Robust Self-organizing Maps

  • Héctor Allende
  • Sebastián Moreno
  • Cristian Rogel
  • Rodrigo Salas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)


The Self Organizing Map (SOM) model is an unsupervised learning neural network that has been successfully applied as a data mining tool. The advantages of the SOMs are that they preserve the topology of the data space, they project high dimensional data to a lower dimension representation scheme, and are able to find similarities in the data.

However, the learning algorithm of the SOM is sensitive to the presence of noise and outliers as we will show in this paper. Due to the influence of the outliers in the learning process, some neurons (prototypes) of the ordered map get located far from the majority of data, and therefore, the network will not effectively represent the topological structure of the data under study.

In this paper, we propose a variant to the learning algorithm that is robust under the presence of outliers in the data by being resistant to these deviations. We call this algorithm Robust SOM (RSOM). We will illustrate our technique on synthetic and real data sets.


Self Organizing Maps Robust Learning Algorithm Data Mining Artificial Neural Networks 


  1. 1.
    Allende, H., Moraga, C., Salas, R.: Robust estimator for the learning process in neural networks applied in time series. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 1080–1086. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)Google Scholar
  3. 3.
    Erwin, E., Obermayer, K., Schulten, K.: Self-organizing maps: ordering, convergence properties and energy functions. Biological Cybernetics 67, 47–55 (1992)zbMATHCrossRefGoogle Scholar
  4. 4.
    Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., Stahel, W.A.: Robust statistics, Wiley Series in Probability and Mathematical Statistics (1986)Google Scholar
  5. 5.
    Huber, P.J.: Robust statistics, Wiley Series in probability and mathematical statistics (1981)Google Scholar
  6. 6.
    Kohonen, T.: The self-organizing map. Proceedings of the IEEE 78, 1464–1480 (1990)CrossRefGoogle Scholar
  7. 7.
    Kohonen, T.: Self-Organizing Maps, vol. 30. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  8. 8.
    Mangasarian, O., Street, W., Wolberg, W.: Breast cancer diagnosis and prognosis via linear programming. Operations Research 43(4), 570–577 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Ritter, H., Schulten, K.: Kohonen’s self organizing maps: Exploring their computational capabilities. In: IEEE ICNN 1988, vol. I, pp. 109–116 (1988)Google Scholar
  10. 10.
    Su, M., Chang, H.: Fast self-organizing feature map algorithm. IEEE Trans. on Neural Networks 11(3), 721–733 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Héctor Allende
    • 1
  • Sebastián Moreno
    • 1
  • Cristian Rogel
    • 1
  • Rodrigo Salas
    • 1
    • 2
  1. 1.Dept. de InformáticaUniversidad Técnica Federico Santa MaríaValparaísoChile
  2. 2.Departamento de ComputaciónUniversidad de Valparaíso 

Personalised recommendations