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A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization.

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    This survey systematically reviews 88 studies on machine learning for visualizations (ML4VIS). It identifies seven key visualization processes benefiting from ML techniques, offering a structured understanding for future research.

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    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Data Visualization

    Background:

    • Machine learning (ML) has achieved significant success, prompting its application in visualization research.
    • The integration of ML into visualization (ML4VIS) is a rapidly growing field requiring structured understanding.

    Purpose of the Study:

    • To systematically survey existing ML4VIS studies.
    • To identify visualization processes amenable to ML assistance and how ML techniques address visualization challenges.

    Main Methods:

    • Systematic literature review of 88 ML4VIS studies.
    • Analysis of ML applications across seven key visualization processes.
    • Mapping ML tasks to visualization needs within an ML4VIS pipeline.

    Main Results:

    • Identified seven core visualization processes enhanced by ML: Data Processing4VIS, Data-VIS Mapping, Insight Communication, Style Imitation, VIS Interaction, VIS Reading, and User Profiling.
    • Established relationships between visualization processes and ML tasks.
    • Proposed an ML4VIS pipeline and ML-VIS mapping.

    Conclusions:

    • ML offers significant benefits across various stages of the visualization pipeline.
    • The study provides a foundational framework and identifies future research opportunities in ML4VIS.
    • An interactive browser is available to explore the survey findings.