A Very Efficient Implementation of Kohonen's Self-Organizing Maps (SOMs) with Starburst Visualizations

Kohonen's self-organizing maps with a number of distinguishing features: (1) A very efficient, single threaded, stochastic training algorithm based on ideas from tensor algebra. Up to 60x faster than traditional single-threaded training algorithms. No special accelerator hardware required. (2) Automatic centroid detection and visualization using starbursts. (3) Two models of the data: (a) a self-organizing map model, (b) a centroid based clustering model. (4) A number of easily accessible quality metrics for the self-organizing map and the centroid based cluster model.

R package for self-organizing maps contains state of the art learning algorithms, visualizations, and evaluation functions.


Release 4.2

  • supports the Kolgomorov-Smirnov convergence test.
  • supports merging of clusters on the map that are close by
  • supports marginal distribution plots

Reference manual

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5.2 by Lutz Hamel, 3 months ago


Browse source code at https://github.com/cran/popsom

Authors: Lutz Hamel [aut, cre] , Benjamin Ott [aut] , Gregory Breard [aut] , Robert Tatoian [aut] , Michael Eiger [aut] , Vishakh Gopu [aut]

Documentation:   PDF Manual  

GPL license

Imports fields, graphics, ggplot2, hash, stats, grDevices

See at CRAN