Constraint Based Action Rule Discovery with Single Classification Rules
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.
KeywordsAssociation Rule Customer Satisfaction Knowledge Discovery Average Cost Actionable Strategy
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- 1.Adomavicius, G., Tuzhilin, A.: Discovery of actionable patterns in databases: the action hierarchy approach. In: Proceedings of KDD’97 Conference, Newport Beach, CA, AAAI Press, Menlo Park (1997)Google Scholar
- 3.Greco, S., et al.: Measuring expected effects of interventions based on decision rules. Journal of Experimental and Theoretical Artificial Intelligence 17(1-2) (2005)Google Scholar
- 5.Silberschatz, A., Tuzhilin, A.: On subjective measures of interestingness in knowledge discovery. In: Proceedings of KDD’95 Conference, AAAI Press, Menlo Park (1995)Google Scholar
- 7.Raś, Z.W., Tzacheva, A., Tsay, L.-S.: Action rules. In: Wang, J. (ed.) Encyclopedia of Data Warehousing and Mining, pp. 1–5. Idea Group Inc, Hershey (2005)Google Scholar