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Reduction of Discriminant Rules Based on Frequent Item Set Calculation

  • María C. Fernández-Baizán
  • Ernestina Menasalvas Ruiz
  • Juan Francisco Martínez Sarrías
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 84)

Abstract

Reduction of the number of attributes to calculate rules in large databases is of great interest in data mining In this paper, we propose a method for reducing the number of attributes in rules using frequent item sets calculation. The method is based in a basic step model. In our approach algorithms are divided in atomic operations that have been called basic steps so that it is easier to optimize the execution of any algorithm. We also present the implementation of this approach in Damisys what demonstrates that our approach is implementable and effective dealing with large datasets.

Keywords

Association Rule Basic Step Mining Association Rule Frequent Item Decision 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 2002

Authors and Affiliations

  • María C. Fernández-Baizán
    • 1
  • Ernestina Menasalvas Ruiz
    • 1
  • Juan Francisco Martínez Sarrías
    • 1
  1. 1.Departamento de Lenguajes y Sistemas Informáticos e Ingeniería del Software, Facultad de InformáticaUniversidad Politecnica de MadridMadridSpain

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