A new version of the rule induction system LERS
Fundamenta Informaticae
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PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
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Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce
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ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
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Tree-based Construction of Low-cost Action Rules
Fundamenta Informaticae
Action Rules Discovery without Pre-existing Classification Rules
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
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
Mining action rules from scratch
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ARAS: action rules discovery based on agglomerative strategy
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
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ISMIS'08 Proceedings of the 17th 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
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Mining Meta-Actions for Action Rules Reduction
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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Action rules describe possible transitions of objects from one state to another with respect to a distinguished attribute. Early research on action rule discovery usually required the extraction of classification rules before constructing any action rule. Newest algorithms discover action rules directly from a decision system. To our knowledge, all these algorithms assume that all attributes are symbolic or require prior discretization of all numerical attributes. This paper presents a new approach for generating action rules from datasets with numerical attributes by incorporating a tree classifier and a pruning step based on meta-actions. Meta-actions are seen as a higher-level knowledge (provided by experts) about correlations between different attributes.