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A Prototype System for Rule Generation in Lipski’s Incomplete Information Databases

  • Hiroshi Sakai
  • Michinori Nakata
  • Dominik Ślęzak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6743)

Abstract

This paper advances rule generation in Lipski’s incomplete information databases, and develops a software tool for rule generation. We focus on three kinds of information incompleteness. The first is non-deterministic information, the second is missing values, and the third is intervals. For intervals, we introduce the concept of a resolution. Three kinds of information incompleteness are uniformly handled by NIS-Apriori algorithm. An overview of a prototype system in Prolog is presented.

Keywords

Lipski’s incomplete information databases Rule generation Apriori algorithm Rough sets Prolog 

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References

  1. 1.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. of VLDB, pp. 487–499 (1994)Google Scholar
  2. 2.
    Dembczyński, K., Greco, S., Słowiński, R.: Rough Set Approach to Multiple Criteria Classification with Imprecise Evaluations and Assignments. European J. Operational Research 198, 626–636 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Grzymała-Busse, J.: Data with Missing Attribute Values: Generalization of Indiscernibility Relation and Rule Induction. Transactions on Rough Sets 1, 78–95 (2004)zbMATHGoogle Scholar
  4. 4.
    Kryszkiewicz, M.: Rules in Incomplete Information Systems. Information Sciences 113, 271–292 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Lipski, W.: On Semantic Issues Connected with Incomplete Information Data Base. ACM Trans. DBS. 4, 269–296 (1979)Google Scholar
  6. 6.
    Lipski, W.: On Databases with Incomplete Information. Journal of the ACM 28, 41–70 (1981)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Orłowska, E., Pawlak, Z.: Representation of Nondeterministic Information. Theoretical Computer Science 29, 27–39 (1984)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Pawlak, Z.: Rough Sets. Kluwer Academic Publishers, Dordrecht (1991)CrossRefzbMATHGoogle Scholar
  9. 9.
    Sakai, H., Okuma, A.: Basic Algorithms and Tools for Rough Non-deterministic Information Analysis. Transactions on Rough Sets 1, 209–231 (2004)zbMATHGoogle Scholar
  10. 10.
    Sakai, H., Ishibashi, R., Nakata, M.: On Rules and Apriori Algorithm in Non-deterministic Information Systems. Transactions on Rough Sets 9, 328–350 (2008)zbMATHGoogle Scholar
  11. 11.
    Sakai, H., Nakata, M., Ślęzak, D.: Rule Generation in Lipski’s Incomplete Information Databases. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS, vol. 6086, pp. 376–385. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information Systems. In: Intelligent Decision Support - Handbook of Advances and Applications of the Rough Set Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)Google Scholar
  13. 13.
    Zadeh, L.A.: Toward a Theory of Fuzzy Information Granulation and its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets and Systems 90, 111–127 (1997)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hiroshi Sakai
    • 1
  • Michinori Nakata
    • 2
  • Dominik Ślęzak
    • 3
    • 4
  1. 1.Mathematical Sciences Section, Department of Basic Sciences, Faculty of EngineeringKyushu Institute of TechnologyTobata, KitakyushuJapan
  2. 2.Faculty of Management and Information ScienceJosai International UniversityGumyo, ToganeJapan
  3. 3.Institute of MathematicsUniversity of WarsawWarsawPoland
  4. 4.Infobright Inc., PolandWarsawPoland

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