Advertisement

On a Tool for Rough Non-deterministic Information Analysis and Its Perspective for Handling Numerical Data

  • Hiroshi Sakai
  • Tetsuya Murai
  • Michinori Nakata
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3558)

Abstract

Rough Non-deterministic Information Analysis (RNIA) is a framework for handling rough sets based concepts, which are defined in not only DeterministicInformation Systems (DISs) but also Non-deterministicInformation Systems (NISs), on computers. This paper at first reports an overview of a tool for RNIA. Then, we enhance the framework of RNIA for handling numerical data. Most of DISs and NISs implicitly consist of categorical data, and multivariate analysis seems to be employed for numerical data. Therefore, it is necessary to investigate rough sets based information analysis for numerical data, too. We introduce numerical patterns into numerical values, and define equivalence relations based on these patterns. Due to this introduction, it is possible to handle the precision of information, namely it is possible to define fine information and coarse information. These fine and coarse concepts cause more flexible information analysis, including rule generation, from numerical data.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)zbMATHGoogle Scholar
  2. 2.
    Pawlak, Z.: Some Issues on Rough Sets. In: Transactions on Rough Sets, vol. 1, pp. 1–58. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough Sets: a tutorial, 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.
    Tsumoto, S.: Knowledge Discovery in Clinical Databases and Evaluation of Discovered Knowledge in Outpatient Clinic. Information Sciences 124, 125–137 (2000)CrossRefGoogle Scholar
  6. 6.
    Polkowski, L., Skowron, A. (eds.): Rough Sets in Knowledge Discovery 1, Studies in Fuzziness and Soft Computing, vol. 18. Physica, New York (1998)Google Scholar
  7. 7.
    Polkowski, L., Skowron, A. (eds.): Rough Sets in Knowledge Discovery 2, Studies in Fuzziness and Soft Computing, vol. 19. Physica, New York (1998)Google Scholar
  8. 8.
    Grzymala-Busse, J.: A New Version of the Rule Induction System LERS. Fundamenta Informaticae 31, 27–39 (1997)zbMATHGoogle Scholar
  9. 9.
    Ziarko, W.: Variable Precision Rough Set Model. Journal of Computer and System Sciences 46, 39–59 (1993)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Zhong, N., Dong, J., Fujitsu, S., Ohsuga, S.: Soft Techniques to Rule Discovery in Data. Transactions of Information Processing Society of Japan 39, 2581–2592 (1998)Google Scholar
  11. 11.
    Rough Set Software. Bulletin of Int’l. Rough Set Society 2, 15–46 (1998)Google Scholar
  12. 12.
    Orowska, E., Pawlak, Z.: Representation of Nondeterministic Information. Theoretical Computer Science 29, 27–39 (1984)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Orowska, E. (ed.): Incomplete Information: Rough Set Analysis. Physica, New York (1998)Google Scholar
  14. 14.
    Lipski, W.: On Semantic Issues Connected with Incomplete Information Data Base. ACM Trans. DBS 4, 269–296 (1979)Google Scholar
  15. 15.
    Lipski, W.: On Databases with Incomplete Information. Journal of the ACM 28, 41–70 (1981)zbMATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Nakamura, A.: A Rough Logic based on Incomplete Information and Its Application. Intl. Journal of Approximate Reasoning 15, 367–378 (1996)zbMATHCrossRefGoogle Scholar
  17. 17.
    Codd, E.: A Relational Model of Data for Large Shared Data Banks. Communication of the ACM 13, 377–387 (1970)zbMATHCrossRefGoogle Scholar
  18. 18.
    Nakata, M., Miyamoto, S.: Databases with Non-deterministic Information. Bulletin of Intl. Rough Set Society 7, 15–21 (2003)Google Scholar
  19. 19.
    Sakai, H., Okuma, A.: Basic Algorithms and Tools for Rough Non-deterministic Information Analysis. Transactions on Rough Sets, Intl. Rough Set Society 1, 209–231 (2004)CrossRefGoogle Scholar
  20. 20.
    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 (LNAI), vol. 1711, pp. 64–72. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  21. 21.
    Sakai, H.: Effective Procedures for Handling Possible Equivalence Relations in Nondeterministic Information Systems. Fundamenta Informaticae 48, 343–362 (2001)MathSciNetGoogle Scholar
  22. 22.
    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
  23. 23.
    Sakai, H.: A Framework of Rough Sets based Rule Generation in Non-deterministic Information Systems. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 143–151. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  24. 24.
    Sakai, H.: An Interactive Tool for Generating Minimal Certain Rules in Nondeterministic Information Systems. In: Proc. International Workshop on Fuzzy Systems and Innovational Computing, Japan Society for Fuzzy Theory and Intelligent Informatics, vol. D4-2, pp. 1–6 (2004)Google Scholar
  25. 25.
    Murai, T., Resconi, G., Nakata, M., Sato, Y.: Operations of Zooming In and Out on Possible Worlds for Semantic Fields. In: Damiani, E., et al. (eds.) Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies (KES 2002), pp. 1083–1087. IOS Press, Amsterdam (2002)Google Scholar
  26. 26.
    Murai, T., Resconi, G., Nakata, M., Sato, Y.: Granular Reasoning Using Zooming In & Out. In: RSFDGrC 2003. LNCS, vol. 2639, pp. 421–424. Springer, Heidelberg (2003)Google Scholar
  27. 27.
    Yao, Y., Liau, C., Zhong, N.: Granular Computing Based on Rough Sets, Quotient Space Theory, and Belief Functions. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 152–159. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hiroshi Sakai
    • 1
  • Tetsuya Murai
    • 2
  • Michinori Nakata
    • 3
  1. 1.Department of Mathematics and Computer Aided Science, Faculty of EngineeringKyushu Institute of TechnologyTobata, KitakyushuJapan
  2. 2.Division of Systems and Information Engineering, Graduate School of EngineeringHokkaido UniversityKita-ku, SapporoJapan
  3. 3.Faculty of Management and Information ScienceJosai International UniversityTogane, ChibaJapan

Personalised recommendations