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A Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm

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  • Wenyuan Zhang
  • Tianyu Huang
  • Jun Chen

Abstract

This paper proposes a modified fuzzy C-means (FCM) algorithm, which combines the local spatial information and the typicality of pixel data in a new fuzzy way. This new algorithm is called bias-correction fuzzy weighted C-ordered-means (BFWCOM) clustering algorithm. It can overcome the shortcomings of the existing FCM algorithm and improve clustering performance. The primary task of BFWCOM is the use of fuzzy local similarity measures (space and grayscale). Meanwhile, this new algorithm adds a typical analysis of data attributes to membership, in order to ensure noise insensitivity and the preservation of image details. Secondly, the local convergence of the proposed algorithm is mathematically proved, providing a theoretical preparation for fuzzy classification. Finally, data classification and real image experiments show the effectiveness of BFWCOM clustering algorithm, having a strong denoising and robust effect on noise images.

Suggested Citation

  • Wenyuan Zhang & Tianyu Huang & Jun Chen, 2019. "A Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-17, June.
    • Handle: RePEc:hin:jnlmpe:5984649
    DOI: 10.1155/2019/5984649
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