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Comprehensive concept description based on association rules: A meta-learning approach

This 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”, which can help us better understand the association rules generated in the first step. We define various types of such meta-rules and report some experiments on benchmark data from the UCI Machine Learning Repository as well as on data from atherosclerosis risk domain. When evaluating the proposed method, we use the LISp-Miner system.