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

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Data Warehousing and Knowledge Discovery (DaWaK 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2114))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Neural Networks Research Centre, Helsinki University of Technology, 5400, FIN, 02015, HUT, Finland

    Samuel Kaski, Janne Sinkkonen & Jaakko Peltonen

Authors
  1. Samuel Kaski
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  2. Janne Sinkkonen
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  3. Jaakko Peltonen
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Editor information

Editors and Affiliations

  1. Kyoto University, Kyoto, 606-8501, Japan

    Yahiko Kambayashi

  2. EC3, Siebensterngasse 21/3, 1070, Wien

    Werner Winiwarter

  3. Center for Spatial Information Science (CSIS), University of Tokyo, 4-6-1, Komaba Meguro-ku, Tokyo, 153-8904, Japan

    Masatoshi Arikawa

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© 2001 Springer-Verlag Berlin Heidelberg

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Kaski, S., Sinkkonen, J., Peltonen, J. (2001). Data Visualization and Analysis with Self-Organizing Maps in Learning Metrics. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2001. Lecture Notes in Computer Science, vol 2114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44801-2_17

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  • DOI: https://doi.org/10.1007/3-540-44801-2_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42553-3

  • Online ISBN: 978-3-540-44801-3

  • eBook Packages: Springer Book Archive

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