This package implements the stochastic (also called on-line) Self-Organizing Map (SOM) algorithms for numeric and relational data.

It is based on a grid (see initGrid), which is part of the parameters given to the algorithm (see initSOM and trainSOM). Many graphs can help you with the results (see plot.somRes).

Details

Package:SOMbrero
Type:Package
Version:1.3-1
Date:2020-08-10
License:GPL (>= 2)

The version of the SOM algorithm implemented in this package is the stochastic version.

Several variants able to handle non-vectorial data are also implemented in their stochastic versions: type="korresp" for contingency tables, as described in Cottrell et al., 2004 (with the observation weights defined in Cottrell and Letremy, 2005) and type="relational" for dissimilarity data, as described in Olteanu and Villa-Vialaneix, 2015a with the fast implementation of Mariette et al., 2017. A special focus has been put on representing graphs, as described in Olteanu and Villa-Vialaneix, 2015b.

References

Kohonen T. (2001) Self-Organizing Maps. Berlin/Heidelberg: Springer-Verlag, 3rd edition.

Cottrell M., Ibbou S., Letrémy P. (2004) SOM-based algorithms for qualitative variables. Neural Networks, 17, 1149-1167.

Cottrell M., Letrémy P. (2005) How to use the Kohonen algorithm to simultaneously analyse individuals in a survey. Neurocomputing, 21, 119-138.

Letrémy P. (2005) Programmes basés sur l'algorithme de Kohonen et dediés à l'analyse des données. SAS/IML programs for 'korresp'. http://samm.univ-paris1.fr/Programmes-SAS-de-cartes-auto.

Mariette J., Rossi F., Olteanu M., Villa-Vialaneix N. (2017) Accelerating stochastic kernel SOM. In: M. Verleysen, XXVth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017), i6doc, Bruges, Belgium, 269-274.

Olteanu M., Villa-Vialaneix N. (2015a) On-line relational and multiple relational SOM. Neurocomputing, 147, 15-30.

Olteanu M., Villa-Vialaneix N. (2015b) Using SOMbrero for clustering and visualizing graphs. Journal de la Société Française de Statistique, 156, 95-119.

Rossi F. (2013) yasomi: Yet Another Self-Organising Map Implementation. R package, version 0.3. https://github.com/fabrice-rossi/yasomi

Villa-Vialaneix N. (2017) Stochastic self-organizing map variants with the R package SOMbrero. In: J.C. Lamirel, M. Cottrell, M. Olteanu, 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (Proceedings of WSOM 2017), IEEE, Nancy, France.

See also