Constraint Based Action Rule Discovery with Single Classification Rules

  • Angelina Tzacheva
  • Zbigniew W. Raś
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4482)


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 (αβ) occurs in objects represented as tuples from a database so does the expectation (φψ). 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.


Association Rule Customer Satisfaction Knowledge Discovery Average Cost Actionable Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Angelina Tzacheva
    • 1
  • Zbigniew W. Raś
    • 2
    • 3
  1. 1.University of South Carolina Upstate, Department of Informatics, Spartanburg, SC 29303USA
  2. 2.University of North Carolina at Charlotte, Department of Computer Science, Charlotte, N.C. 28223USA
  3. 3.Polish-Japanese Institute of Information Technology, 02-008 WarsawPoland

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