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Miyamoto, S., et al. (2007) Data Clustering Algorithms for Information Systems. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W. and Wang, G., Eds., Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Springer, 13-24.
https://doi.org/10.1007/978-3-540-72530-5_2

has been cited by the following article:

• TITLE: Pseudo-Semi-Overlap Functions-Based Fuzzy Rough Sets Applied to Image Edge Extraction

  AUTHORS: Ran Yin, Minge Chen, Yu Liu, Yafei Zhao, Jianwei Li

  KEYWORDS: Pseudo-Semi-Overlap Functions, Fuzzy Rough Set, Fuzzy Mathematical Morphology, Image Edge Extraction

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.12 No.7, July 12, 2024

  ABSTRACT: As an extension of overlap functions, pseudo-semi-overlap functions are a crucial class of aggregation functions. Therefore, (I, PSO)-fuzzy rough sets are introduced, utilizing pseudo-semi-overlap functions, and further extended for applications in image edge extraction. Firstly, a new clustering function, the pseudo-semi-overlap function, is introduced by eliminating the symmetry and right continuity present in the overlap function. The relaxed nature of this function enhances its applicability in image edge extraction. Secondly, the definitions of (I, PSO)-fuzzy rough sets are provided, using (I, PSO)-fuzzy rough sets, a pair of new fuzzy mathematical morphological operators (IPSOFMM operators) is proposed. Finally, by combining the fuzzy C-means algorithm and IPSOFMM operators, a novel image edge extraction algorithm (FCM-IPSO algorithm) is proposed and implemented. Compared to existing algorithms, the FCM-IPSO algorithm exhibits more image edges and a 73.81% decrease in the noise introduction rate. The outstanding performance of (I, PSO)-fuzzy rough sets in image edge extraction demonstrates their practical application value.