On-Line Relational SOM for Dissimilarity Data

  • Madalina OlteanuEmail author
  • Nathalie Villa-Vialaneix
  • Marie Cottrell
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 198)


In some applications and in order to address real world situations better, data may be more complex than simple vectors. In some examples, they can be known through their pairwise dissimilarities only. Several variants of the Self Organizing Map algorithm were introduced to generalize the original algorithm to this framework. Whereas median SOM is based on a rough representation of the prototypes, relational SOM allows representing these prototypes by a virtual combination of all elements in the data set. However, this latter approach suffers from two main drawbacks. First, its complexity can be large. Second, only a batch version of this algorithm has been studied so far and it often provides results having a bad topographic organization. In this article, an on-line version of relational SOM is described and justified. The algorithm is tested on several datasets, including categorical data and graphs, and compared with the batch version and with other SOM algorithms for non vector data.


Online Algorithm Dissimilarity Matrix Batch Algorithm Pairwise Dissimilarity Dissimilarity Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Andras, P.: Kernel-Kohonen networks. International Journal of Neural Systems 12, 117–135 (2002)Google Scholar
  2. 2.
    Boulet, R., Jouve, B., Rossi, F., Villa, N.: Batch kernel SOM and related laplacian methods for social network analysis. Neurocomputing 71(7-9), 1257–1273 (2008)CrossRefGoogle Scholar
  3. 3.
    Conan-Guez, B., Rossi, F., El Golli, A.: Fast algorithm and implementation of dissimilarity self-organizing maps. Neural Networks 19(6-7), 855–863 (2006)zbMATHCrossRefGoogle Scholar
  4. 4.
    Cottrell, M., Fort, J.C., Pagès, G.: Theoretical aspects of the SOM algorithm. Neurocomputing 21, 119–138 (1998)zbMATHCrossRefGoogle Scholar
  5. 5.
    Cottrell, M., Letrémy, P.: How to use the Kohonen algorithm to simultaneously analyse individuals in a survey. Neurocomputing 63, 193–207 (2005)CrossRefGoogle Scholar
  6. 6.
    Cottrell, M., Olteanu, M., Rossi, F., Rynkiewicz, J., Villa-Vialaneix, N.: Neural networks for complex data. Künstliche Intelligenz 26(2), 1–8 (2012)Google Scholar
  7. 7.
    DeSalle, R., Egan, M., Siddal, M.: The unholy trinity: taxonomy, species delimitation and dna barcoding. Philosophical Transactions of the Royal Society B-Biological Sciences 360, 1905–1916 (2005)CrossRefGoogle Scholar
  8. 8.
    Fort, J.C., Letremy, P., Cottrell, M.: Advantages and drawbacks of the batch kohonen algorithm. In: ESANN 2002, pp. 223–230 (2002)Google Scholar
  9. 9.
    Fruchterman, T., Reingold, B.: Graph drawing by force-directed placement. Software-Practice and Experience 21, 1129–1164 (1991)CrossRefGoogle Scholar
  10. 10.
    Gärtner, T.: Kernel for Structured Data. World Scientific (2008)Google Scholar
  11. 11.
    Hammer, B., Gisbrecht, A., Hasenfuss, A., Mokbel, B., Schleif, F.-M., Zhu, X.: Topographic Mapping of Dissimilarity Data. In: Laaksonen, J., Honkela, T. (eds.) WSOM 2011. LNCS, vol. 6731, pp. 1–15. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Hammer, B., Hasenfuss, A., Strickert, M., Rossi, F.: Topographic processing of relational data. In: Proceedings of the 6th Workshop on Self-Organizing Maps (WSOM 2007), Bielefeld, Germany (September 2007) (to be published)Google Scholar
  13. 13.
    Hammer, B., Rossi, F., Hasenfuss, A.: Accelerating relational clustering algorithms with sparse prototype representation. In: Proceedings of the 6th Workshop on Self-Organizing Maps, WSOM 2007 (2007)Google Scholar
  14. 14.
    Hebert, P.D.N., Penton, E.H., Burns, J.M., Janzen, D.H., Hallwachs, W.: Ten species in one: DNA barcoding reveals cryptic species in the neotropical skipper butterfly astraptes fulgerator. Genetic Analysis (2004)Google Scholar
  15. 15.
    Kimura, M.: A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. Journal of Molecular Evolution 16, 111–120 (1980)CrossRefGoogle Scholar
  16. 16.
    Kohohen, T., Somervuo, P.: Self-Organizing maps of symbol strings. Neurocomputing 21, 19–30 (1998)CrossRefGoogle Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    Olteanu, M., Nicolas, V., Schaeffer, B., Denys, C., Kennis, J., Colyn, M., Missoup, A.D., Laredo, C.: On the use of self-organizing maps for visualizing and studying barcode data. application to two data sets (preprint submitted for publication, 2012)Google Scholar
  19. 19.
    Rossi, F., Hasenfuss, A., Hammer, B.: Accelerating relational clustering algorithms with sparse prototype representation. In: 6th International Workshop on Self-Organizing Maps (WSOM). Neuroinformatics Group. Bielefield University, Bielefield (2007)Google Scholar
  20. 20.
    Saitou, N., Nei, M.: The neighbor-joining method: a new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4(4), 406–425 (1987), Google Scholar
  21. 21.
    Tenenbaum, J.B., Silva, V., Langford, J.C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Madalina Olteanu
    • 1
    Email author
  • Nathalie Villa-Vialaneix
    • 1
  • Marie Cottrell
    • 1
  1. 1.SAMM-Université Paris 1 Panthéon SorbonneParisFrance

Personalised recommendations