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Data Mining and Knowledge Discovery in Medical Applications Using Self-Organizing Maps

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Medical Data Analysis (ISMDA 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1933))

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Abstract

In the contribution the authors discuss the application of Self-Organizing Maps (SOMs) for data mining and knowledge discovery in medicine. Thereby, the usually assumed but not verified topology preservation is in the main focus. Extensions of the usual SOM are offered to obtain correct results. The authors give examples for applications such as visualization and clustering.

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Author information

Authors and Affiliations

  1. Clinic for Psychotherapy, University Leipzig, K.-Tauchnitz-Str. 25, 04109, Leipzig, Germany

    Thomas Villmann & Michael Geyer

  2. Clinic of Neurology, University Leipzig, Liebigstr. 22a, 04103, Leipzig, Germany

    Wieland Hermann

Authors
  1. Thomas Villmann
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  2. Wieland Hermann
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  3. Michael Geyer
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Editor information

Editors and Affiliations

  1. Fachbereich Biologie und Informatik AG Adaptive Systemarchitektur, Johann Wolfgang Goethe-Universität, Robert-Mayer-Str. 11-15, 60054, Frankfurt a.M., Germany

    Rüdiger W. Brause

  2. Klinik für Allgemein- und Gefässchirurgie, Johann Wolfgang Goethe-Universität, Theodor-Stern-Kai 7, 60590, Frankfurt a.M., Germany

    Ernst Hanisch

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© 2000 Springer-Verlag Berlin Heidelberg

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Villmann, T., Hermann, W., Geyer, M. (2000). Data Mining and Knowledge Discovery in Medical Applications Using Self-Organizing Maps. In: Brause, R.W., Hanisch, E. (eds) Medical Data Analysis. ISMDA 2000. Lecture Notes in Computer Science, vol 1933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39949-6_18

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  • DOI: https://doi.org/10.1007/3-540-39949-6_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41089-8

  • Online ISBN: 978-3-540-39949-0

  • eBook Packages: Springer Book Archive

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