On a Rough Sets Based Data Mining Tool in Prolog: An Overview

  • Hiroshi Sakai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4369)


RoughNon-deterministicInformation Analysis (RNIA) is a framework for handling rough sets based concepts, which are defined in not only DISs (DeterministicInformation Systems) but also NISs (Non-deterministicInformation Systems), on computers. RNIA is also recognized as a framework of data mining from uncertain tables. This paper focuses on programs in prolog, and briefly surveys a software tool for RNIA.


Equivalence Class Equivalence Relation Incomplete Information Negative Selection Globally Consistent 
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  1. 1.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)MATHGoogle Scholar
  2. 2.
    Pawlak, Z.: Some Issues on Rough Sets. Transactions on Rough Sets, Int’l. Rough Set Society 1, 1–58 (2004)MathSciNetGoogle Scholar
  3. 3.
    Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough Sets: a tutorial. In: Rough Fuzzy Hybridization, pp. 3–98. Springer, Heidelberg (1999)Google Scholar
  4. 4.
    Nakamura, A., Tsumoto, S., Tanaka, H., Kobayashi, S.: Rough Set Theory and Its Applications. Journal of Japanese Society for AI 11(2), 209–215 (1996)Google Scholar
  5. 5.
    Polkowski, L., Skowron, A. (eds.): Rough Sets in Knowledge Discovery 1. Studies in Fuzziness and Soft Computing, vol. 18. Physica-Verlag (1998)Google Scholar
  6. 6.
    Polkowski, L., Skowron, A. (eds.): Rough Sets in Knowledge Discovery 2. Studies in Fuzziness and Soft Computing, vol. 19. Physica-Verlag (1998)Google Scholar
  7. 7.
    Grzymala-Busse, J.: A New Version of the Rule Induction System LERS. Fundamenta Informaticae 31, 27–39 (1997)MATHGoogle Scholar
  8. 8.
    Tsumoto, S.: Knowledge Discovery in Clinical Databases and Evaluation of Discovered Knowledge in Outpatient Clinic. Information Sciences 124, 125–137 (2000)CrossRefGoogle Scholar
  9. 9.
    Rough Set Software. Bulletin of Int’l. Rough Set Society 2, 15–46 (1998)Google Scholar
  10. 10.
    Orłowska, E., Pawlak, Z.: Representation of Nondeterministic Information. Theoretical Computer Science 29, 27–39 (1984)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Orłowska, E. (ed.): Incomplete Information: Rough Set Analysis. Physica-Verlag (1998)Google Scholar
  12. 12.
    Demri, S., Orłowska, E.: Incomplete Information: Structure, Inference, Complexity, Monographs in Theoretical Computer Science. Springer, Heidelberg (2002)Google Scholar
  13. 13.
    Lipski, W.: On Semantic Issues Connected with Incomplete Information Data Base. ACM Trans. DBS 4, 269–296 (1979)Google Scholar
  14. 14.
    Lipski, W.: On Databases with Incomplete Information. Journal of the ACM 28, 41–70 (1981)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Nakamura, A.: A Rough Logic based on Incomplete Information and Its Application. Int’l. Journal of Approximate Reasoning 15, 367–378 (1996)MATHCrossRefGoogle Scholar
  16. 16.
    Kryszkiewicz, M.: Rules in Incomplete Information Systems. Information Sciences 113, 271–292 (1999)MATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Nakata, M., Miyamoto, S.: Databases with Non-deterministic Information. Bulletin of Int’l. Rough Set Society 7, 15–21 (2003)Google Scholar
  18. 18.
    Sakai, H., Okuma, A.: An Algorithm for Finding Equivalence Relations from Tables with Non-deterministic Information. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS, vol. 1711, pp. 64–73. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  19. 19.
    Sakai, H.: Effective Procedures for Handling Possible Equivalence Relations in Non-deterministic Information Systems. Fundamenta Informaticae 48, 343–362 (2001)MathSciNetGoogle Scholar
  20. 20.
    Sakai, H.: Effective Procedures for Data Dependencies in Information Systems. In: Rough Set Theory and Granular Computing. Studies in Fuzziness and Soft Computing, vol. 125, pp. 167–176. Springer, Heidelberg (2003)Google Scholar
  21. 21.
    Sakai, H., Okuma, A.: Basic Algorithms and Tools for Rough Non-deterministic Information Analysis. Transactions on Rough Sets, Int’l. Rough Set Society 1, 209–231 (2004)Google Scholar
  22. 22.
    Sakai, H., Nakata, M.: Discernibility Functions and Minimal Rules in Non-deterministic Information Systems. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 254–264. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  23. 23.
    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

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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hiroshi Sakai
    • 1
  1. 1.Department of Mathematics and Computer Aided ScienceFaculty of Engineering, Kyushu Institute of TechnologyTobata, KitakyushuJapan

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