Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Measuring the interestingness of discovered knowledge: A principled approach
Intelligent Data Analysis
A Unified View of Objective Interestingness Measures
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Application-Independent Feature Construction from Noisy Samples
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Optimonotone Measures For Optimal Rule Discovery
Computational Intelligence
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The large amount of association rules resulting from a KDD process makes the exploitation of the patterns embedded in the database difficult even impossible. In order to address this problem, various interestingness measures were proposed for selecting the most relevant rules. Nevertheless, the choice of an appropriate measure remains a hard task and the use of several measures may lead to conflicting information. In this paper, we propose a unified framework for a set of interestingness measures $\mathcal{M}$ and prove that most of the usual objective measures behave in a similar way. In the context of classification rules, we show that each measure of $\mathcal{M}$ admits a lower bound on condition that a minimal frequency threshold and a maximal number of exceptions are considered. Furthermore, our framework enables to characterize the whole collection of the rules simultaneously optimizing all the measures of $\mathcal{M}$. We finally provide a method to mine a rule cover of this collection.