Regularization Background of Clustering Algorithms

  • Piotr Boguś
  • Katarzyna Lewandowska
  • Francesco Masulli
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 19)


The paper presents the hard, fuzzy, possibilistic and maximum entropy principle clustering algorithms from the regularization theory point of view. The differences between the objective functions lay in the choice which their term is treated as the standard term and which as the regularization term.


Cluster Algorithm Vector Quantization Regularization Term Learn Vector Quantization Possibilistic Approach 
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 2003

Authors and Affiliations

  • Piotr Boguś
    • 1
  • Katarzyna Lewandowska
    • 1
  • Francesco Masulli
    • 2
    • 3
  1. 1.Department of Physics and BiophysicsMedical University of GdańskGdańskPoland
  2. 2.INFM National Institute for Physics of MatterGenovaItaly
  3. 3.Department of Computer SciencesUniversity of PisaPisaItaly

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