Data Visualization and Analysis with Self-Organizing Maps in Learning Metrics

  • Samuel Kaski
  • Janne Sinkkonen
  • Jaakko Peltonen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2114)


High-dimensional data can be visualized and analyzed with the Self-Organizing Map, a method for clustering data and visualizing it on a lower-dimensional display. Results depend on the (often Euclidean) distance measure of the data space. We introduce an improved metric that emphasizes important local directions by measuring changes in an auxiliary, interesting property of the data points, for example their class. A Self-Organizing Map is computed in the new metric and used for vi- sualizing and clustering the data. The trained map represents directions of highest relevance for the property of interest. In data analysis it is especially beneficial that the importance of the original data variables throughout the data space can be assessed and visualized. We apply the method to analyze the bankruptcy risk of Finnish enterprises.


Gaussian Mixture Model Capital Structure Data Space Data Visualization Fisher Information Matrix 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Samuel Kaski
    • 1
  • Janne Sinkkonen
    • 1
  • Jaakko Peltonen
    • 1
  1. 1.Neural Networks Research CentreHelsinki University of TechnologyHUTFinland

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