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Deterministic Extraction of Compact Sets of Rules for Subgroup Discovery

  • Juan L. Domínguez-OlmedoEmail author
  • Jacinto Mata Vázquez
  • Victoria Pachón
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9375)

Abstract

This work presents a novel deterministic method to obtain rules for Subgroup Discovery tasks. It makes no previous discretization for the numeric attributes, but their conditions are obtained dynamically. To obtain the final rules, the AUC value of a rule has been used for selecting them. An experimental study supported by appropriate statistical tests was performed, showing good results in comparison with the classic deterministic algorithms CN2-SD and APRIORI-SD. The best results were obtained in the number of induced rules, where a significant reduction was achieved. Also, better coverage and less number of attributes were obtained in the comparison with CN2-SD.

Keywords

Data mining Machine learning Rule-based systems 

Notes

Acknowledgments

This work was partially funded by the Regional Government of Andalusia (Junta de Andalucía), grant number TIC-7629.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Juan L. Domínguez-Olmedo
    • 1
    Email author
  • Jacinto Mata Vázquez
    • 1
  • Victoria Pachón
    • 1
  1. 1.Escuela Técnica Superior de IngenieríaUniversity of HuelvaHuelvaSpain

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