Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Action rules mining: Research Articles
International Journal of Intelligent Systems - Knowledge Discovery: Dedicated to Jan M. Żytkow
Mining for Interesting Action Rules
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Discovery of extended action rules
Discovery of extended action rules
Mining action rules from scratch
Expert Systems with Applications: An International Journal
ARAS: action rules discovery based on agglomerative strategy
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
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
Mining Non-redundant Reclassification Rules
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Action Rules and the GUHA Method: Preliminary Considerations and Results
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
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It is highly expected that knowledge discovery and data mining (KDD) methods can extract useful and understandable knowledge from large amount of data. Action rule mining presents an approach to automatically construct relevantly useful and understandable strategies by comparing the profiles of two sets of targeted objects -- those that are desirable and those that are undesirable. The discovered knowledge provides an insight of how relationships should be managed so that objects of low performance can be improved. Traditionally, it was constructed from one or two classification rules. The quality and quantity of such Action Rules depend on adopted classification methods. In this paper, we present StrategyGenerator, a new algorithm for constructing a complete set of Action Rules which satisfies specified constraints. This algorithm does not require prior extraction of classification rules. Action rules are generated directly from a database.