Separation and Unification of Individuality and Collectivity and Its Application to Explicit Class Structure in Self-Organizing Maps

  • Ryotaro Kamimura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)


In this paper, we propose a new type of learning method in which individuality and collectivity are separated and unified to control the characteristics of neurons. This unification is expected to enhance the characteristics shared by individual and collective outputs, while the characteristics specific to them are weakened. We applied the method to self-organizing maps to demonstrate the utility of unification. In self-organizing maps, the introduction of unification has the effect of controlling cooperation among neurons. Experimental results on the glass identification problem from the machine learning database showed that explicit class boundaries could be obtained by introducing the unification.


Connection Weight Quantization Error Class Boundary Neighborhood Function Spread Parameter 
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.
    Kohonen, T.: Self-Organizing Maps. Springer (1995)Google Scholar
  2. 2.
    Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Transactions on Computers C-18(5), 401–409 (1969)Google Scholar
  3. 3.
    Ultsch, A., Siemon, H.P.: Kohonen self-organization feature maps for exploratory data analysis. In: Proceedings of International Neural Network Conference, pp. 305–308. Kulwer Academic Publisher, Dordrecht (1990)Google Scholar
  4. 4.
    Vesanto, J.: SOM-based data visualization methods. Intelligent Data Analysis 3, 111–126 (1999)CrossRefzbMATHGoogle Scholar
  5. 5.
    Kaski, S., Nikkila, J., Kohonen, T.: Methods for interpreting a self-organized map in data analysis. In: Proceedings of European Symposium on Artificial Neural Networks, Bruges, Belgium (1998)Google Scholar
  6. 6.
    Yin, H.: ViSOM-a novel method for multivariate data projection and structure visualization. IEEE Transactions on Neural Networks 13(1), 237–243 (2002)CrossRefGoogle Scholar
  7. 7.
    Kamimura, R.: Self-enhancement learning: target-creating learning and its application to self-organizing maps. Biological Cybernetics, 1–34 (2011)Google Scholar
  8. 8.
    Frank, A., Asuncion, A.: UCI machine learning repository (2010)Google Scholar
  9. 9.
    Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM toolbox for Matlab. tech. rep., Laboratory of Computer and Information Science, Helsinki University of Technology (2000)Google Scholar
  10. 10.
    Kiviluoto, K.: Topology preservation in self-organizing maps. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 294–299 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ryotaro Kamimura
    • 1
  1. 1.IT Education CenterHiratsukaJapan

Personalised recommendations