K-optimal rule discovery finds the
K rules that optimize a user-specified measure of rule value 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-optimal rule discovery tasks and demonstrates its efficiency.
exploratory rule discovery - association rules - classification rules - rule search - search space pruning