R/SOMbrero-package.R
SOMbrero-package.RdThis 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. 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.
initGrid, trainSOM,
plot.somRes and sombreroGUI.