Interactive Classification through Neural Networks

  • Mohamed Daoudi
  • Denis Hamad
  • Jack-Gérard Postaire
Conference paper


In this paper, we present a simple and fast way to provide the operator a plane representation of multidimensional data through neural networks for interactive classification. The superiority of humans over automatic clustering procedures comes from their ability in recognising cluster structures in a two dimensional space, even in the presence of outliers between the clusters, of bridging clusters and of all kinds of irrelevant details in the data points distribution. When giving the operator the interactive means which will help him to isolate clusters of two dimensional points, this visualisation becomes base of a clustering procedure where the operator doesn’t loose his grip on the data he is analysing.


Hide Layer Multidimensional Data Plane Representation Dimensional Point Interactive Classification 
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/Wien 1993

Authors and Affiliations

  • Mohamed Daoudi
    • 1
  • Denis Hamad
    • 1
  • Jack-Gérard Postaire
    • 1
  1. 1.Centre d’automatique de LilleUniversité des Sciences et Technologies de LilleVilleneuve d’Ascq CedexFrance

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