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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2015: Intelligent Data Engineering and Automated Learning – IDEAL 2015 pp 138–145Cite as

Deterministic Extraction of Compact Sets of Rules for Subgroup Discovery

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Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

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Acknowledgments

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

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Authors and Affiliations

  1. Escuela Técnica Superior de Ingeniería, University of Huelva, Huelva, Spain

    Juan L. Domínguez-Olmedo, Jacinto Mata Vázquez & Victoria Pachón

Authors
  1. Juan L. Domínguez-Olmedo
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  2. Jacinto Mata Vázquez
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  3. Victoria Pachón
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Corresponding author

Correspondence to Juan L. Domínguez-Olmedo .

Editor information

Editors and Affiliations

  1. Wroclaw University of Technology, Wroclaw, Poland

    Konrad Jackowski

  2. Department of Systems, Wroclaw University of Technology, Wroclaw, Poland

    Robert Burduk

  3. Wroclaw Univ of Tech, Wroclaw, Poland

    Krzysztof Walkowiak

  4. Wroclaw University of Technology, Faculty of Electronics, Wroclaw, Poland

    Michal Wozniak

  5. School of Electrical & Electronic E, University of Manchester, Manchester, United Kingdom

    Hujun Yin

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Domínguez-Olmedo, J.L., Vázquez, J.M., Pachón, V. (2015). Deterministic Extraction of Compact Sets of Rules for Subgroup Discovery. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_17

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  • DOI: https://doi.org/10.1007/978-3-319-24834-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24833-2

  • Online ISBN: 978-3-319-24834-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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