A Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm. (18th June 2019)

• Record Type: Journal Article
• Authors: Zhang, Wenyuan; Huang, Tianyu; Chen, Jun
• Other Names: Llamazares Bonifacio Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2019(2019)
• Publication Date: 2019-06-18
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2019/5984649
• Languages: English
• ISSNs: 1024-123X

Abstract

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.