License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
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DOI: 10.4230/DagSemProc.07131.6
URN: urn:nbn:de:0030-drops-11182
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Hammer, Barbara ; Hasenfuss, Alexander

Relational Clustering

07131.HammerBarbara.Paper.1118.pdf (0.2 MB)


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 =	{Hammer, Barbara and Hasenfuss, Alexander},
  title =	{{Relational Clustering}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-11182},
  doi =		{10.4230/DagSemProc.07131.6},
  annote =	{Keywords: Neural gas, dissimilarity data}

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

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