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
Tree-based Construction of Low-cost Action Rules
Fundamenta Informaticae
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
Action Rules Discovery Based on Tree Classifiers and Meta-actions
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
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
Discovering the concise set of actionable patterns
ISMIS'08 Proceedings of the 17th 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
Causality-based cost-effective action mining
Intelligent Data Analysis
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E-action rules, introduced in [8], represent actionability knowledge hidden in a decision system. They enhance action rules [3] and extended action rules [4], [6], [7] by assuming that data can be either symbolic or nominal. Several efficient strategies for mining e-action rules have been developed [6], [7], [5], and [8]. All of them assume that data are complete. Clearly, this constraint has to be relaxed since information about attribute values for some objects can be missing or represented as multi-values. To solve this problem, we present DEAR_3 which is an e-action rule generating algorithm. It has three major improvements in comparison to DEAR_2: handling data with missing attribute values and uncertain attribute values, and pruning outliers at its earlier stage.