Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH scholarly article en Hammer, Barbara; Hasenfuss, Alexander License
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URN: urn:nbn:de:0030-drops-11182


Relational Clustering



We introduce relational variants of neural gas, a very efficient and powerful neural clustering algorithm. It is assumed that a similarity or dissimilarity matrix is given which stems from Euclidean distance or dot product, respectively, however, the underlying embedding of points is unknown. In this case, 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. Interestingly, convergence is guaranteed even for general symmetric and nonsingular metrics.

BibTeX - Entry

  author =	{Barbara Hammer and Alexander Hasenfuss},
  title =	{Relational Clustering},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  year =	{2007},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann },
  number =	{07131},
  series =	{Dagstuhl Seminar Proceedings},
  ISSN =	{1862-4405},
  publisher =	{Internationales Begegnungs- und Forschungszentrum f{\"u}r Informatik (IBFI), Schloss Dagstuhl, Germany},
  address =	{Dagstuhl, Germany},
  URL =		{},
  annote =	{Keywords: Neural gas, dissimilarity data}

Keywords: Neural gas, dissimilarity data
Seminar: 07131 - Similarity-based Clustering and its Application to Medicine and Biology
Related Scholarly Article:
Issue date: 2007
Date of publication: 2007

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