SOMbrero implements the stochastic (also called on-line) Self-Organising Map (SOM) algorithm for numeric and relational data. Data are clustered into a 2-dimensional squared grid that can be initialized with initGrid. The main function is trainSOM that implements three types of algorithms:
the standard numeric SOM as in (Kohonen, 2001)
the relational SOM designed to deal with datasets described by a dissimilarity as in (Olteanu & Villa-Vialaneix, 2015) and (Mariette et al., 2017)
the KORRESP method that deals with contingency tables as in (Cottrell et al, 2004) and (Cottrell et al, 2005)
Results can be displayed with the function plot.somRes and quality criteria are provided by quality. Finally, a super-clustering can be computed with superClass.
Kohonen T. (2001) Self-Organizing Maps. Berlin/Heidelberg: Springer-Verlag, 3rd edition.
Cottrell M., Ibbou S., Letremy P. (2004) SOM-based algorithms for qualitative variables. Neural Networks, 17, 1149-1167.
Cottrell M., Letremy P. (2005) How to use the Kohonen algorithm to simultaneously analyse individuals in a survey. Neurocomputing, 21, 119-138.
Mariette J., Rossi F., Olteanu M., Mariette J. (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.
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.