K-most-interesting rule discovery finds the k rules that optimize a user-specified measure of interestingness with respect to a set of sample data and user-specified constraints. This approach avoids many limitations of the frequent itemset approach of association rule discovery. This paper presents a scalable algorithm applicable to a wide range of k-most-interesting rule discovery tasks and demonstrates its efficiency.

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