Autonomous clustering for machine learning

  • Oscar Luaces
  • Juan José del Coz
  • José Ramón Quevedo
  • Jaime Alonso
  • José Ranilla
  • Antonio Bahamonde
Plasticity Phenomena (Maturing, Learning & Memory)
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1606)


In this paper, starting from a collection of training examples, we show how to produce a very compact set of classification rules. The induction idea is a clustering principle based on Kohonen’s self-organizing algorithms. The function to optimize in the aggregation of examples to become rules is a classificatory quality measure called impurity level, which was previously employed in our system called Fan. The rule conditions obtained in this way are densely populated areas in the attribute space. The main goal of our system, in addition to its accuracy, is the high quality of explanations that it can provide attached to the classification decisions.


Classification Rule Impurity Level Original Training Cross Validation Experiment Symbolic Attribute 
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 1999

Authors and Affiliations

  • Oscar Luaces
    • 1
  • Juan José del Coz
    • 1
  • José Ramón Quevedo
    • 1
  • Jaime Alonso
    • 1
  • José Ranilla
    • 1
  • Antonio Bahamonde
    • 1
  1. 1.Centro de Inteligencia Artificial. Universidad de Oviedo at GijónGijónEspaña

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