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A Novel Fuzzy c-Means Clustering Algorithm Using Adaptive Norm

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Abstract

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.

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Acknowledgements

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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Authors and Affiliations

  1. Department of Automation, Xiamen University, Xiamen, China

    Yunlong Gao, Dexin Wang, Zhihao Wang & Baihua Chen

  2. College of Information Engineer, Jimei University, Xiamen, China

    Jinyan Pan

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  1. Yunlong Gao
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  4. Zhihao Wang
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Correspondence to Jinyan Pan.

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Gao, Y., Wang, D., Pan, J. et al. A Novel Fuzzy c-Means Clustering Algorithm Using Adaptive Norm. Int. J. Fuzzy Syst. 21, 2632–2649 (2019). https://doi.org/10.1007/s40815-019-00740-9

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