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    Area of Science:

    • Data Science
    • Artificial Intelligence
    • Information Granulation

    Background:

    • Large numeric datasets pose challenges for direct analysis and structure identification.
    • Information granulation offers a method to represent complex data through aggregated information granules.
    • Existing methods may struggle with diverse data geometries and computational efficiency.

    Purpose of the Study:

    • To develop a novel two-step scheme for describing large numeric data using information granules.
    • To capture the underlying structure of complex datasets efficiently.
    • To generalize numeric prototypes into granular prototypes for enhanced data representation.

    Main Methods:

    • A two-step clustering approach combining DBSCAN and fuzzy C-means algorithms.
    • DBSCAN is used in the first phase for flexible data structure handling.
    • Fuzzy C-means is applied to formed clusters, followed by generalization into granular prototypes.
    • The principle of justifiable granularity is employed for quality quantification.

    Main Results:

    • The developed approach successfully generates representative information granules from large numeric data.
    • Experimental studies on synthetic and real-world data demonstrate the effectiveness of the proposed scheme.
    • The performance is assessed through comparative analysis, highlighting the utility of granular prototypes.

    Conclusions:

    • The proposed two-step clustering and granulation method provides an effective way to describe large numeric datasets.
    • This approach enhances data structure capture and prototype generalization.
    • The findings support the application of granular computing principles in data analysis.