Constraint Based Action Rule Discovery with Single Classification Rules

Abstract

Action rules can be seen as an answer to the question: what one can do with results of data mining and knowledge discovery? Some applications include: medical field, e-commerce, market basket analysis, customer satisfaction, and risk analysis. Action rules are 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 suggest an 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). With each object a number of actionable strategies can be associated and each one of them may lead to different expectations and the same to different re-classifications of objects. In this paper we will focus on a new strategy of constructing action rules directly from single classification rules instead of pairs of classification rules. It presents a gain on the simplicity of the method of action rules construction, as well as on its time complexity. We present A*-type heuristic strategy for discovering only interesting action rules, which satisfy user-defined constraints such as: feasibility, maximal cost, and minimal confidence. We, therefore, propose a new method for fast discovery of interesting action rules.