Improved FCM Clustering Algorithm Based on Spatial Correlation and Membership Smoothing

XIAO Mansheng; XIAO Zhe; WEN Zhicheng; ZHOU Liqian

doi:10.11999/JEIT160710

Volume 39 Issue 5

May 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(5): 1123-1129

XIAO Mansheng, XIAO Zhe, WEN Zhicheng, ZHOU Liqian. Improved FCM Clustering Algorithm Based on Spatial Correlation and Membership Smoothing[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1123-1129. doi: 10.11999/JEIT160710

Citation:

XIAO Mansheng, XIAO Zhe, WEN Zhicheng, ZHOU Liqian. Improved FCM Clustering Algorithm Based on Spatial Correlation and Membership Smoothing[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1123-1129. doi: 10.11999/JEIT160710

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Improved FCM Clustering Algorithm Based on Spatial Correlation and Membership Smoothing

doi: 10.11999/JEIT160710

1.
2.
(College of Science and Technology, Hunan University of Technology, Zhuzhou 412008, China)

Funds:

The Natural Science Foundation of Hunan Province (2015JJ2047, 2016JJ5034, 2016JJ5036), The Scientific Research Project of Hunan Provincial Department of Education (15A055, 15C0403)

Received Date: 2016-07-05
Rev Recd Date: 2016-12-20
Publish Date: 2017-05-19

Abstract

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.
- Spatial correlation,
- Membership smoothing,
- Fuzzy C-Means (FCM),
- Spatial distance,
- Control parameter

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References(25)

References

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