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    Local-to-Global Correlation Clustering (LoGCC) speeds up analysis of scalar fields by creating local clusters then merging them globally. This framework enhances correlation clustering for large datasets and interactive use.

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    Area of Science:

    • Data analysis
    • Computational science
    • Scientific visualization

    Background:

    • Correlation clustering (CC) effectively analyzes scalar fields but is computationally intensive.
    • Existing CC methods struggle with large datasets and interactive analysis due to high computational cost.

    Purpose of the Study:

    • Introduce the Local-to-Global Correlation Clustering (LoGCC) framework to accelerate pivot-based CC.
    • Leverage spatial structure and correlation transitivity for efficient clustering.
    • Enhance scalability and applicability of CC for large-scale and interactive analysis.

    Main Methods:

    • Developed a two-stage LoGCC framework: a local step using neighborhood graphs and a global step for merging clusters.
    • Implemented LoGCC with Pivot and CN-Pivot methods to demonstrate generality.
    • Evaluated performance on synthetic, meteorological, and medical image segmentation datasets.

    Main Results:

    • LoGCC achieved significant speedups: up to 15× for Pivot and 200× for CN-Pivot.
    • Demonstrated improved scalability for analyzing larger scalar fields.
    • Maintained high cluster quality comparable to existing methods.

    Conclusions:

    • LoGCC framework substantially accelerates correlation clustering for scalar fields.
    • The method enhances scalability, making CC practical for large-scale and interactive applications.
    • LoGCC broadens the utility of correlation clustering across scientific domains.