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 |
Documentation:
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