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International Symposium on Intelligent Data Analysis

IDA 2007: Advances in Intelligent Data Analysis VII pp 93–105Cite as

Relational Topographic Maps

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4723))

Abstract

We introduce relational variants of neural topographic maps including the self-organizing map and neural gas, which allow clustering and visualization of data given as pairwise similarities or dissimilarities with continuous prototype updates. It is assumed that the (dis-)similarity matrix originates from Euclidean distances, however, the underlying embedding of points is unknown. Batch optimization schemes for topographic map formations are formulated in terms of the given (dis-)similarities and convergence is guaranteed, thus providing a way to transfer batch optimization to relational data.

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Authors and Affiliations

  1. Clausthal University of Technology – Department of Informatics, Clausthal-Zellerfeld, Germany

    Alexander Hasenfuss & Barbara Hammer

Authors
  1. Alexander Hasenfuss
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  2. Barbara Hammer
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Editor information

Michael R. Berthold John Shawe-Taylor Nada Lavrač

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© 2007 Springer-Verlag Berlin Heidelberg

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Hasenfuss, A., Hammer, B. (2007). Relational Topographic Maps. In: R. Berthold, M., Shawe-Taylor, J., Lavrač, N. (eds) Advances in Intelligent Data Analysis VII. IDA 2007. Lecture Notes in Computer Science, vol 4723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74825-0_9

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  • DOI: https://doi.org/10.1007/978-3-540-74825-0_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74824-3

  • Online ISBN: 978-3-540-74825-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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