%0 Conference Paper %1 Webb06a %A Webb, G.I. %B Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2006) %C New York %D 2006 %E Ungar, L. %E Craven, M. %E Gunopulos, D. %E Eliassi-Rad, T. %I The Association for Computing Machinery %K Association Discovery, OPUS, Rule discovery sound statistically %P 434 - 443 %T Discovering Significant Rules %X In many applications, association rules will only be interesting if they represent non-trivial correlations between all constituent items. Numerous techniques have been developed that seek to avoid false discoveries. However, while all provide useful solutions to aspects of this problem, none provides a generic solution that is both flexible enough to accommodate varying definitions of true and false discoveries and powerful enough to provide strict control over the risk of false discoveries. This paper presents generic techniques that allow definitions of true and false discoveries to be specified in terms of arbitrary statistical hypothesis tests and which provide strict control over the experimentwise risk of false discoveries.

@inproceedings{Webb06a, abstract = {In many applications, association rules will only be interesting if they represent non-trivial correlations between all constituent items. Numerous techniques have been developed that seek to avoid false discoveries. However, while all provide useful solutions to aspects of this problem, none provides a generic solution that is both flexible enough to accommodate varying definitions of true and false discoveries and powerful enough to provide strict control over the risk of false discoveries. This paper presents generic techniques that allow definitions of true and false discoveries to be specified in terms of arbitrary statistical hypothesis tests and which provide strict control over the experimentwise risk of false discoveries.}, added-at = {2016-03-20T05:42:04.000+0100}, address = {New York}, author = {Webb, G.I.}, biburl = {https://www.bibsonomy.org/bibtex/27be458175e285a327901c29ee5819948/giwebb}, booktitle = {Proceedings of the Twelfth {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining (KDD-2006)}, editor = {Ungar, L. and Craven, M. and Gunopulos, D. and Eliassi-Rad, T.}, interhash = {775c0a74d7301da332cb62e2b32532e3}, intrahash = {7be458175e285a327901c29ee5819948}, keywords = {Association Discovery, OPUS, Rule discovery sound statistically}, location = {Philadelphia, PA}, pages = {434 - 443}, publisher = {The Association for Computing Machinery}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {Discovering Significant Rules}, year = 2006 }