Hebbian Iterative Method for Unsupervised Clustering with Automatic Detection of the Number of Clusters with Discrete Recurrent Networks

  • Enrique Mérida-Casermeiro
  • Domingo López-Rodríguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4177)


In this paper, two important issues concerning pattern recognition by neural networks are studied: a new model of hebbian learning, as well as the effect of the network capacity when retrieving patterns and performing clustering tasks. Particularly, an explanation of the energy function when the capacity is exceeded: the limitation in pattern storage implies that similar patterns are going to be identified by the network, therefore forming different clusters.

This ability can be translated as an unsupervised learning of pattern clusters, with one major advantage over most clustering algorithms: the number of data classes is automatically learned, as confirmed by the experiments. Two methods to reinforce learning are proposed to improve the quality of the clustering, by enhancing the learning of patterns relationships.

As a related issue, a study on the net capacity, depending on the number of neurons and possible outputs, is presented, and some interesting conclusions are commented.


Energy Function Input Pattern Learning Rule Unsupervised Cluster Recurrent Network 
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 2006

Authors and Affiliations

  • Enrique Mérida-Casermeiro
    • 1
  • Domingo López-Rodríguez
    • 1
  1. 1.Department of Applied MathematicsUniversity of MálagaMálagaSpain

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