A New Adaptation of Self-Organizing Map for Dissimilarity Data

  • Tien Ho-Phuoc
  • Anne Guérin-Dugué
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)


Adaptation of the Self-Organizing Map to dissimilarity data is of a growing interest. For many applications, vector representation is not available and but only proximity data (distance, dissimilarity, similarity, ranks ...). In this article, we present a new adaptation of the SOM algorithm which is compared with two existing ones. Three metrics for quality estimate (quantization and neighborhood) are used for comparison. Numerical experiments on artificial and real data show the algorithm quality. The strong point of the proposed algorithm comes from a more accurate prototype estimate which is one of the difficult parts of Dissimilarity SOM algorithms (DSOM).


dissimilarity data self organizing map 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kohonen, T.: Self-Organizing Maps. Springer, New York (1997)zbMATHGoogle Scholar
  2. 2.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Dokl. 10(8), 707–710 (1966)MathSciNetGoogle Scholar
  3. 3.
    Borg, I., Groenen, P.: Modern Multidimensional Scaling: Theory and Applications. Springer, New York (1997)zbMATHGoogle Scholar
  4. 4.
    Graepel, T., Obermayer, K.A.: stochastic self-organizing map for proximity data. Neural Computation 11(1), 139–155 (1999)CrossRefGoogle Scholar
  5. 5.
    Kohonen, T., Somervuo, P.J.: Self-organizing maps for symbol strings. Neurocomputing 21, 19–30 (1998)zbMATHCrossRefGoogle Scholar
  6. 6.
    Kohonen, T., Somervuo, P.J.: How to make large self-organizing maps for non vectorial data. Neural networks 21(8) (2002)Google Scholar
  7. 7.
    El Golli, A., Conan-Guez, B., Rossi, F.: A self organizing map for dissimilarity data. In: IFCS-04, International Federation of Classification Societies, Chicago, pp. 61–68 (2004)Google Scholar
  8. 8.
    Ambroise, C., Govaert, G.: Analyzing dissimilarity matrices via Kohonen maps. In: IFCS-96, vol. 2, Kobe, Japan, pp. 96–99. Int. Federation of Classification Societies (1996)Google Scholar
  9. 9.
    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
  10. 10.
    Martínez, C.D., Juan, A., Casacuberta, F.: Improving classification using median string and NN rules. In: IX Spanish Symp. on Pattern Recog. and Image Analysis, vol. 2, pp. 391–395 (2001)Google Scholar
  11. 11.
  12. 12.
    Joly, S., Le Calvé, G.: Similarity functions. In: Van Cutsem, B. (ed.) Classification and Dissimilarity Analysis. Lecture Notes in Statistics, Springer, New York (1994)Google Scholar
  13. 13.
    Venna, J., Kaski, S.: Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 485–491. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  14. 14.
  15. 15.

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Tien Ho-Phuoc
    • 1
  • Anne Guérin-Dugué
    • 1
  1. 1.Laboratory of Images and Signals, INPG-CNRS, 46 avenue Félix Viallet, 38031 GrenobleFrance

Personalised recommendations