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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Kohonen, T.: Self-Organizing Maps, 3rd edn., vol. 30. Springer, Heidelberg (2001)
Cottrell, M., Letremy, P., Roy, E.: Analyzing a contingency table with Kohonen maps: a factorial correspondence analysis. In: Mira, J., Cabestany, J., Prieto, A.G. (eds.) IWANN 1993. LNCS, vol. 686, pp. 305–311. Springer, Heidelberg (1993)
Kohohen, T., Somervuo, P.: Self-organizing maps of symbol strings. Neurocomputing 21, 19–30 (1998)
Mac Donald, D., Fyfe, C.: The kernel self organising map. In: Proceedings of 4th International Conference on Knowledge-Based Intelligence Engineering Systems and Applied Technologies, pp. 317–320 (2000)
Andras, P.: Kernel-Kohonen networks. International Journal of Neural Systems 12, 117–135 (2002)
Villa, N., Rossi, F.: A comparison between dissimilarity SOM and kernel SOM for clustering the vertices of a graph. In: 6th International Workshop on Self-Organizing Maps (WSOM), Bielefield, Germany, Neuroinformatics Group, Bielefield University (2007)
Hammer, B., Hasenfuss, A.: Topographic mapping of large dissimilarity data sets no access. Neural Computation 22(9), 2229–2284 (2010)
Olteanu, M., Villa-Vialaneix, N.: On-line relational and multiple relational som. Neurocomputing (forthcoming, 2014)
Olteanu, M., Villa-Vialaneix, N., Cierco-Ayrolles, C.: Multiple kernel self-organizing maps. In: Verleysen, M. (ed.) XXIst European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, pp. 83–88. d-side publications (2013)
Kohonen, T., Hynninen, J., Kangas, J., Laaksonen, J.: Som_pak: The self-organizing map program package. Technical Report A31, Helsinki University of Technology, Laboratory of Computer and Information Science (1996)
Yan, J.: som: Self-Organizing Map. R package version 0.3-5 (2010)
Hamel, L., Ott, B., Breard, G.: popsom: Self-Organizing Maps With Population Based Convergence Criterion. R package version 2.3 (2013)
Wehrens, R., Buydens, L.: Self- and super-organising maps in r: the kohonen package. J. Stat. Softw. 21(5) (2007)
Rossi, F.: yasomi: Yet Another Self Organising Map Implementation. R package version 0.3/r39 (2012)
Ritter, H., Martinetz, T., Shulten, K.: Neural computation and Self-Organizing Maps, an Introduction. Addison-Wesley (1992)
Fort, J., Letremy, P., Cottrell, M.: Advantages and drawbacks of the batch kohonen algorithm. In: Verleysen, M. (ed.) Proceedings of 10th European Symposium on Artificial Neural Networks (ESANN 2002), Bruges, Belgium, pp. 223–230 (2002)
Cottrell, M., de Bodt, E.: A Kohonen map representations to avoid misleading interpretations. In: Verleysen, M. (ed.) Proceedings of ESANN 1996, D Facto, Bruxelles, pp. 103–110 (1996)
Ultsch, A., Siemon, H.: Kohonen’s self organizing feature maps for exploratory data analysis. In: Proceedings of International Neural Network Conference, INNC 1990 (1990)
Vesanto, J.: Data Exploration Process Based on the Self–Organizing Map. PhD thesis, Helsinki University of Technology, Espoo (Finland), Acta Polytechnica Scandinavica, Mathematics and Computing Series No.115 (2002)
Polzlbauer, G.: Survey and comparison of quality measures for self-organizing maps. In: Paralic, J., Polzlbauer, G., Rauber, A. (eds.) Proceedings of the Fifth Workshop on Data Analysis (WDA 2004), Sliezsky dom, Vysoke Tatry, Slovakia, pp. 67–82. Elfa Academic Press (2004)
RStudio, Inc.: shiny: Web Application Framework for R. R package version 0.6.0 (2013)
Becker, R., Chambers, J., Wilks, A.: The New S Language. Wadsworth & Brooks/Cole (1988)
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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
Publisher Name: Springer, Cham
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