Generation of rules from incomplete information systems

  • Marzena Kryszkiewicz
Parallel Session 3a
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1263)

Abstract

In the paper we define certain and possible rules in an incomplete information system as certain/possible ones in every completion of the initial system. The careful examination of the dependencies between an incomplete system and its completions allow us to state that it is feasible to generate all certain rules and some important class of possible rules directly from the incomplete information system. Space complexity of the proposed method of rules' generation is linear with regard to the number of objects in the initial system.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Kononenko I., Bratko I., Roskar E., Experiments in Automatic Learning of Medical Diagnostic Rules, Technical Report, Jozef Stefan Institute, Ljubljana, Yugoslavia, 1984.Google Scholar
  2. [2]
    Quinlan J.R., Induction of Decision Trees, in Readings in Machine Learning, Shavlik J.W., Dietterich T.G. (ed.), 1990, Morgan Kaufmann Publishers, pp. 57–69.Google Scholar
  3. [3]
    Chmielewski M.R., Grzymala-Busse J.W., Peterson N.W., Than S., The Rule Induction System LERS — A Version for Personal Computers, Foundations of Computing and Decision Sciences, Vol. 18 No. 3–4, 1993, pp. 181–212.Google Scholar
  4. [4]
    Kryszkiewicz M., Rules in Incomplete Information Systems, submitted to Information Sciences.Google Scholar
  5. [5]
    Kryszkiewicz M., Rules in Incomplete Information Systems, submitted to Information Sciences Proceedings from the Third Joint Conference on Information Sciences, North Carolina, USA, March 2–5, 1997, to appear.Google Scholar
  6. [5a]
    Kryszkiewicz M., Rough Set Approach to Incomplete Information Systems, Proceedings of Second Annual Joint Conference on Information Sciences, Wrightsville Beach, North Carolina, USA, 28 September–1 October 1995, pp. 194–197.Google Scholar
  7. [6]
    Slowinski R., Stefanowski J., Rough-Set Reasoning about Uncertain Data, in Fundamenta Informaticae, Vol. 27, No. 2–3, 1996, pp. 229–244.Google Scholar
  8. [7]
    Skowron A., Rauszer C., The Discernibility Matrices and Functions in Information Systems, in Intelligent Decision Support: Handbook of Applications and Advances of Rough Sets Theory, Slowinski R. (ed.), 1992, Kluwer Academic Publisher, pp. 331–362.Google Scholar
  9. [8]
    Lipski W.J., On Semantic Issues Connected with Incomplete Information Databases, ACM Transaction on Databases Systems, 4, 1979, pp. 262–296.CrossRefGoogle Scholar
  10. [9]
    Nguyen S.H., Nguyen H.S., Some Efficient Algorithms for Rough Set Methods, in Proceedings of Sixth Intl. Conference IPMU '96, July 1–5, Granada, Espana, Vol. 3, 1996, pp. 1451–1456.Google Scholar

Copyright information

© Springer-Verlag 1997

Authors and Affiliations

  • Marzena Kryszkiewicz
    • 1
  1. 1.Institute of Computer ScienceWarsaw University of TechnologyWarsawPoland

Personalised recommendations