Speeding Up the Dissimilarity Self-Organizing Maps by Branch and Bound

  • Brieuc Conan-Guez
  • Fabrice Rossi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)


This paper proposes to apply the branch and bound principle from combinatorial optimization to the Dissimilarity Self-Organizing Map (DSOM), a variant of the SOM that can handle dissimilarity data. A new reference model optimization method is derived from this principle. Its results are strictly identical to those of the original DSOM algorithm by Kohonen and Somervuo, while its running time is reduced by a factor up to 2.5 compared to the one of the previously proposed optimized implementation.


Exhaustive Search Word List Model Collision Neighborhood Function Prior Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Brieuc Conan-Guez
    • 1
  • Fabrice Rossi
    • 2
  1. 1.LITA EA3097, Université de Metz, Ile du Saulcy, F-57045, MetzFrance
  2. 2.Projet AxIS, INRIA, Domaine de Voluceau, Rocquencourt, B.P. 105, 78153 Le Chesnay CedexFrance

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