Abstract
This paper presents SOMbrero, a new R package for self-organizing maps. Along with the standard SOM algorithm for numeric data, it implements self-organizing maps for contingency tables (“Korresp”) and for dissimilarity data (“relational SOM”), all relying on stochastic (i.e., on-line) training. It offers many graphical outputs and diagnostic tools, and comes with a user-friendly web graphical interface, based on the shiny R package.
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Boelaert, J., Bendhaiba, L., Olteanu, M., Villa-Vialaneix, N. (2014). SOMbrero: An R Package for Numeric and Non-numeric Self-Organizing Maps. In: Villmann, T., Schleif, FM., Kaden, M., Lange, M. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-319-07695-9_21
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DOI: https://doi.org/10.1007/978-3-319-07695-9_21
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