Large Matrix Visualization

Large Matrix Visualization is a methodological framework within information visualization that enables the representation and analysis of high-dimensional data matrices through heatmap-based techniques, distinguished by its focus on scalability, pattern preservation, and interactive exploration of large-scale datasets.

9

Publications

3.5K

Citations

19

Authors

10

Institutions

Publications per year

2017–2026

2

Authors

19

  1. CK
    Christoph Kunz

    Fraunhofer Institute for Industrial Engineering, Germany

    2 publications · 36 citations

    2

    h-index

  2. VB
    Veit Botsch

    Fraunhofer Institute for Industrial Engineering, Germany

    2 publications · 36 citations

    2

    h-index

  3. KR
    K. R. Gabriel

    University of Rochester, United States

    2 publications · 3.2K citations

    2

    h-index

  4. AB
    Anastasia Baryshnikova

    University of Toronto, Canada

    1 publications · 15 citations

    1

    h-index

  5. EC
    E. Chow

    Queen's University, Canada

    1 publications · 160 citations

    1

    h-index

1–5 of 19

Venues

  1. Biometrika

    2 publications · 3.2K citations

    2

    h-index

  2. The American Statistician

    2 publications · 197 citations

    2

    h-index

  3. 1

    h-index

  4. Data

    1 publications · 11 citations

    1

    h-index

  5. Zenodo (CERN European Organization for Nuclear Research)

    Repository · 1 publications · 15 citations

    1

    h-index

1–5