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).

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 Letrémy, 2005a) 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).

In addition, the numeric version of the algorithm handles missing values: missing entries are not used during training but the resulting map can be used to fill missing entries (using the entry of the corresponding prototype). The method is taken from Cottrell and Letrémy (2005b).


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

Cottrell M., Letrémy P. (2005b) Missing values: processing with the Kohonen algorithm. Proceedings of Applied Stochastic Models and Data Analysis (ASMDA 2005), 489-496.

Letrémy P. (2005) Programmes basés sur l'algorithme de Kohonen et dediés à l'analyse des données. SAS/IML programs for 'korresp'.

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.

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.


Nathalie Vialaneix
Élise Maigné
Jérome Mariette
Madalina Olteanu
Fabrice Rossi
Laura Bendhaïba
Julien Boelaert

Maintainer: Nathalie Vialaneix