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.
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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
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