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    The novel granular weighted kernel fuzzy clustering (GWKFC) algorithm improves data representation and structural characteristics. Experiments show GWKFC outperforms existing methods in granular clustering, especially for complex datasets.

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    Area of Science:

    • Data Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Current granular clustering methods lack effective strategies for selecting numeric representatives and assigning weights.
    • Existing approaches fail to adequately capture the structural characteristics of granular data.

    Purpose of the Study:

    • To introduce a new granular weighted kernel fuzzy clustering (GWKFC) algorithm to address limitations in existing granular clustering techniques.
    • To enhance the representation and structural characterization of granular data for improved clustering performance.

    Main Methods:

    • Developed the representative selection and granularity generation (RSGG) algorithm, inspired by density peak clustering (DPC), for selecting numeric representatives.
    • Constructed interval and triangular granular data using the RSGG algorithm and the principle of justifiable granularity (PJG).
    • Introduced a novel kernel function-based distance formula and new weights for granular data, leading to the GWKFC algorithm.

    Main Results:

    • The GWKFC algorithm demonstrated superior granular clustering results compared to ten other algorithms across various datasets, including artificial, UCI, large, and high-dimensional data.
    • The RSGG algorithm provides improved numeric representatives, and the new weighting strategy enhances granular data specificity and coverage.
    • The kernel distance formula exhibits stronger spatial division capabilities than traditional Euclidean distance.

    Conclusions:

    • The proposed GWKFC algorithm offers a significant advancement in granular clustering by effectively handling representative selection, granularity, and distance calculation.
    • The study establishes a comprehensive framework for granular modeling, clustering, and assessment.
    • The GWKFC algorithm shows strong potential for applications requiring robust and accurate granular data analysis.