What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Case Bases for Action Recommendation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Optimal Actions for Profitable CRM
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Action rules mining: Research Articles
International Journal of Intelligent Systems - Knowledge Discovery: Dedicated to Jan M. Żytkow
Mining for Interesting Action Rules
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Tree-based Construction of Low-cost Action Rules
Fundamenta Informaticae
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Constraint Based Action Rule Discovery with Single Classification Rules
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Mining action rules from scratch
Expert Systems with Applications: An International Journal
ARAS: action rules discovery based on agglomerative strategy
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Action rule discovery from incomplete data
Knowledge and Information Systems
Action rules discovery, a new simplified strategy
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
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One of the central issues in data mining community is to make the mined patterns actionable. Action rules are those actionable patterns, which provide hints to a user what actions (i.e., changes within some values of flexible attributes) should be taken to reclassify some objects from an undesired decision class to a desired one. Both changing the value of a flexible attribute and the corresponding change of the value of a decision attribute may incur cost (negative utility) or bring benefit (positive utility) for the user. Obviously, the user is more interested in the rules which are expected to bring higher utility. In this paper, we formally define the expected utility of an action rule for measuring its interestingness. Our definitions explicitly state the problem of mining action rules as a search problem in a framework of support and expected utility. We also propose an effective algorithm for mining action rules with higher expected utilities. Our experiment shows the usefulness of the proposed approach.