High-dimensional Scatterplot Matrices

High-Dimensional Scatterplot Matrices is a visualization framework that extends traditional scatterplot matrices to enable the interactive exploration and analysis of pairwise relationships among variables in high-dimensional datasets by systematically arranging bivariate scatterplots in a grid structure while incorporating dimensionality reduction, variable ordering, and scalability techniques to mitigate visual clutter and enhance interpretability.

18

Publications

1.1K

Citations

47

Authors

29

Institutions

Publications per year

2017–2026

3

Authors

47

  1. MS
    Michael Sedlmair

    University of Stuttgart, Germany

    2 publications · 204 citations

    2

    h-index

  2. MM
    Marcus Magnor

    Technische Universität Braunschweig, Germany

    2 publications · 110 citations

    2

    h-index

  3. EA
    Elke A. Rundensteiner

    Worcester Polytechnic Institute, United States

    2 publications · 77 citations

    2

    h-index

  4. ME
    Martin Eisemann

    Technische Universität Braunschweig, Germany

    2 publications · 110 citations

    2

    h-index

  5. GA
    Geórgia Albuquerque

    Technische Universität Braunschweig, Germany

    2 publications · 110 citations

    2

    h-index

1–5 of 47

Venues

  1. Computer Graphics Forum

    2 publications · 69 citations

    2

    h-index

  2. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 2 publications · 217 citations

    2

    h-index

  3. The American Statistician

    2 publications · 232 citations

    2

    h-index

  4. Scientific Visualization

    1 publications · 11 citations

    1

    h-index

  5. IEEE Symposium on Information Visualization

    Conference · 1 publications · 30 citations

    1

    h-index

1–5