%0 Journal Article %1 Webb11 %A Webb, G.I. %D 2011 %I Wiley %J WIREs Data Mining and Knowledge Discovery %K Association Discovery, Rule discovery sound statistically %N 3 %P 183-192 %T Filtered-top-k Association Discovery %V 1 %X Association mining has been one of the most intensively researched areas of data mining. However, direct uptake of the resulting technologies has been relatively low. This paper examines some of the reasons why the dominant paradigms in association mining have not lived up to their promise, and argues that a powerful alternative is provided by top-k techniques coupled with appropriate statistical and other filtering.

@article{Webb11, abstract = {Association mining has been one of the most intensively researched areas of data mining. However, direct uptake of the resulting technologies has been relatively low. This paper examines some of the reasons why the dominant paradigms in association mining have not lived up to their promise, and argues that a powerful alternative is provided by top-k techniques coupled with appropriate statistical and other filtering.}, added-at = {2016-03-20T05:42:04.000+0100}, author = {Webb, G.I.}, biburl = {https://www.bibsonomy.org/bibtex/2b2635cf9bce43c66a22481cc49dbf5cd/giwebb}, interhash = {a25641e8876cff4a8f1eed4730e5bbc0}, intrahash = {b2635cf9bce43c66a22481cc49dbf5cd}, journal = {WIREs Data Mining and Knowledge Discovery}, keywords = {Association Discovery, Rule discovery sound statistically}, number = 3, pages = {183-192}, publisher = {Wiley}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {Filtered-top-k Association Discovery}, volume = 1, year = 2011 }