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Genetic Programming for Mining Association Rules in Relational Database Environments

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Handbook of Genetic Programming Applications

Abstract

Most approaches for the extraction of association rules look for associations from a dataset in the form of a single table. However, with the growing interest in the storage of information, relational databases comprising a series of relations (tables) and relationships have become essential. We present the first grammar-guided genetic programming approach for mining association rules directly from relational databases. We represent the relational databases as trees by means of genetic programming, preserving the original database structure and enabling rules to be defined in an expressive and very flexible way. The proposed model deals with both positive and negative items, and also with both discrete and quantitative attributes. We exemplify the utility of the proposed approach with an artificial generated database having different characteristics. We also analyse a real case study, discovering interesting students’ behaviors from a moodle database.

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Notes

  1. 1.

    This synthetic relational database is available for download at http://www.uco.es/grupos/kdis/kdiswiki/index.php/Mining_Association_Rules_in_Relational_Databases.

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Acknowledgements

This research was supported by the Spanish Ministry of Science and Technology, project TIN-2011-22408, and by FEDER funds. This research was also supported by the Spanish Ministry of Education under FPU grants AP2010-0041 and AP2010-0042.

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Authors and Affiliations

  1. Department of Computer Science and Numerical Analysis, University of Crdoba, Rabanales Campus, 14071, Cordoba, Spain

    J. M. Luna, A. Cano & S. Ventura

Authors
  1. J. M. Luna
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  2. A. Cano
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  3. S. Ventura
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Corresponding author

Correspondence to S. Ventura .

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Editors and Affiliations

  1. BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, USA

    Amir H. Gandomi

  2. Department of Civil & Environmental Engineering, Michigan State University, East Lansing, Michigan, USA

    Amir H. Alavi

  3. Department of Computer Science and Information Systems, University of Limerick, Limerick, Limerick, Ireland

    Conor Ryan

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Luna, J.M., Cano, A., Ventura, S. (2015). Genetic Programming for Mining Association Rules in Relational Database Environments. In: Gandomi, A., Alavi, A., Ryan, C. (eds) Handbook of Genetic Programming Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-20883-1_17

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  • DOI: https://doi.org/10.1007/978-3-319-20883-1_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20882-4

  • Online ISBN: 978-3-319-20883-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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