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Similarity-Based Clustering pp 92–117Cite as

Median Topographic Maps for Biomedical Data Sets

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5400))

Abstract

Median clustering extends popular neural data analysis methods such as the self-organizing map or neural gas to general data structures given by a dissimilarity matrix only. This offers flexible and robust global data inspection methods which are particularly suited for a variety of data as occurs in biomedical domains. In this chapter, we give an overview about median clustering and its properties and extensions, with a particular focus on efficient implementations adapted to large scale data analysis.

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Author information

Authors and Affiliations

  1. Clausthal University of Technology, D-38678, Clausthal-Zellerfeld, Germany

    Barbara Hammer & Alexander Hasenfuss

  2. INRIA Rocquencourt, Domaine de Voluceau, Rocquencourt, B.P. 105, 78153, Le Chesnay Cedex, France

    Fabrice Rossi

Authors
  1. Barbara Hammer
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  2. Alexander Hasenfuss
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  3. Fabrice Rossi
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Editor information

Editors and Affiliations

  1. Mathematics and Computing Science, Intelligent Systems Group, University Groningen, P.O. Box 407, 9700 AK, Groningen, Netherlands

    Michael Biehl

  2. Department of Computer Science, Clausthal University of Technology, 38679, Clausthal-Zellerfeld, Germany

    Barbara Hammer

  3. Machine Learning Group, DICE, Place du Levant, Université catholique de Louvain,, 3-B-1348, Louvain-la-Neuve, Belgium

    Michel Verleysen

  4. Dep. of Mathematics/Physics/Computer Sciences, University of Applied Sciences Mittweida, Technikumplatz 17, 09648, Mittweida, Germany

    Thomas Villmann

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Hammer, B., Hasenfuss, A., Rossi, F. (2009). Median Topographic Maps for Biomedical Data Sets. In: Biehl, M., Hammer, B., Verleysen, M., Villmann, T. (eds) Similarity-Based Clustering. Lecture Notes in Computer Science(), vol 5400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01805-3_6

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  • DOI: https://doi.org/10.1007/978-3-642-01805-3_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01804-6

  • Online ISBN: 978-3-642-01805-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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