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Automatic Selection of Partitioning Variables for Small Multiple Displays.

Anushka Anand, Justin Talbot

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    This summary is machine-generated.

    This study introduces an automated method to rank variables for creating effective small multiple displays. It helps data analysts quickly identify the most insightful visualizations from complex datasets.

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    Area of Science:

    • Data Visualization
    • Computer Science
    • Statistics

    Background:

    • Small multiple displays are effective for visualizing conditional data structures.
    • Identifying optimal partitioning variables for small multiples is challenging.
    • Manual selection of variables can be time-consuming and subjective.

    Purpose of the Study:

    • To propose an automated method for ranking variables to create effective small multiple displays.
    • To enable data analysts to focus on the most promising small multiple visualizations.
    • To handle diverse quality measures and visualization types.

    Main Methods:

    • A randomized, non-parametric permutation test is employed.
    • The method ranks partitioning variables based on their ability to reveal conditional structure.
    • It is designed to discount spurious patterns in the data.

    Main Results:

    • The proposed method effectively ranks partitioning variables for small multiple displays.
    • Demonstrated effectiveness on scatterplots of real-world, multidimensional datasets.
    • Successfully handles various quality measures and visualization types.

    Conclusions:

    • Automated variable ranking enhances the creation of informative small multiple displays.
    • The permutation test approach is robust and versatile.
    • Facilitates more efficient data exploration and insight discovery.