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    Area of Science:

    • Data Science
    • Machine Learning
    • Pattern Recognition

    Background:

    • Clustering is crucial for data analysis and interpretation in complex datasets.
    • Fuzzy clustering, particularly fuzzy C-means (FCM), effectively handles data uncertainty.
    • Improving FCM's classification performance remains an active research area.

    Purpose of the Study:

    • To develop a novel nonlinear transformation strategy to enhance FCM classification performance.
    • To restructure data for improved separability and intraclass compactness.
    • To boost the effectiveness of FCM-based classifiers.

    Main Methods:

    • Partitioning datasets into subsets based on original labels with feature weighting.
    • Constructing nonlinear transformation models using Support Vector Regression (SVR).
    • Optimizing weights with Particle Swarm Optimization (PSO) to enhance intraclass similarity.

    Main Results:

    • The proposed nonlinear transformation significantly enhances data separability.
    • Intraclass compactness is improved by amplifying similarity among samples within the same class.
    • Classification accuracy improved by an average of 16.029% and up to 57.365% compared to standard FCM.

    Conclusions:

    • The novel nonlinear transformation strategy effectively improves FCM classification performance.
    • The method demonstrates feasibility and effectiveness on public datasets.
    • This approach offers a promising advancement for fuzzy clustering applications.