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>).
Package details |
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Author | Lutz Hamel [aut, cre], Benjamin Ott [aut], Gregory Breard [aut], Robert Tatoian [aut], Michael Eiger [aut], Vishakh Gopu [aut] |
Maintainer | Lutz Hamel <lutzhamel@uri.edu> |
License | GPL-3 |
Version | 6.0 |
URL | https://github.com/lutzhamel/popsom |
Package repository | View on CRAN |
Installation |
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