EnvSOM: A SOM Algorithm Conditioned on the Environment for Clustering and Visualization

  • Serafín Alonso
  • Mika Sulkava
  • Miguel Angel Prada
  • Manuel Domínguez
  • Jaakko Hollmén
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6731)


In this paper, we present a new approach suitable for analysis of large data sets, conditioned on the environment. Mainly, the envSOM algorithm consists of two consecutive trainings of the self-organizing map. In the first phase, a SOM is trained using every available variable, but only those which characterize the environment are used to compute the winner unit. Therefore, this phase produces an accurate model of the environment. In the second phase, a new SOM is initialized appropriately with information from the codebooks of the first SOM. The new SOM uses all the variables for winner selection. However, in this case the environmental variables are kept fixed and only the remaining ones are involved in the update process. A model of the whole data set influenced by the environmental conditions is obtained in this second phase. The result of this algorithm represents a probability function of a data set, given the environment information. Therefore, it could be very useful in the analysis of processes which have close dependencies on environmental conditions.


Self-organizing maps variants of SOM environmental conditions envSOM data mining pattern recognition 


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  1. 1.
    Kangas, J., Kohonen, T., Laaksonen, J.: Variants of self-organizing maps. IEEE Transactions on Neural Networks 1, 93–99 (1990)CrossRefGoogle Scholar
  2. 2.
    Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)CrossRefzbMATHGoogle Scholar
  3. 3.
    Hagenbuchner, M., Tsoi, A.C.: A supervised training algorithm for self-organizing maps for structures. Pattern Recognition Letters 26, 1874–1884 (2005)CrossRefGoogle Scholar
  4. 4.
    Melssen, W., Wehrens, R., Buydens, L.: Supervised Kohonen networks for classification problems. Chemometrics and Intelligent Laboratory Systems 83, 99–113 (2006)CrossRefGoogle Scholar
  5. 5.
    Koikkalainen, P., Oja, E.: Self-organizing hierarchical feature maps. In: International Joint Conference on Neural Networks, vol. 2, pp. 279–284. IEEE, INNS (1990)Google Scholar
  6. 6.
    Koikkalainen, P.: Progress with the tree-structured self-organizing map. In: Cohn, A.G. (ed.) 11th European Conference on Artificial Intelligence, ECCAI (1994)Google Scholar
  7. 7.
    Laaksonen, J., Koskela, M., Laakso, S., Oja, E.: PicSOM - content-based image retrieval with self-organizing maps. Pattern Recognition Letters 21, 1199–1207 (2000)CrossRefzbMATHGoogle Scholar
  8. 8.
    Rauber, A., Merkl, D., Dittenbach, M.: The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data. IEEE Transactions on Neural Networks 13(6), 1331–1341 (2002)CrossRefzbMATHGoogle Scholar
  9. 9.
    Hammer, B., Micheli, A., Sperduti, A., Strickert, M.: Recursive self-organizing network models. Neural Networks 17, 1061–1085 (2004)CrossRefzbMATHGoogle Scholar
  10. 10.
    Guimarães, G., Sousa-Lobo, V., Moura-Pires, F.: A taxonomy of self-organizing maps for temporal sequence processing. Intelligent Data Analysis (4), 269–290 (2003)Google Scholar
  11. 11.
    Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM toolbox for Matlab 5 (2000)Google Scholar
  12. 12.
    Abramowitz, G., Leuning, R., Clark, M., Pitman, A.: Evaluating the performance of land surface models. Journal of Climate 21(21), 5468–5481 (2008)CrossRefGoogle Scholar
  13. 13.
    Luyssaert, S., Janssens, I.A., Sulkava, M., Papale, D., Dolman, A.J., Reichstein, M., Hollmén, J., Martin, J.G., Suni, T., Vesala, T., Loustau, D., Law, B.E., Moors, E.J.: Photosynthesis drives anomalies in net carbon-exchange of pine forests at different latitudes. Global Change Biology 13(10), 2110–2127 (2007)CrossRefGoogle Scholar
  14. 14.
    Zaehle, S., Friend, A.D.: Carbon and nitrogen cycle dynamics in the o-cn land surface model: 1. model description, site-scale evaluation, and sensitivity to parameter estimates. Global Biogeochemical Cycles 24 (February 2010)Google Scholar
  15. 15.
    Zaehle, S., Friend, A.D., Friedlingstein, P., Dentener, F., Peylin, P., Schulz, M.: Carbon and nitrogen cycle dynamics in the o-cn land surface model: 2. role of the nitrogen cycle in the historical terrestrial carbon balance. Global Biogeochemical Cycles 24 (February 2010)Google Scholar
  16. 16.
    Krinner, G., Viovy, N., de Noblet-Ducoudre, N., Ogee, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., Prentice, I.C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochemical Cycles 19 (February 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Serafín Alonso
    • 1
  • Mika Sulkava
    • 2
  • Miguel Angel Prada
    • 2
  • Manuel Domínguez
    • 1
  • Jaakko Hollmén
    • 2
  1. 1.Grupo de Investigación SUPPRESSUniversidad de LeónLeónSpain
  2. 2.Department of Information and Computer ScienceAalto University School of ScienceEspooFinland

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