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Post–mining on Association Rule Bases

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Artificial Intelligence. ECAI 2023 International Workshops (ECAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1948))

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Abstract

Association rule mining identifies potentially unknown correlations between columns in a relational database and is therefore a central task in the data mining process. In many cases though, the resulting set of association rules is by far too big and confusing for a domain expert to extract useful knowledge. Moreover, the domain expert might often expect that particular association rules are generated by the data mining process and would like to be able to search, e. g., for similar rules according to their expectation.

In this paper, we propose to store association rules in a rule base which will offer functionality for the management of the rules. The rule base can also offer subsumption features for condensing huge sets of derived association rules to smaller subsets of interesting rules that can be investigated by the domain expert. The rule base can store association rules derived in different attempts of rule mining. And depending on the result of a rule mining attempt, further attempts can be initiated. The post–processing of the rule mining result, for example, can derive further association rules by grouping the values of attributes in their consequences; it can also initiate another round of pre-processing of the base relation to group certain values of certain attributes to sets (tiles), and thus to derive correlations between sets of attribute values in further steps of association rule mining.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Knowledge-Based Systems Group (KBS), University of Würzburg, Am Hubland, 97074, Würzburg, Germany

    Dietmar Seipel, Marcel Waleska, Daniel Weidner & Sven Rausch

  2. Institute of Computer Science, Semantic Information Systems Group (SIS), Osnabrück University, Wachsbleiche 27, 49090, Osnabrück, Germany

    Martin Atzmueller

  3. German Research Center for Artificial Intelligence (DFKI), Hamburger Street 24, 49084, Osnabrück, Germany

    Martin Atzmueller

Authors
  1. Dietmar Seipel
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  2. Marcel Waleska
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  3. Daniel Weidner
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  4. Sven Rausch
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  5. Martin Atzmueller
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Corresponding author

Correspondence to Dietmar Seipel .

Editor information

Editors and Affiliations

  1. Halmstad University, Halmstad, Sweden

    Sławomir Nowaczyk

  2. Warsaw University of Technology, Warsaw, Poland

    Przemysław Biecek

  3. Warsaw University, Warsaw, Poland

    Neo Christopher Chung

  4. University of Huddersfield, Huddersfield, UK

    Mauro Vallati

  5. AGH University of Science and Technology, Kraków, Poland

    Paweł Skruch

  6. AGH University of Science and Technology, Kraków, Poland

    Joanna Jaworek-Korjakowska

  7. University of Huddersfield, Huddersfield, UK

    Simon Parkinson

  8. University of Huddersfield, Huddersfield, UK

    Alexandros Nikitas

  9. Universität Osnabrück, Osnabrück, Germany

    Martin Atzmüller

  10. University of Economics Prague, Prague, Czech Republic

    Tomáš Kliegr

  11. University of Bamberg, Bamberg, Germany

    Ute Schmid

  12. Jagiellonian University, Kraków, Poland

    Szymon Bobek

  13. Jožef Stefan Institute, Ljubljana, Slovenia

    Nada Lavrac

  14. HU University of Applied Sciences Utrecht, Utrecht, The Netherlands

    Marieke Peeters

  15. Rotterdam University of Applied Sciences, Rotterdam, The Netherlands

    Roland van Dierendonck

  16. Amsterdam University of Applied Sciences, Amsterdam, The Netherlands

    Saskia Robben

  17. University of Reims Champagne-Ardenne, Reims, France

    Eunika Mercier-Laurent

  18. Istanbul Technical University, Istanbul, Türkiye

    Gülgün Kayakutlu

  19. Wroclaw University of Economics and Business, Wrocław, Poland

    Mieczyslaw Lech Owoc

  20. University of Galway, Galway, Ireland

    Karl Mason

  21. University of Galway, Galway, Ireland

    Abdul Wahid

  22. University of Calabria, Rende, Italy

    Pierangela Bruno

  23. University of Calabria, Rende, Italy

    Francesco Calimeri

  24. Marche Polytechnic University, Ancona, Italy

    Francesco Cauteruccio

  25. University of Calabria, Rende, Italy

    Giorgio Terracina

  26. University of Bamberg, Bamberg, Germany

    Diedrich Wolter

  27. Coburg University of Applied Sciences, Coburg, Germany

    Jochen L. Leidner

  28. FAU Erlangen-Nürnberg, Erlangen, Germany

    Michael Kohlhase

  29. University of Leeds, Leeds, UK

    Vania Dimitrova

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Seipel, D., Waleska, M., Weidner, D., Rausch, S., Atzmueller, M. (2024). Post–mining on Association Rule Bases. In: Nowaczyk, S., et al. Artificial Intelligence. ECAI 2023 International Workshops. ECAI 2023. Communications in Computer and Information Science, vol 1948. Springer, Cham. https://doi.org/10.1007/978-3-031-50485-3_2

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  • DOI: https://doi.org/10.1007/978-3-031-50485-3_2

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