Fuzzy Clustering of the Self-Organizing Map: Some Applications on Financial Time Series

  • Peter Sarlin
  • Tomas Eklund
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6731)


The Self-organizing map (SOM) has been widely used in financial applications, not least for time-series analysis. The SOM has not only been utilized as a stand-alone clustering technique, its output has also been used as input for second-stage clustering. However, one ambiguity with the SOM clustering is that the degree of membership in a particular cluster is not always easy to judge. To this end, we propose a fuzzy C-means clustering of the units of two previously presented SOM models for financial time-series analysis: financial benchmarking of companies and monitoring indicators of currency crises. It allows each time-series point to have a partial membership in all identified, but overlapping, clusters, where the cluster centers express the representative financial states for the companies and countries, while the fluctuations of the membership degrees represent their variations over time.


Self-organizing maps fuzzy C-means financial time series 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 66, 59–69 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Ultsch, A., Siemon, H.P.: Kohonen’s self organizing feature maps for exploratory data analysis. In: Proceedings of the International Conference on Neural Networks, pp. 305–308. Kluwer, Dordrecht (1990)Google Scholar
  3. 3.
    Lampinen, J., Oja, E.: Clustering properties of hierarchical self-organizing maps. Journal of Mathematical Imaging and Vision 2(2–3), 261–272 (1992)CrossRefzbMATHGoogle Scholar
  4. 4.
    Murtagh, F.: Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering. Pattern Recognition Letters 16(4), 399–408 (1995)CrossRefGoogle Scholar
  5. 5.
    Kiang, M.Y.: Extending the Kohonen self-organizing map networks for clustering analysis. Computational Statistics and Data Analysis 38, 161–180 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Vesanto, J., Sulkava, M.: Distance Matrix Based Clustering of the Self-Organizing Map. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 951–956. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)CrossRefGoogle Scholar
  8. 8.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)CrossRefzbMATHGoogle Scholar
  9. 9.
    Eklund, T., Back, B., Vanharanta, H., Visa, A.: Using the Self-Organizing Map as a Visualization Tool in Financial Benchmarking. Information Visualization 2, 171–181 (2003)CrossRefGoogle Scholar
  10. 10.
    Sarlin, P.: Visual monitoring of financial stability with a self-organizing neural network. In: Proceedings of the 10th IEEE International Conference on Intelligent Systems Design and Applications, pp. 248–253. IEEE Press, Los Alamitos (2010)Google Scholar
  11. 11.
    Liu, S., Lindholm, C.: Assessing the Early Warning Signals of Financial Crises: A Fuzzy Clustering Approach. Intelligent Systems in Accounting, Finance & Management 14, 179–202 (2006)CrossRefGoogle Scholar
  12. 12.
    Kohonen, T.: Self-Organizing Maps. Springer, Berlin (2001)CrossRefzbMATHGoogle Scholar
  13. 13.
    Dunn, J.C.: A Fuzzy Relative of the ISODATA Process and its Use in Detecting Compact, Well-Separated Clusters. Cybernetics and Systems 3, 32–57 (1973)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Eklund, T., Back, B., Vanharanta, H., Visa, A.: Evaluating a SOM-Based Financial Benchmarking Tool. Journal of Emerging Technologies in Accounting 5, 109–127 (2008)CrossRefGoogle Scholar
  15. 15.
    Guiver, J.P., Klimasauskas, C.C.: Applying Neural Networks, Part IV: Improving Performance. PC AI Magazine 5, 34–41 (1991)Google Scholar
  16. 16.
    Ward, J.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58, 236–244 (1963)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Resta, M.: Early Warning Systems: an approach via Self Organizing Maps with applications to emergent markets. In: Proceedings of the 18th Italian Workshop on Neural Networks, pp. 176–184. IOS Press, Amsterdam (2009)Google Scholar
  18. 18.
    Berg, A., Pattillo, C.: What caused the Asian crises: An early warning system approach. Economic Notes 28, 285–334 (1999)CrossRefGoogle Scholar
  19. 19.
    Sarlin, P., Marghescu, D.: Visual Predictions of Currency Crises using Self-Organizing Maps. Intelligent Systems in Accounting, Finance and Management (forthcoming, 2011)Google Scholar
  20. 20.
    Marghescu, D., Sarlin, P., Liu, S.: Early Warning Analysis for Currency Crises in Emerging Markets: A Revisit with Fuzzy Clustering. Intelligent Systems in Accounting, Finance and Management 17(2–3), 143–165 (2010)CrossRefGoogle Scholar
  21. 21.
    Bezdek, J.C.: Cluster validity with fuzzy sets. Cybernetics 3, 58–73 (1974)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(8), 841–847 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Peter Sarlin
    • 1
    • 2
  • Tomas Eklund
    • 1
  1. 1.Turku Centre for Computer Science – TUCS, Department of Information TechnologiesÅbo Akademi UniversityTurkuFinland
  2. 2.European Central BankFrankfurt am MainGermany

Personalised recommendations