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URN: urn:nbn:de:0030-drops-11182
URL: http://drops.dagstuhl.de/opus/volltexte/2007/1118/
Hammer, Barbara ; Hasenfuss, Alexander

Relational Clustering

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Abstract

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

@InProceedings{hammer_et_al:DSP:2007:1118,
  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 =		{http://drops.dagstuhl.de/opus/volltexte/2007/1118},
  annote =	{Keywords: Neural gas, dissimilarity data}
}

Keywords: Neural gas, dissimilarity data
Seminar: 07131 - Similarity-based Clustering and its Application to Medicine and Biology
Issue date: 2007
Date of publication: 16.07.2007

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