ARAS: Action Rules Discovery Based on Agglomerative Strategy

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 = [(ω) ∧ (α → β)] ⇒ [φ → ψ], 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 [5]. ARAS system is more simple than DEAR and its time complexity is also lower.