Identifying non-actionable association rules
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Action-Rules: How to Increase Profit of a Company
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Mining Optimal Actions for Profitable CRM
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Postprocessing Decision Trees to Extract Actionable Knowledge
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Mining action rules from scratch
Expert Systems with Applications: An International Journal
Action rule extraction from a decision table: ARED
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Discovering the concise set of actionable patterns
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Combined association rule mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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The increased competition faced by today's companies can wield data mining tools to extract actionable knowledge and then use it as a weapon to outmaneuver competitors and boost revenue. Mining reclassification rules is a way to model actionable patterns directly from a given data set. The previous work on reclassification rule mining has shown that they are effective when variables are weakly correlated. However, when the data set is correlated, some redundant rules are in the result set. This problem becomes critical for discovering rules in correlated data which may have long frequent factor-sets. In this paper, we investigate properties of reclassification rules and offer a new method to discovery a set of non-redundant reclassification rules without information loss.