kohonen-package {kohonen} | R Documentation |
Supervised and Unsupervised Self-Organising Maps
Description
Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.
Details
The kohonen package implements several forms of self-organising maps
(SOMs). Online and batch training algorithms are available; batch
training can also be done in parallel. Multiple data layers may be
presented to the training algorithm, with potentially different distance
measures for each layer. The overall distance is a weighted average of
the layer distances. Layers may be selected through the whatmap
argument, or by providing a weight of zero. The basic function is
supersom
; som
is simply a wrapper for SOMs using just one
layer (the classical form).
New data may be mapped to a trained SOM using the map.kohonen
function. Function predict.kohonen
will map data to the SOM, and
will return predictions (i.e., average values for winning units) for
those layers that are not in the new data object.
Several visualisation methods are available in function
plot.kohonen
.
Index of help topics:
check.whatmap Check the validity of a whatmap argument classvec2classmat Convert a classification vector into a matrix or the other way around. degelder Powder pattern data by Rene de Gelder expandMap Expand a self-organising map getCodes Extract codebook vectors from a kohonen object kohonen-package Supervised and Unsupervised Self-Organising Maps layer.distances Assessing distances to winning units map.kohonen Map data to a supervised or unsupervised SOM nir Near-infrared data with temperature effects object.distances Calculate distances between object vectors in a SOM peppaPic Synthetic image of a pepper plant with peppers plot.kohonen Plot kohonen object predict.kohonen Predict properties using a trained Kohonen map summary.kohonen Summary and print methods for kohonen objects supersom Self- and super-organising maps tricolor Provides smooth unit colors for SOMs unit.distances SOM-grid related functions wines Wine data yeast Yeast cell-cycle data
Author(s)
Ron Wehrens and Johannes Kruisselbrink
Maintainer: Ron Wehrens <ron.wehrens@gmail.com>
References
R. Wehrens and J. Kruisselbrink: Flexible Self-Organising Maps in kohonen 3.0. Journal of Statistical Software, 87, 7 (2018).