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Relational Neural Gas

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Book cover KI 2007: Advances in Artificial Intelligence (KI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4667))

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Abstract

We introduce relational variants of neural gas, a very efficient and powerful neural clustering algorithm, which allow a clustering and mining of data given in terms of a pairwise similarity or dissimilarity matrix. It is assumed that this matrix stems from Euclidean distance or dot product, respectively, however, the underlying embedding of points is unknown. One can equivalently formulate batch optimization in terms of the given similarities or dissimilarities, thus providing a way to transfer batch optimization to relational data. For this procedure, convergence is guaranteed and extensions such as the integration of label information can readily be transferred to this framework.

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Authors and Affiliations

  1. Clausthal University of Technology, Institute of Computer Science, Clausthal-Zellerfeld, Germany

    Barbara Hammer & Alexander Hasenfuss

Authors
  1. Barbara Hammer
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  2. Alexander Hasenfuss
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Editor information

Joachim Hertzberg Michael Beetz Roman Englert

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© 2007 Springer-Verlag Berlin Heidelberg

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Hammer, B., Hasenfuss, A. (2007). Relational Neural Gas. In: Hertzberg, J., Beetz, M., Englert, R. (eds) KI 2007: Advances in Artificial Intelligence. KI 2007. Lecture Notes in Computer Science(), vol 4667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74565-5_16

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  • DOI: https://doi.org/10.1007/978-3-540-74565-5_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74564-8

  • Online ISBN: 978-3-540-74565-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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