Self-organizing Map Visualization

Self-Organizing Map Visualization is a dimensionality reduction and data visualization technique that projects high-dimensional data onto a low-dimensional regular grid of nodes, preserving topological relationships through competitive learning and neighborhood-based adaptation.

285

Publications

14.2K

Citations

642

Authors

311

Institutions

Publications per year

2017–2026

31

Authors

642

  1. TK
    Teuvo Kohonen

    University of Helsinki, Finland

    11 publications · 1.8K citations

    11

    h-index

  2. SK
    Samuel Kaski

    Aalto University, Finland

    10 publications · 1.3K citations

    10

    h-index

  3. AR
    Andreas Rauber

    TU Wien, Austria

    6 publications · 207 citations

    6

    h-index

  4. PS
    Peter Sarlin

    Åbo Akademi University, Finland

    6 publications · 133 citations

    6

    h-index

  5. TS
    Tobias Schreck

    University of Konstanz, Germany

    5 publications · 333 citations

    5

    h-index

1–5 of 642

Venues

  1. IEEE Transactions on Neural Networks

    10 publications · 1.3K citations

    10

    h-index

  2. Neural Networks

    4 publications · 273 citations

    4

    h-index

  3. Aaltodoc (Aalto University)

    Repository · 4 publications · 169 citations

    4

    h-index

  4. Neurocomputing

    4 publications · 558 citations

    4

    h-index

  5. Information Visualization

    3 publications · 176 citations

    3

    h-index

1–5