-均值聚类算法.中国体视学与图像分析,2004,10(2):109-113.
    16 朱林,王士同,修宇.鲁棒的模糊方向相似性聚类算法.智能系统学报,2008,3(1):43-50.
    17 朱林,王士同,邓赵红.改进模糊划分的FCM聚类算法的一般化研究.计算机研究与发展,2009,46(5):814-822.
    18 黄成泉,王士同,蒋亦樟.熵指数约束的模糊聚类新算法. 计算机研究与发展,2014,51(9):2117-2129.
    19 范九伦.抑制式模糊C-均值聚类研究综述.西安邮电大学学报,2014,19(3):1-5.
    20 Szilágyi L, Szilágyi SM, BenyZó. Analytical and numerical evaluation of the suppressed fuzzy C-means algorithm:a study on the competition in C-means clustering models. Soft Computing, 2010, 14(5):495-505.
    21 Hung WL, Yang MS, Chen DH. Parameter selection for suppressed fuzzy C-means with an application to MRI segmentation. Pattern Recognition Letters, 2006, 27:424-438.
    22 Zhao F, Fan JL, Liu H. Optimal-selection-based suppressed fuzzy C-means clustering algorithm with self-tuning non local spatial information for image segmentation. Expert Systems with Applications, 2014, 41(9):4083-4093.
    23 Hung WL, Chen DH, Yang MS. Suppressed fuzzy-soft learning vector quantization for MRI segmentation. Artificial intelligence in medicine, 2011, 52(1):33-43.
    24 Krinidis S, Chatzis V. A robust fuzzy local information C-means clustering algorithm. IEEE Trans. on Image Processing, 2010, 19(5):1328-1337.
    25 Szilágyi L. Lessons to learn from a mistaken optimization. Pattern Recognition Letters, 2014, 36:29-35.
    26 Yu J, Yang MS. Optimality test for generalized FCM and its application to parameter selection. IEEE Trans. on Fuzzy Systems, 2005, 13(1):164-176.
    27 Wu KL. An analysis of partition index maximization algorithm. IEEE International Conference on Fuzzy Systems. 2009. 1785-1790.
    28 Wang J, Wang S, Chung F, et al. Fuzzy partition based soft subspace clustering and its applications in high dimensional data. Information Sciences, 2013, 246:133-154.
    29 Wu Z, Zhang H, Liu J. A fuzzy support vector machine algorithm for classification based on a novel PIM fuzzy clustering method. Neurocomputing, 2014, 125:119-124.
    30 Wu KL. Analysis of parameter selections for fuzzy C-means. Pattern Recognition, 2012, 45(1):407-415.
    31 郭华峰,陈德华,陆慧娟.面向隶属度修正模糊聚类的参数选择方法.计算机系统应用,2015,24(1):166-170.
    32 Pal NR, Bezdek JC. On cluster validity for the fuzzy C-means model. IEEE Trans. on Fuzzy Systems, 1995, 3(3):370-379.
    33 Blake CL, Merz CJ. UCI repository of machine learning databases. Irvine, CA:University of California, Department of Information and Computer Science, 1998.
    34 Yang MS, Hung WL, Chen DH. Self-organizing map for symbolic data. Fuzzy Sets and Systems, 2012, 203:49-73.
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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

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History
  • Received:July 05,2015
  • Revised:September 06,2015
  • Online: March 17,2016
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