Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying non-actionable association rules
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Microeconomic View of Data Mining
Data Mining and Knowledge Discovery
Profit Mining: From Patterns to Actions
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Item selection by "hub-authority" profit ranking
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Postprocessing Decision Trees to Extract Actionable Knowledge
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A decision-theoretic approach to data mining
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Expert Systems with Applications: An International Journal
Action Rules Discovery without Pre-existing Classification Rules
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Mining Non-redundant Reclassification Rules
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Computers and Industrial Engineering
Action Rules Discovery Based on Tree Classifiers and Meta-actions
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
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
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
An expected utility-based approach for mining action rules
Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics
Mining actionable behavioral rules
Decision Support Systems
From music to emotions and tinnitus treatment, initial study
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Pair-Based object-driven action rules
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
Mining Meta-Actions for Action Rules Reduction
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
Causality-based cost-effective action mining
Intelligent Data Analysis
| Hi-index | 12.06 |
Action rules provide hints to a business user what actions (i.e. changes within some values of flexible attributes) should be taken to improve the profitability of customers. That is, taking some actions to re-classify some customers from less desired decision class to the more desired one. However, in previous work, each action rule was constructed from two rules, extracted earlier, defining different profitability classes. In this paper, we make a first step towards formally introducing the problem of mining action rules from scratch and present formal definitions. In contrast to previous work, our formulation provides guarantee on verifying completeness and correctness of discovered action rules. In addition to formulating the problem from an inductive learning viewpoint, we provide theoretical analysis on the complexities of the problem and its variations. Furthermore, we present efficient algorithms for mining action rules from scratch. In an experimental study we demonstrate the usefulness of our techniques.