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

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Book cover Modeling Decisions for Artificial Intelligence (MDAI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

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References

  1. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)

    MATH  Google Scholar 

  2. Pawlak, Z.: Some Issues on Rough Sets. In: Transactions on Rough Sets, vol. 1, pp. 1–58. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  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. 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. Tsumoto, S.: Knowledge Discovery in Clinical Databases and Evaluation of Discovered Knowledge in Outpatient Clinic. Information Sciences 124, 125–137 (2000)

    Article  Google Scholar 

  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. 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. Grzymala-Busse, J.: A New Version of the Rule Induction System LERS. Fundamenta Informaticae 31, 27–39 (1997)

    MATH  Google Scholar 

  9. Ziarko, W.: Variable Precision Rough Set Model. Journal of Computer and System Sciences 46, 39–59 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  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. Rough Set Software. Bulletin of Int’l. Rough Set Society 2, 15–46 (1998)

    Google Scholar 

  12. Orowska, E., Pawlak, Z.: Representation of Nondeterministic Information. Theoretical Computer Science 29, 27–39 (1984)

    Article  MathSciNet  Google Scholar 

  13. Orowska, E. (ed.): Incomplete Information: Rough Set Analysis. Physica, New York (1998)

    Google Scholar 

  14. Lipski, W.: On Semantic Issues Connected with Incomplete Information Data Base. ACM Trans. DBS 4, 269–296 (1979)

    Google Scholar 

  15. Lipski, W.: On Databases with Incomplete Information. Journal of the ACM 28, 41–70 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  16. Nakamura, A.: A Rough Logic based on Incomplete Information and Its Application. Intl. Journal of Approximate Reasoning 15, 367–378 (1996)

    Article  MATH  Google Scholar 

  17. Codd, E.: A Relational Model of Data for Large Shared Data Banks. Communication of the ACM 13, 377–387 (1970)

    Article  MATH  Google Scholar 

  18. Nakata, M., Miyamoto, S.: Databases with Non-deterministic Information. Bulletin of Intl. Rough Set Society 7, 15–21 (2003)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  21. Sakai, H.: Effective Procedures for Handling Possible Equivalence Relations in Nondeterministic Information Systems. Fundamenta Informaticae 48, 343–362 (2001)

    MathSciNet  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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. 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. 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. 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)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Aided Science, Faculty of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu, 804, Japan

    Hiroshi Sakai

  2. Division of Systems and Information Engineering, Graduate School of Engineering, Hokkaido University, Kita-ku, Sapporo, 060, Japan

    Tetsuya Murai

  3. Faculty of Management and Information Science, Josai International University, Gumyo, Togane, Chiba, 283, Japan

    Michinori Nakata

Authors
  1. Hiroshi Sakai
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  2. Tetsuya Murai
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  3. Michinori Nakata
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Editor information

Editors and Affiliations

  1. IIIA, Artificial Intelligence Research Institute CSIC, Spanish National Research Council, Campus UAB s/n, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

  2. Toho Gakuen, 3-1-10 Naka, Kunitachi, 186-0004, Tokyo, Japan

    Yasuo Narukawa

  3. Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 305-8573, Ibaraki, Japan

    Sadaaki Miyamoto

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© 2005 Springer-Verlag Berlin Heidelberg

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Sakai, H., Murai, T., Nakata, M. (2005). On a Tool for Rough Non-deterministic Information Analysis and Its Perspective for Handling Numerical Data. In: Torra, V., Narukawa, Y., Miyamoto, S. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2005. Lecture Notes in Computer Science(), vol 3558. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526018_21

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  • DOI: https://doi.org/10.1007/11526018_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27871-9

  • Online ISBN: 978-3-540-31883-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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