JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2026, Vol. 61 ›› Issue (1): 49-64.doi: 10.6040/j.issn.1671-9352.4.2025.004

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Fuzzy rough c-means based on the knowledge measure

  

  1. 1. School of Science, North China University of Science and Technology, Tangshan 063210, Hebei, China;
    2. Hebei Key Laboratory of Data Science and Application, Tangshan 063210, Hebei, China;
    3. Tangshan Key Laboratory of Data Science, Tangshan 063210, Hebei, China
  • Published:2026-01-15

Abstract: A knowledge-based fuzzy rough c-means clustering method(KFRCM)is introduced. Traditional clustering methods have limitations in handling data with fuzzy boundaries, which are sensitive to the initial cluster centers, and exhibit low efficiency in high-dimensional spaces. To address these issues, the KFRCM is proposed. a feature-weighted knowledge measure is incorporated, fuzzy membership functions are integrated with rough set approximation operators, and Gaussian kernel similarity is utilized to enhance boundary characterization. Experimental results on 14 datasets demonstrate that the proposed KFRCM algorithm outperforms 6 mainstream clustering algorithms in terms of accuracy, stability, and computational efficiency. This study is recognized as the first integration of knowledge measurement with fuzzy rough clustering, offering a new perspective and an advanced algorithmic framework for developing more reliable and adaptable clustering techniques.

Key words: fuzzy rough sets, knowledge measurement, clustering analysis, gaussian kernel function, upper and lower approximation sets

CLC Number: 

  • TP391
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