popsom: An Efficient Implementation of Kohonen's Self-Organizing Maps
(SOMs) with Starburst Visualizations
Kohonen's self-organizing maps with a number of distinguishing features:
(1) An efficient, single threaded, stochastic training algorithm inspired by ideas from tensor algebra. Provides significant speedups over traditional single-threaded training algorithms. No special accelerator hardware required (see <doi:10.1007/978-3-030-01057-7_60>).
(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 (see <doi:10.1007/978-3-319-28518-4_4>).
| Version: |
6.0 |
| Imports: |
fields, graphics, ggplot2, hash, stats, grDevices |
| Suggests: |
testthat (≥ 3.0.0) |
| Published: |
2021-12-20 |
| 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> |
| BugReports: |
https://github.com/lutzhamel/popsom/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/lutzhamel/popsom |
| NeedsCompilation: |
yes |
| Materials: |
NEWS |
| CRAN checks: |
popsom results |
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