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    Area of Science:

    • Computer Graphics
    • Data Visualization
    • Applied Mathematics

    Background:

    • Curve bundling methods reduce visual clutter in datasets like graph drawings and trajectory data.
    • Existing techniques often struggle with complex, high-dimensional curve sets.

    Purpose of the Study:

    • To develop a new, statistically-controlled curve bundling technique.
    • To enable data modification through a simplified, bundled representation.

    Main Methods:

    • Functional decomposition of datasets into piecewise-polynomial basis functions.
    • Representation of curves using centroid curves and principal component functions.
    • Development of a bundling method based on cluster centroids and centroid deformation.

    Main Results:

    • A novel bundling method for 2D and 3D curve sets.
    • The ability to modify underlying data via the bundled view.
    • Successful application to graph bundling, trajectory analysis, and field visualization.

    Conclusions:

    • Functional decomposition offers a robust framework for curve bundling.
    • The proposed method enhances pattern discovery and controlled data manipulation.
    • This technique is versatile across various visualization domains.