Dissimilarity Matrix Visualization

Dissimilarity Matrix Visualization is a visualization technique that encodes pairwise dissimilarities among objects in a multidimensional space into a two-dimensional matrix representation, distinguished by its explicit spatial arrangement of entries to reveal structural patterns such as clusters, gradients, or outliers in the underlying dissimilarity data.

18

Publications

1.5K

Citations

39

Authors

24

Institutions

Publications per year

2017–2026

2

Authors

39

  1. JC
    James C. Bezdek

    University of West Florida, United States

    4 publications · 515 citations

    4

    h-index

  2. VS
    Victor S. Lobanov

    University of Florida, United States

    2 publications · 105 citations

    2

    h-index

  3. DN
    Dmitrii N. Rassokhin

    Johnson & Johnson (United States), United States

    2 publications · 105 citations

    2

    h-index

  4. DK
    Dimitris K. Agrafiotis

    University of Bristol, United Kingdom

    2 publications · 105 citations

    2

    h-index

  5. RJ
    Richard J. Hathaway

    Georgia Southern University, United States

    2 publications · 427 citations

    2

    h-index

1–5 of 39

Venues

  1. Journal of Computational and Graphical Statistics

    JCGS · 2 publications · 327 citations

    2

    h-index

  2. Journal of Computational Chemistry

    2 publications · 105 citations

    2

    h-index

  3. Scandinavian Journal of Psychology

    1 publications · 27 citations

    1

    h-index

  4. Clinical Trials

    1 publications · 26 citations

    1

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 45 citations

    1

    h-index

1–5