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Meta-learning for Post-processing of Association Rules

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 6263)

Abstract

The paper presents a novel approach to post-processing of association rules based on the idea of meta-learning. A subsequent association rule mining step is applied to the results of ”standard” association rule mining. We thus obtain ”rules about rules” that help to better understand the association rules generated in the first step.

We define various types of such meta-rules and report some experiments on UCI data. When evaluating the proposed method, we use the apriori algorithm implemented in Weka.

Keywords

  • Association Rule
  • Rule Mining
  • Association Rule Mining
  • Apriori Algorithm
  • Binary Attribute

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-642-15105-7_20
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Berka, P., Rauch, J. (2010). Meta-learning for Post-processing of Association Rules. In: Bach Pedersen, T., Mohania, M.K., Tjoa, A.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2010. Lecture Notes in Computer Science, vol 6263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15105-7_20

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  • DOI: https://doi.org/10.1007/978-3-642-15105-7_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15104-0

  • Online ISBN: 978-3-642-15105-7

  • eBook Packages: Computer ScienceComputer Science (R0)