%0 Journal Article %1 keyhere %A Webb, Geoffrey I. %A Zhang, Songmao %D 2005 %J Data Mining and Knowledge Discovery %K frequent-patterns top-k %N 1 %P 39--79 %T K-Optimal Rule Discovery %U http://dx.doi.org/10.1007/s10618-005-0255-4 %V 10 %X 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. ER -

@article{keyhere, abstract = {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. ER -}, added-at = {2009-04-23T12:20:35.000+0200}, author = {Webb, Geoffrey I. and Zhang, Songmao}, biburl = {https://www.bibsonomy.org/bibtex/2364a3846a10ac9771eb4495e76cd19c5/claudio.lucchese}, description = {SpringerLink - Journal Article}, interhash = {f7cf385d4b2380d566612cd86a21715e}, intrahash = {364a3846a10ac9771eb4495e76cd19c5}, journal = {Data Mining and Knowledge Discovery}, keywords = {frequent-patterns top-k}, month = {#jan#}, number = 1, pages = {39--79}, timestamp = {2009-04-23T12:20:35.000+0200}, title = {K-Optimal Rule Discovery}, url = {http://dx.doi.org/10.1007/s10618-005-0255-4}, volume = 10, year = 2005 }