R: Supervised and Unsupervised Self-Organising Maps

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
    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).

[Package kohonen version 3.0.7 Index]