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    This study introduces Ant Colony Stream Clustering (ACSC), an online, bio-inspired algorithm for clustering dynamic data streams. ACSC efficiently identifies and refines clusters in real-time, offering a scalable and robust solution for noisy data streams.

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    Area of Science:

    • Data Science
    • Artificial Intelligence
    • Bio-inspired Computing

    Background:

    • Clustering dynamic data streams presents challenges due to unbounded data, online arrival, single-pass examination, limited memory, and evolving cluster properties.
    • Traditional clustering methods are often unsuitable for the constraints and characteristics of data streams.

    Purpose of the Study:

    • To propose and evaluate an online, bio-inspired algorithm for clustering dynamic data streams.
    • To address limitations of memory, processing time, noise, and changing statistical properties in data stream clustering.

    Main Methods:

    • The Ant Colony Stream Clustering (ACSC) algorithm, a density-based approach, identifies clusters as high-density areas using micro-clusters.
    • A tumbling window model processes the stream, forming rough clusters via a stochastic method for speed.
    • Artificial ants refine clusters by probabilistically moving micro-clusters based on local density and similarity, inspired by ant sorting behavior.

    Main Results:

    • ACSC demonstrates scalable clustering quality, robustness to noise, and favorable performance compared to leading algorithms.
    • The algorithm requires fewer parameters and less computational time than existing methods.
    • Summary statistics of clusters are stored offline using constituent micro-clusters.

    Conclusions:

    • ACSC provides an effective and efficient bio-inspired solution for clustering dynamic data streams.
    • The algorithm's stochastic and ant-inspired refinement methods contribute to its speed and performance.
    • ACSC is a promising approach for real-time analysis of evolving, noisy data streams.