ARAS: Action Rules Discovery Based on Agglomerative Strategy

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.