Global action rules in distributed knowledge systems
Fundamenta Informaticae
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
Action rules mining: Research Articles
International Journal of Intelligent Systems - Knowledge Discovery: Dedicated to Jan M. Żytkow
Action Rules Discovery without Pre-existing Classification Rules
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
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
ARAS: action rules discovery based on agglomerative strategy
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Action rule extraction from a decision table: ARED
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
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A new strategy for discovering action rules (or interventions) is presented in this paper. The current methods [14], [12], [8] require to discover classification rules before any action rule can be constructed from them. Several definitions of action rules [8], [13], [9], [3] have been proposed. They differ in the generality of their classification parts but they are always constructed from certain pairs of classification rules. Our new strategy defines the classification part of an action rule in a unique way. Also, action rules are constructed from single classification rules. We show how to compute their confidence and support. Action rules are used to reclassify objects. In this paper, we propose a method for measuring the level of reclassification freedom for objects in a decision system.