Visualization Architecture Based on SOM for Two-Class Sequential Data

  • Ken-ichi Fukui
  • Kazumi Saito
  • Masahiro Kimura
  • Masayuki Numao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)


In this paper, we propose a visualization architecture that constructs a map suggesting clusters in sequence that involve classification utilizing the class label information for the display method of the map. This architecture is based on Self-Organizing Maps (SOM) that are to create clusters and to arrange the similar clusters near within the low dimensional map. This proposed method consists of three steps, firstly the winner neuron trajectories are obtained by SOM, secondly, connectivity weights are obtained by a single layer perceptron based on the winner neuron trajectories, finally, the map is visualized by reversing the obtained weights into the map. In the experiments using time series of real-world medical data, we evaluate the visualization and classification performance by comparing the display method by the number of sample ratio for classes belonging to each cluster.


Reference Vector Display Method Winner Neuron Connectivity Weight Inspection Interval 
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, Heidelberg (1995)Google Scholar
  2. 2.
    Kruskal, J.B., Wish, M.: Multidimensional scaling. In: Number 07-011 in Paper Series on Quantitative Applications in the Social Sciences (1978)Google Scholar
  3. 3.
    Sammon, J.: A nonlinear mapping for data structure analysis. IEEE Transactions on Computers c-18, 401–409 (1969)CrossRefGoogle Scholar
  4. 4.
    Hecht-Nielsen, R.: Counterpropagaton networks. In: IEEE Frist international Conference on Neural Networks, pp. 19–32 (1987)Google Scholar
  5. 5.
    Fritzke, B.: Growing cell structures: A selforganizing networks for unsupervised and supervised learning. Neural Networks 7, 1441–1460 (1994)CrossRefGoogle Scholar
  6. 6.
    Fukuda, N., Saito, K., Matsuo, S., Ishikawa, M.: Som learning model using teacher information. Technical Report 732, IEICE (2004)Google Scholar
  7. 7.
    Kohonen, T., Kaski, S., Lagus, K., Salojrvi, J., Honkela, J., Paatero, V., Saarela, A.: Self organization of a massive document collection. IEEE Transaction on Neural Networks 11(3), 574–585 (2000)CrossRefGoogle Scholar
  8. 8.
    Fukui, K., Saito, K., Kimura, M., Numao, M.: Visualizing Dynamics of the Hot Topics Using Sequence-Based Self-organizing Maps. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3684, pp. 745–751. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Kohonen, T.: Comparison of som point densities based on different criteria. Neural Computation 11, 2081–2095 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ken-ichi Fukui
    • 1
  • Kazumi Saito
    • 2
  • Masahiro Kimura
    • 3
  • Masayuki Numao
    • 1
  1. 1.The Institute of Scientific and Industrial ResearchOsaka UniversityJapan
  2. 2.NTT Communication Science LaboratoriesJapan
  3. 3.Department of Electronics and InformaticsRyukoku UniversityJapan

Personalised recommendations