Robust Fuzzy C-Means Based on Adaptive Relaxation

Yunlong GAO; Zhihao WANG; Jinyan PAN; Sizhe LUO; Dexin WANG

doi:10.11999/JEIT190556

Volume 42 Issue 7

Jul. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(7): 1774-1781

Yunlong GAO, Zhihao WANG, Jinyan PAN, Sizhe LUO, Dexin WANG. Robust Fuzzy C-Means Based on Adaptive Relaxation[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1774-1781. doi: 10.11999/JEIT190556

Citation:

Yunlong GAO, Zhihao WANG, Jinyan PAN, Sizhe LUO, Dexin WANG. Robust Fuzzy C-Means Based on Adaptive Relaxation[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1774-1781. doi: 10.11999/JEIT190556

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Robust Fuzzy C-Means Based on Adaptive Relaxation

doi: 10.11999/JEIT190556

1.
College of Aeronautics and Astronautics, Xiamen University, Xiamen 361102, China
2.
College of Information Engineering, Jimei University, Xiamen 361021, China

Funds: The National Natural Science Foundation of China (61203176), The Provincial Natural Science Foundation of Fujian Province (2013J05098, 2016J01756)

Received Date: 2019-07-24
Rev Recd Date: 2020-03-13

Available Online: 2020-04-09

Publish Date: 2020-07-23

Abstract

Abstract

Noise is one of the most important influences for clustering. Existing fuzzy clustering methods try to reduce the impact of noise by relaxing the constraint condition of membership. But there are still two basic problems to be solved. The first is how to evaluate the probability that a sample point is a noise. The second is how to retain the effect of normal points while suppressing the impact of noise. To solve these two problems, Robust Fuzzy C-Means based on Adaptive Relaxation (AR-RFCM) is proposed. The new model estimates the reliability of sample points by the method of the K-Nearest Neighbor (KNN). It adjusts adaptively the relaxation parameters to reduce the impact of noise, and keeps the effect of reliable sample points at the same time. In addition, AR-RFCM utilizes the sparsity of membership in K-means to improve the effect of reliable sample points. Therefore, the compactness of clusters is improved and the impact of noise is suppressed. Experiments demonstrate that AR-RFCM has a good robustness for noise, and also achieves higher rand index in all 25 UCI data sets, even averagely higher than FCM 7.7864%.
- Noise,
- Clustering,
- Fuzzy C-Means (FCM),
- Adaptive,
- Relaxation

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References(16)

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