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    Multidimensional projections (MDP) reduce data dimensions for visualization but introduce distortions. This survey analyzes MDP techniques, their distortions, and layout enrichment methods to improve visual analytics.

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    Area of Science:

    • Computer Science
    • Information Visualization
    • Human-Computer Interaction

    Background:

    • Multidimensional data visualization is crucial for data analysis.
    • Multidimensional projections (MDP) reduce data dimensionality for visual representation.
    • Existing MDP techniques often introduce distortions, limiting data interpretation.

    Purpose of the Study:

    • To provide a comprehensive survey of Multidimensional Projection (MDP) techniques.
    • To analyze the properties, distortions, and evaluation of MDPs.
    • To review layout enrichment methods for enhancing MDP visualizations.

    Main Methods:

    • Categorization and analysis of MDP techniques based on properties and traits.
    • Examination of distortion types and quantitative evaluation mechanisms.
    • Qualitative analysis of distortion impact on user analytic tasks.
    • Review of layout enrichment schemes to mitigate distortions.

    Main Results:

    • MDPs transform high-dimensional data into scatter plots, but distortions can mislead users.
    • Various MDP techniques possess distinct properties affecting visual perception and analytic tasks.
    • Quantitative and qualitative analyses reveal the impact of distortions on data interpretation.
    • Layout enrichment methods can help overcome MDP limitations.

    Conclusions:

    • A structured understanding of MDPs, their distortions, and enrichment techniques is essential for effective visual analytics.
    • Guidelines for selecting appropriate MDPs and enrichment strategies are provided.
    • Future research should address identified gaps in MDP and visual analytics.