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    This study introduces an incremental local distribution-based clustering algorithm with Bayesian adaptive resonance theory (ILBART) to address imbalanced data challenges. ILBART effectively detects smaller clusters in imbalanced datasets and dynamically adapts to evolving data relationships.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Data Mining

    Background:

    • Existing Bayesian clustering algorithms struggle with imbalanced data, favoring larger clusters and under-detecting smaller ones.
    • Imbalanced datasets pose significant challenges for traditional clustering methods, limiting their real-world applicability.

    Purpose of the Study:

    • To develop a novel clustering algorithm capable of accurately handling imbalanced datasets.
    • To create an adaptive algorithm that processes dynamic data and evolving cluster relationships without predefined parameters.

    Main Methods:

    • An incremental local distribution-based clustering algorithm with Bayesian adaptive resonance theory (ILBART) was developed.
    • The algorithm was designed to adapt to changing environments and evolving relationships among clusters autonomously.
    • Experiments were conducted using several imbalanced data sets to evaluate ILBART's performance.

    Main Results:

    • ILBART accurately identifies clusters even in severely imbalanced data distributions.
    • The algorithm efficiently processes dynamic data, adapting to evolving cluster relationships.
    • ILBART demonstrated superior performance compared to other relevant clustering algorithms across several indices.

    Conclusions:

    • ILBART offers a robust solution for clustering imbalanced data, outperforming existing methods.
    • The algorithm's adaptive nature makes it suitable for dynamic and evolving data environments.
    • ILBART provides accurate detection of smaller clusters in imbalanced datasets.