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    This study introduces the Minimum Description Length (MDL) principle for selecting aggregated views in hierarchical data visualizations. MDL-based methods reduce visual clutter and improve target identification, especially for outliers.

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    Area of Science:

    • Information Visualization
    • Data Analysis
    • Human-Computer Interaction

    Background:

    • Hierarchical datasets often suffer from visual clutter in aggregated views.
    • Selecting optimal aggregated views is crucial for effective data exploration.
    • Existing methods may not adequately balance information content with display space.

    Purpose of the Study:

    • To apply the Minimum Description Length (MDL) principle for selecting aggregated views of hierarchical datasets.
    • To develop MDL formulae for generating uneven tree cuts suitable for treemaps and sunburst diagrams.
    • To enhance drill-down interactions in hierarchical visualizations.

    Main Methods:

    • Developed MDL formulae for generating uneven tree cuts considering display space and information content.
    • Implemented a proof-of-concept for treemap and sunburst visualizations.
    • Integrated the approach into an existing visualization tool to enhance drill-down interaction.
    • Validated using the feature congestion measure for clutter on a large web directory dataset (DMOZ).

    Main Results:

    • MDL-based aggregated views maintain a near-constant clutter level across different display resolutions.
    • Crowdsourced user study indicated faster target location, particularly for outliers, using MDL-generated views compared to baseline methods.
    • The approach effectively balances information and visual clutter in hierarchical data representations.

    Conclusions:

    • The Minimum Description Length principle offers an efficient method for generating informative and less cluttered aggregated views of hierarchical data.
    • MDL-enhanced visualizations improve user performance in target acquisition tasks within complex datasets.
    • This approach has practical implications for interactive data exploration tools and large-scale directory navigation.