popsom7:A Fast, User-Friendly Implementation of Self-Organizing Maps
(SOMs)
Methods for building self-organizing maps (SOMs) with a number of distinguishing features such automatic centroid
detection and cluster visualization using starbursts. For more
details see the paper "Improved Interpretability of the Unified
Distance Matrix with Connected Components" by Hamel and Brown
(2011) in <ISBN:1-60132-168-6>. The package provides
user-friendly access to two models we construct: (a) a SOM
model and (b) a centroid based clustering model. The package
also exposes a number of quality metrics for the quantitative
evaluation of the map, Hamel (2016)
<doi:10.1007/978-3-319-28518-4_4>. Finally, we reintroduced
our fast, vectorized training algorithm for SOM with
substantial improvements. It is about an order of magnitude
faster than the canonical, stochastic C implementation
<doi:10.1007/978-3-030-01057-7_60>.