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DEC: dynamically evolving clustering and its application to structure identification of evolving fuzzy models.

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    This study introduces an online evolving clustering method for streaming data, improving model identification by using cluster weight and distance. The approach efficiently estimates cluster centers for real-time applications.

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    Area of Science:

    • Machine Learning
    • Data Mining
    • Artificial Intelligence

    Background:

    • Model identification from input-output data relies on accurate cluster center estimation.
    • Existing methods often focus on data density or distance, limiting adaptability to dynamic data streams.

    Purpose of the Study:

    • To propose a novel online evolving clustering approach for streaming data.
    • To enhance the accuracy and efficiency of cluster center estimation for dynamic data.
    • To develop evolving Takagi-Sugeno models based on the proposed clustering method.

    Main Methods:

    • Introduced an online evolving clustering approach utilizing cluster weight and distance.
    • Defined cluster weight in data and time space with exponential decay to capture stream dynamics.
    • Applied computational geometry for neighborhood information and distinguished core/non-core clusters to identify outliers.
    • Developed evolving Takagi-Sugeno models using estimated cluster centers.

    Main Results:

    • The proposed approach achieves results comparable to or better than existing methods.
    • Significantly reduced computational overhead, demonstrating suitability for real-time applications.
    • Effectively identified cluster centers and outliers in streaming data.

    Conclusions:

    • The novel online evolving clustering approach provides an efficient and effective method for model identification in streaming data.
    • The method's ability to capture data stream dynamics and reduce computational load makes it valuable for real-time systems.
    • The developed Takagi-Sugeno models show competitive performance, highlighting the robustness of the clustering technique.