Abstract
Action rules can be seen as logical terms describing knowledge about possible actions associated with objects which is hidden in a decision system. Classical strategy for discovering them from a database requires prior extraction of classification rules which next are evaluated pair by pair with a goal to build a strategy of action based on condition features in order to get a desired effect on a decision feature. An actionable strategy is represented as a term \(r = [(\omega) \wedge (\alpha \rightarrow \beta)] \Rightarrow [\phi \rightarrow \psi]\), where ω, α, β, φ, and ψ are descriptions of objects or events. The term r states that when the fixed condition ω is satisfied and the changeable behavior (α→β) occurs in objects represented as tuples from a database so does the expectation (φ→ψ). This paper proposes a new strategy, called ARAS, for constructing action rules with the main module resembling LERS [6]. ARAS system is more simple than DEAR and its time complexity is also lower.
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Raś, Z.W., Wyrzykowska, E., Wasyluk, H. (2008). ARAS: Action Rules Discovery Based on Agglomerative Strategy. In: Raś, Z.W., Tsumoto, S., Zighed, D. (eds) Mining Complex Data. MCD 2007. Lecture Notes in Computer Science(), vol 4944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68416-9_16
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DOI: https://doi.org/10.1007/978-3-540-68416-9_16
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