Computational Statistics

, Volume 16, Issue 3, pp 387–398 | Cite as

Statistical Pruning of Discovered Association Rules

  • Dario Bruzzese
  • Cristina Davino


Nowadays mining association rules in a database is a quite simple task; many algorithms have been developed to discover regularities in data. The analysis and the interpretation of the discovered rules are more difficult or almost impossible, given the huge number of generated rules. In this paper we propose a three step strategy to select only interesting association rules after the mining process. The proposed approach is based on the introduction of statistical tests in order to prune logical implications that are not significant.


association rules support confidence significance tests 


  1. Agrawal, R., Imielinski, T. & Swami, A. (1993), ‘Mining Association Rules between Sets of Items in Large Databases’, Proceedings of the 1993 ACM SIGMOD Conference, May, Washington DC, USA, 207–216.Google Scholar
  2. Bayardo, R.J.Jr., Agrawal, R. (1999), ‘Mining the Most Interesting Rules’, Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 145–154.Google Scholar
  3. Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., & Verkamo, A.I. (1994), ‘Finding interesting rules from large sets of discovered association rules’, Proceedings of the Third International Conference on Information and Knowledge Management CIKM-94, 401–407.Google Scholar
  4. Liu, B., Hsu, W. & Ma, Y. (1999), ‘Pruning and Summarizing the Discovered Associations’, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), August 15–18, San Diego, CA, USA.Google Scholar
  5. Liu, B., Hsu, W., Wang, K. & Chen, S. (1999), ‘Visually Aided Exploration Interesting Association Rules’, Proceedings of the Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD-99), April 26–28, Beijing.Google Scholar
  6. Silberschatz, A. & Tuzhilin, A. (1995), ‘On subjective measures of interestingness in knowledge discovery’, Proceedings of the First International Conference on Knowledge Discovery and Data Mining, 275–281.Google Scholar
  7. Toivonen, H., Klemettinen, M., Ronkainen, P., Hatonen, K. & Mannila, H. (1995), ‘Pruning and grouping of discovered association rules’, Workshop Notes of the ECML-95 Workshop on Statistics, Machine Learning, and Knowledge Discovery in Databases, 47–52, Heraklion, Greece, April 1995.Google Scholar
  8. Weber I. (1998), ‘On Pruning Strategies for Discovery of Generalized and Quantitative Association Rules’, Proceedings of Knowledge Discovery and Data Mining Workshop, Singapore.Google Scholar
  9. Shah, D., Lakshmanan, L.V.S., Ramamritham, K. & Sudarshan S. (1999), ‘Interestingness and Pruning of Mined Patterns’, Workshop Notes of the 1999 ACM SIGMOD Research Issues in Data Mining and Knowledge Discovery.Google Scholar

Copyright information

© Physica-Verlag 2001

Authors and Affiliations

  • Dario Bruzzese
    • 1
  • Cristina Davino
    • 1
  1. 1.Dipartimento di Matematica e StatisticaUniversità degli Studi di Napoli“Federico II”NapoliItaly

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