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The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
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    This study introduces new methods for incremental fuzzy data stream clustering, enabling models to adapt to continuous data. The novel approach improves cluster comparison and fusion, outperforming existing algorithms.

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    Area of Science:

    • Machine Learning
    • Data Mining
    • Artificial Intelligence

    Background:

    • Data stream (DS) learning requires incremental model updates due to high-volume, continuous data.
    • Fuzzy DS clustering involves absorbing data into existing clusters or creating new ones.
    • Overlapping clusters necessitate incremental merging strategies.

    Purpose of the Study:

    • To formalize and operationalize incremental fuzzy cluster comparison for data stream learning.
    • To develop robust cluster comparison measures (CMs) for incremental fusion processes.
    • To enhance the performance of fuzzy data stream clustering algorithms.

    Main Methods:

    • Introduced recursively extendable (RE) aggregation functions for incremental fusion.
    • Proposed two cluster comparison approaches: similarity and overlapping, based on RE functions.
    • Integrated incremental CMs into the d-FuzzStream algorithm for analysis.

    Main Results:

    • Demonstrated effective incremental comparison and fusion of fuzzy clusters.
    • The proposed RE aggregation functions enable efficient online processing.
    • The enhanced d-FuzzStream algorithm showed improved performance.

    Conclusions:

    • The novel incremental cluster comparison methods provide a formal basis for fuzzy DS clustering.
    • The new approach enhances the adaptability and accuracy of data stream clustering.
    • This work offers a significant advancement over existing state-of-the-art DS clustering algorithms.