A new version of the rule induction system LERS
Fundamenta Informaticae
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Extension of the HEPAR II Model to Multiple-Disorder Diagnosis
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
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International Journal of Intelligent Systems - Knowledge Discovery: Dedicated to Jan M. Żytkow
Action rules discovery, a new simplified strategy
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Action rules discovery system DEAR_3
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Action Rules Discovery without Pre-existing Classification Rules
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
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
Knowledge management techniques for analysis of clinical databases
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
An expected utility-based approach for mining action rules
Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics
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
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Action rules can be seen as logical terms describing knowledge about possible actions associated with objects which is hidden in a decision system. Classical strategy for discovering them from a database requires prior extraction of classification rules which next are evaluated pair by pair with a goal to build a strategy of action based on condition features in order to get a desired effect on a decision feature. An actionable strategy is represented as a term r = [(ω) Λ (α → β)] ⇒ [φ → ψ], where ω, α, β, φ, and ψ are descriptions of objects or events. The term r states that when the fixed condition ω is satisfied and the changeable behavior (α → β) occurs in objects represented as tuples from a database so does the expectation (φ → ψ). This paper proposes a new strategy, called ARAS, for constructing action rules with the main module resembling LERS [6]. ARAS system is more simple than DEAR and its time complexity is also lower.