Abstract
In this paper, we propose a novel algorithm which discovers a set of action rules for converting negative examples into positive examples. Unlike conventional action rule discovery methods, our method AARUDIA (Achievable Action RUle DIscovery Algorithm) considers the effects of actions and the achievability of the class change for disk-resident data. In AARUDIA, effects of actions are specified using domain rules and the achievability is inferred with Naive Bayes classifiers. AARUDIA takes a new breadth-first search method which manages actionable literals and stable literals, and exploits the achievability to reduce the number of discovered rules. Experimental results with inflated real-world data sets are promising and demonstrate the practicality of AARUDIA.
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Suzuki, E. (2009). Discovering Action Rules That Are Highly Achievable from Massive Data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_72
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DOI: https://doi.org/10.1007/978-3-642-01307-2_72
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