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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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    Area of Science:

    • Data visualization
    • Machine learning
    • Computational biology

    Background:

    • Embedding visualization aids high-dimensional data interpretation.
    • Current comparison methods require direct point correspondences, limiting broader applications.
    • Existing techniques fail to capture complex relationships between point groups.

    Purpose of the Study:

    • To develop a general framework for comparing embedding visualizations using shared class labels.
    • To characterize intra- and inter-class relationships by partitioning points into confusion, neighborhood, and relative size regions.
    • To enable meaningful comparisons across different datasets and label hierarchies.

    Main Methods:

    • Developed a framework partitioning points into class-based regions (confusion, neighborhood, relative size).
    • Utilized perceptual neighborhood graphs to define these regions.
    • Introduced quantitative metrics to characterize intra- and inter-class relationships.

    Main Results:

    • Demonstrated framework generality in machine learning and single-cell biology use cases.
    • Highlighted metrics' ability to provide insightful comparisons across label hierarchies.
    • Evaluation study showed increased participant confidence and structured comparisons.

    Conclusions:

    • The proposed framework offers a robust method for comparing embedding visualizations without point correspondences.
    • The class-label-based approach enhances understanding of complex data relationships.
    • This method improves decision-making and interpretation in diverse scientific domains.