%0 Journal Article %1 WebbZhang05 %A Webb, G. I. %A Zhang, S. %C Netherlands %D 2005 %I Springer %J Data Mining and Knowledge Discovery %K Association Discovery, OPUS Rule discovery, sound statistically %N 1 %P 39-79 %T k-Optimal-Rule-Discovery %V 10 %X 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.

@article{WebbZhang05, abstract = {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.}, added-at = {2016-03-20T05:42:04.000+0100}, address = {Netherlands}, author = {Webb, G. I. and Zhang, S.}, biburl = {https://www.bibsonomy.org/bibtex/2ba962d7deda58efa2a7fda211e575363/giwebb}, interhash = {f7cf385d4b2380d566612cd86a21715e}, intrahash = {ba962d7deda58efa2a7fda211e575363}, journal = {Data Mining and Knowledge Discovery}, keywords = {Association Discovery, OPUS Rule discovery, sound statistically}, number = 1, pages = {39-79}, publisher = {Springer}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {k-Optimal-Rule-Discovery}, volume = 10, year = 2005 }