International Journal of Fuzzy Systems

, Volume 21, Issue 8, pp 2632–2649 | Cite as

A Novel Fuzzy c-Means Clustering Algorithm Using Adaptive Norm

  • Yunlong Gao
  • Dexin Wang
  • Jinyan PanEmail author
  • Zhihao Wang
  • Baihua Chen


The fuzzy c-means (FCM) clustering algorithm is an unsupervised learning method that has been widely applied to cluster unlabeled data automatically instead of artificially, but is sensitive to noisy observations due to its inappropriate treatment of noise in the data. In this paper, a novel method considering noise intelligently based on the existing FCM approach, called adaptive-FCM and its extended version (adaptive-REFCM) in combination with relative entropy, are proposed. Adaptive-FCM, relying on an inventive integration of the adaptive norm, benefits from a robust overall structure. Adaptive-REFCM further integrates the properties of the relative entropy and normalized distance to preserve the global details of the dataset. Several experiments are carried out, including noisy or noise-free University of California Irvine (UCI) clustering and image segmentation experiments. The results show that adaptive-REFCM exhibits better noise robustness and adaptive adjustment in comparison with relevant state-of-the-art FCM methods.


Fuzzy c-means clustering Adaptive norm Noise robustness Relative entropy 



This study was supported by the National Natural Science Foundation of China (61203176) and the Natural Science Foundation of Fujian Province (2013J05098, 2016J01756).


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Copyright information

© Taiwan Fuzzy Systems Association 2019

Authors and Affiliations

  • Yunlong Gao
    • 1
  • Dexin Wang
    • 1
  • Jinyan Pan
    • 2
    Email author
  • Zhihao Wang
    • 1
  • Baihua Chen
    • 1
  1. 1.Department of AutomationXiamen UniversityXiamenChina
  2. 2.College of Information EngineerJimei UniversityXiamenChina

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