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Discovering Strong Meta Association Rules Using Bees Swarm Optimization

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Book cover Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2018)

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Abstract

For several applications, association rule mining produces an extremely large number of rules. Analyzing a large number of rules can be very time-consuming for users. Therefore, eliminating irrelevant association rules is necessary. This paper addresses this problem by proposing an efficient approach based on the concept of meta association rules. The algorithm first discovers dependencies between association rules called meta association rules. Then, these dependencies are used to eliminate association rules that can be replaced by a more general rule. Because the set of meta-rules can be very large, a bee swarm optimization approach is applied to quickly extract the strongest meta-rules. The approach has been applied on a synthetic dataset and compared with a state-of-the-art algorithm. Results are promising in terms of number of rules found and their quality.

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Author information

Authors and Affiliations

  1. IMADA, SDU, Odense, Denmark

    Youcef Djenouri

  2. RIMA, USTHB, Algiers, Algeria

    Asma Belhadi

  3. School of Social Sciences and Humanities, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  4. Department of Computing, Mathematics, and Physics, Western Norway University of Applied Sciences (HVL), Bergen, Norway

    Jerry Chun-Wei Lin

Authors
  1. Youcef Djenouri
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  2. Asma Belhadi
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  3. Philippe Fournier-Viger
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  4. Jerry Chun-Wei Lin
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Corresponding author

Correspondence to Youcef Djenouri .

Editor information

Editors and Affiliations

  1. University of Melbourne, Melbourne, VIC, Australia

    Mohadeseh Ganji

  2. University of Melbourne, Melbourne, VIC, Australia

    Lida Rashidi

  3. McGill University, Montreal, QC, Canada

    Benjamin C. M. Fung

  4. Griffith University, Gold Coast, QLD, Australia

    Can Wang

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Djenouri, Y., Belhadi, A., Fournier-Viger, P., Lin, J.CW. (2018). Discovering Strong Meta Association Rules Using Bees Swarm Optimization. In: Ganji, M., Rashidi, L., Fung, B., Wang, C. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 11154. Springer, Cham. https://doi.org/10.1007/978-3-030-04503-6_21

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  • DOI: https://doi.org/10.1007/978-3-030-04503-6_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04502-9

  • Online ISBN: 978-3-030-04503-6

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