popsom: 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.

Version: 5.2
Imports: fields, graphics, ggplot2, hash, stats, grDevices
Published: 2021-07-09
Author: Lutz Hamel [aut, cre], Benjamin Ott [aut], Gregory Breard [aut], Robert Tatoian [aut], Michael Eiger [aut], Vishakh Gopu [aut]
Maintainer: Lutz Hamel <lutzhamel at uri.edu>
License: GPL-2 | GPL-3 [expanded from: GPL]
URL: https://github.com/lutzhamel/popsom
NeedsCompilation: yes
Materials: NEWS
CRAN checks: popsom results

Downloads:

Reference manual: popsom.pdf
Package source: popsom_5.2.tar.gz
Windows binaries: r-devel: popsom_5.2.zip, r-devel-UCRT: popsom_5.2.zip, r-release: popsom_5.2.zip, r-oldrel: popsom_5.2.zip
macOS binaries: r-release (arm64): popsom_5.2.tgz, r-release (x86_64): popsom_5.2.tgz, r-oldrel: popsom_5.2.tgz
Old sources: popsom archive

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