Recent Advances with the Growing Hierarchical Self-Organizing Map

  • Michael Dittenbach
  • Andreas Rauber
  • Dieter Merkl


We present our recent work on the Growing Hierarchical Self-Organizing Map, a dynamically growing neural network model which evolves into a hierarchical structure according to the necessities of the input data during an unsupervised training process. The benefits of this novel architecture are shown by organizing a real-world document collection according to semantic similarities.


Weight Vector Neural Network Model Internal Affair Neighboring Unit Neighboring Branch 
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 London Limited 2001

Authors and Affiliations

  • Michael Dittenbach
    • 1
  • Andreas Rauber
    • 2
  • Dieter Merkl
    • 2
  1. 1.E-Commerce Competence Center - EC3WienAustria
  2. 2.Institut für Softwaretechnik, Technische Universität WienWienAustria

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