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| Improved FCM Clustering Algorithm Based on Spatial Correlation and Membership Smoothing |
| XIAO Mansheng①② XIAO Zhe① WEN Zhicheng② ZHOU Liqian② |
①(College of Science and Technology, Hunan University of Technology, Zhuzhou 412008, China)
②(School of Computer Science, Hunan University of Technology, Zhuzhou 412008, China) |
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Abstract Concerning the problem that general Fuzzy C-Means (FCM) and its improved algorithm are sensitive to noise in the samples clustering and clustering boundary is not accurate enough, an improved FCM clustering algorithm based on spatial correlation is proposed. Firstly, it can improve the method of clustering center calculation and the function of distance calculation, through analyzing spatial distribution characteristics, interaction and influence value of the samples. Then, it redefines the fuzzy membership matrix through introducing a control parameter during summing membership of the samples with neighborhood information, thus realizing smoothing membership of neighborhood samples. Theoretical analysis and experimental results show that the improved algorithm has a better effect for samples with a lot of noise, and that the regional boundary value can process the image better.
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Received: 05 July 2016
Published: 28 February 2017
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| Fund: The Natural Science Foundation of Hunan Province (2015JJ2047, 2016JJ5034, 2016JJ5036), The Scientific Research Project of Hunan Provincial Department of Education (15A055, 15C0403) |
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Corresponding Authors:
XIAO Mansheng
E-mail: xiaomansheng@tom.com
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