Meta-learning for post-processing of association rules

Authors:
Petr Berka;Jan Rauch
Affiliations:
University of Economics, Prague and Institute of Finance and Administration, Prague;University of Economics, Prague
Venue:
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Year:
2010

Citing 6
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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.