Advertisement

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)

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 (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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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
  2. 2.
    Hilderman, R.J., Hamilton, H.J.: Knowledge Discovery and Measures of Interest. Kluwer Academic Publishers, Dordrecht (2001)CrossRefMATHGoogle Scholar
  3. 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
  4. 4.
    Pawlak, Z.: Information systems - theoretical foundations. Information Systems Journal 6, 205–218 (1991)CrossRefMATHGoogle Scholar
  5. 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
  6. 6.
    Raś, Z., Wieczorkowska, A.: Action Rules: how to increase profit of a company. In: Zighed, A.D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 587–592. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 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
  8. 8.
    Raś, Z.W., Dardzińska, A.: Action rules discovery, a new simplified strategy. In: Esposito, F., et al. (eds.) ISMIS 2006. LNCS (LNAI), vol. 4203, pp. 445–453. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Tsay, L.-S., Raś, Z.W.: Action rules discovery system DEAR, method and experiments. Journal of Experimental and Theoretical Artificial Intelligence 17(1-2), 119–128 (2005)CrossRefMATHGoogle Scholar

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

Personalised recommendations