Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
A Unified View of Objective Interestingness Measures
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
On Optimal Rule Mining: A Framework and a Necessary and Sufficient Condition of Antimonotonicity
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining classification rules without support: an anti-monotone property of Jaccard measure
DS'11 Proceedings of the 14th international conference on Discovery science
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We propose a formal definition of the robustness of association rules for interestingness measures. It is a central concept in the evaluation of the rules and has only been studied unsatisfactorily up to now. It is crucial because a good rule (according to a given quality measure) might turn out as a very fragile rule with respect to small variations in the data. The robustness measure that we propose here is based on a model we proposed in a previous work. It depends on the selected quality measure, the value taken by the rule and the minimal acceptance threshold chosen by the user. We present a few properties of this robustness, detail its use in practice and show the outcomes of various experiments. Furthermore, we compare our results to classical tools of statistical analysis of association rules. All in all, we present a new perspective on the evaluation of association rules.