%0 Conference Paper %1 WebbZhang02 %A Webb, G. I. %A Zhang, S. %B Proceedings of the First International NAISO Congress on Autonomous Intelligent Systems (ICAIS 2002) %C Canada/The Netherlands %D 2002 %I NAISO Academic Press %K Association Discovery OPUS, Rule %T Removing Trivial Associations in Association Rule Discovery %X Association rule discovery has become one of the most widely applied data mining strategies. Techniques for association rule discovery have been dominated by the frequent itemset strategy as exemplified by the Apriori algorithm. One limitation of this approach is that it provides little opportunity to detect and remove association rules on the basis of relationships between rules. As a result, the association rules discovered are frequently swamped with large numbers of spurious rules that are of little interest to the user. This paper presents association rule discovery techniques that can detect and discard one form of spurious association rule: trivial associations.

@inproceedings{WebbZhang02, abstract = {Association rule discovery has become one of the most widely applied data mining strategies. Techniques for association rule discovery have been dominated by the frequent itemset strategy as exemplified by the Apriori algorithm. One limitation of this approach is that it provides little opportunity to detect and remove association rules on the basis of relationships between rules. As a result, the association rules discovered are frequently swamped with large numbers of spurious rules that are of little interest to the user. This paper presents association rule discovery techniques that can detect and discard one form of spurious association rule: trivial associations.}, added-at = {2016-03-20T05:42:04.000+0100}, address = {Canada/The Netherlands}, audit-trail = {Pre-publication PDF posted}, author = {Webb, G. I. and Zhang, S.}, biburl = {https://www.bibsonomy.org/bibtex/2387b05d9464eae58d8ffa19c1cb23543/giwebb}, booktitle = {Proceedings of the First International NAISO Congress on Autonomous Intelligent Systems (ICAIS 2002)}, interhash = {2b0d052bef183c977b24ade7182df71f}, intrahash = {387b05d9464eae58d8ffa19c1cb23543}, keywords = {Association Discovery OPUS, Rule}, location = {Geelong, Australia}, publisher = {NAISO Academic Press}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {Removing Trivial Associations in Association Rule Discovery}, year = 2002 }