Knowledge actionability: satisfying technical and business interestingness
International Journal of Business Intelligence and Data Mining
Tree-based Construction of Low-cost Action Rules
Fundamenta Informaticae
Discovering Action Rules That Are Highly Achievable from Massive Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Towards Business Interestingness in Actionable Knowledge Discovery
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
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
Discovering the concise set of actionable patterns
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach
Journal of Intelligent Information Systems
Action rules discovery, a new simplified strategy
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Cooperative discovery of interesting action rules
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
An expected utility-based approach for mining action rules
Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics
Tree-based Construction of Low-cost Action Rules
Fundamenta Informaticae
Mining actionable behavioral rules
Decision Support Systems
An agent model for analysis of human performance quality
Web Intelligence and Agent Systems
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Action rules assume that attributes in a database are divided into two groups: stable and flexible. In general, an action rule can be constructed from two rules extracted earlier from the same database. Furthermore, we assume that these two rules describe two different decision classes and our goal is to reclassify objects from one of these classes into the other one. Flexible attributes are essential in achieving that goal because they provide a tool for making hints to a user about what changes within some values of flexible attributes are needed for a given group of objects to reclassify them into a new decision class. A new subclass of attributes called semi-stable attributes is introduced. Semi-stable attributes are typically a function of time and undergo deterministic changes (e.g., attribute age or height). So, the set of conditional attributes is partitioned into stable, semi-stable, and flexible. Depending on the semantics of attributes, some semi-stable attributes can be treated as flexible and the same new action rules can be constructed. These new action rules are usually built to replace some existing action rules whose confidence is too low to be of any interest to a user. The confidence of new action rules is always higher than the confidence of rules they replace. Additionally, the notion of the cost and feasibility of an action rule is introduced in this article. A heuristic strategy for constructing feasible action rules that have high confidence and possibly the lowest cost is proposed. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 719–736, 2005.