Rule Quality Measures in Creation and Reduction of Data Rule Models

  • Marek Sikora
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4259)


Properties of several rule quality measures are characterized in the paper. Possibilities of their application in algorithms of rules induction and reduction are presented. Influence of replacing rules accuracy with the Bayesian confirmation measure has been tested.


Quality Measure Rule Induction Decision Class Conditional Attribute Rule Ranking 
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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  1. 1.
    Ågotnes, T., Komorowski, J., Løken, T.: Taming Large Rule Models in Rough Set Approaches. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 193–203. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  2. 2.
    An, A., Cercone, N.: Rule quality measures for rule induction systems: Description and evaluation. Computational Intelligence 17(3) (2001)Google Scholar
  3. 3.
    Bayardo, R.J., Agrawal, R.: Mining the most interesting rules. In: Proc. of the Fifth ACM-SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, pp. 145–154 (1999)Google Scholar
  4. 4.
    Bruha, I.: Quality of Decision Rules: Definitions and Classification Schemes for Multiple Rules. In: Nakhaeizadeh, G., Taylor, C.C. (eds.) Machine Learning and Statistics, The Interface. John Wiley and Sons, Chichester (1997)Google Scholar
  5. 5.
    Brzezińska, I., Słowiński, R.: Monotonicity of Bayesian confirmation measure in rule support and confidence. In: Proc. of the AI-METH, Gliwice, November 16-18 (2005)Google Scholar
  6. 6.
    Grzymała-Busse, J.: LERS – a system for learning from examples based on rough sets. In: Słowiński, R. (ed.) Intelligent Decision Support, pp. 3–18. Kluwer, Dordrecht (1992)Google Scholar
  7. 7.
    Greco, S., Pawlak, Z., Slowinski, R.: Can Bayesian confirmation measures be useful for rough set decision rules. Engineering Applications of Artificial Intelligence 17, 345–361 (2004)CrossRefGoogle Scholar
  8. 8.
    Greco, S., Matarazzo, B., Słowiński, R.: Rough Membership and Bayesian Confirmation Measures for Parameterized Rough Sets. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 314–324. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Kaufman, K.A., Michalski, R.S.: Learning in Inconsistent World, Rule Selection in STAR/AQ18, Machine Learning and Inference Laboratory Report P99-2 (1999)Google Scholar
  10. 10.
  11. 11.
    Nguyen, H.S., Nguyen, S.H.: Some Efficient Algorithms for Rough Set Methods. In: Proceedings of the Sixth International Conference, Information Processing and Management of Uncertainty in Knowledge-Based Systems, Granada, Spain, vol. 2, pp. 1451–1456 (1996)Google Scholar
  12. 12.
    Pawlak, Z.: Rough Sets. Theoretical aspects of reasoning about data. Kluwer, Dordrecht (1991)MATHGoogle Scholar
  13. 13.
    Sikora, M.: An algorithm for generalization of decision rules by joining. Foundation on Computing and Decision Sciences 30(3) (2005)Google Scholar
  14. 14.
    Sikora, M.: Approximate decision rules induction algorithm using rough sets and rule-related quality measures. Archives of Theoretical and Applied Informatics (4) (2004)Google Scholar
  15. 15.
    Sikora, M.: Filtering of decision rules sets using rules quality measures. Studia Informatica, Gliwice 46(4) (2001)Google Scholar
  16. 16.
    Sikora, M., Proksa, P.: Induction of decision and association rules for knowledge discovery in industrial databases. In: DM-IEEE, Workshop – Alternative Techniques of Data Mining, Brighton, November 01-04 (2004)Google Scholar
  17. 17.
    Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27, 245–253 (1996)MATHMathSciNetGoogle Scholar
  18. 18.
    Stefanowski, J.: Rules induction algorithms in knowledge discovery. Habilitation thesis. Poznan University of Technology, Dissertations No. 361 (2001)Google Scholar
  19. 19.
    Zhong, N., Skowron, A.: A rough set-based knowledge discovery process. Int. J. Appl. Math. Comput. Sci. 11(3), 603–619 (2001)MATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marek Sikora
    • 1
  1. 1.Institute of Computer SciencesSilesian University of TechnologyGliwicePoland

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