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    Area of Science:

    • Data Visualization
    • Statistical Computing
    • Pattern Recognition

    Background:

    • Scagnostics (Scatterplot Diagnostics) were developed to analyze large scatterplot matrices.
    • The Tukeys' original concept aimed to simplify the examination of numerous plots and enhance detail.
    • Wilkinson's implementation allowed scagnostics computations on extensive datasets and numerous plots.

    Purpose of the Study:

    • To investigate the sensitivity of scagnostics to scale transformations.
    • To demonstrate how statistical transformations can enhance scagnostics' pattern discovery capabilities.
    • To reveal hidden data structures obscured by untransformed visualizations.

    Main Methods:

    • Illustrating the sensitivity of scagnostics to scale transformations.
    • Applying statistical transformations in conjunction with scagnostics.
    • Analyzing data to identify structures not apparent in standard scatterplots.

    Main Results:

    • Scagnostics are significantly affected by scale transformations.
    • Combining statistical transformations with scagnostics uncovers previously hidden data structures.
    • This approach improves the interpretability of complex datasets.

    Conclusions:

    • Statistical transformations are crucial for maximizing the utility of scagnostics.
    • The proposed method enhances the discovery of hidden patterns in data visualization.
    • This technique offers a valuable tool for exploratory data analysis.