Skip to main content

Granular Computing

Structures, Representations, and Applications

  • Conference paper
  • First Online:
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2639))

Abstract

The structure and representation theories of (crisp/fuzzy) granulations are presented. The results are applied to data mining, fuzzy control, security and etc.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Tsau Young Lin, Yiyu Yao, and Lotfi Zadeh, Data Mining, Rough Sets and Granular Computing, Physica Verlag, Heidelberg, 2002

    MATH  Google Scholar 

  2. Witold Pedrycz, Granular Computing, Physica Verlag, Heidelberg, 2002

    Google Scholar 

  3. Ning Zhong, Andrzej Skowron, Setsuo Ohsuga (eds) New Directions in Roughsets, Data Mining, and Granular-Soft Computing Springer Verlag, LNCS 1711, 1999

    Google Scholar 

  4. Hector Garcia-Molina, Jeffrey D. Ullman, Jennifer Widom, Database Systems — The Complete Book, Prentice Hall, 2002, ISBN 0-13-031-995-3

    Google Scholar 

  5. Date, C. J.: Introduction to Database Systems. 3rd, 7th edn. Addison-Wesley, Reading, Massachusetts (1981, 2000).

    MATH  Google Scholar 

  6. Z. Pawlak, Rough sets. International Journal of Information and Computer Science 11, 1982, pp. 341–356.

    Article  MATH  MathSciNet  Google Scholar 

  7. T. Y. Lin, “Deductive Data Mining”, In: the Proceeding of International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFD-GrC2003), Chongqing, China, May 26–29, 2003, this Volume.

    Google Scholar 

  8. T. Y. Lin, “Attribute (Feature) Completion — The Theory of Attributes from Data Mining Prospect,” in: Proceedings of International Conference on Data Mining, Maebashi, Japan, Dec 9–12, 2002, pp. 282–289

    Google Scholar 

  9. T. Y. Lin, “Placing the Chinese Walls on the Boundary of Conflicts — Analysis of Symmetric Binary Relations.” In: Proceedings of the International Conference on Computer Software and Applications, Oxford, England, Aug 26–29, 2002, pp. 966–971

    Google Scholar 

  10. T. Y. Lin, “Database Mining on Derived Attributes — Granular and Rough Computing Approach,” in: Rough sets and Current Trends in Computing, Alpigini, Peters Skowron, Zhong (eds), Lecture Notes in Artificial Intelligence, 2002, pp 14–32.

    Google Scholar 

  11. T. Y. Lin, “Granular Computing on Binary Relations-Analysis of Conflict and Chinese Wall Security Policy,” in: Rough sets and Current Trends in Computing Alpigini, Peters Skowron, Zhong (eds), Lecture Notes in Artificial Intelligence No 2475, 2002, pp. 296–299

    Google Scholar 

  12. T. Y. Lin, “Granulation and Nearest Neighborhood: Rough Set Approach.” In: Granular Computing: An Emerging Paradigm, W. Pedrycs (ed), Physica-Verlag, 2001, pp. 125–142

    Google Scholar 

  13. A. Bargiela, W. Pedrycz, and K. Hirota, “Logic-based Granular Prototyping,” in: Proceedings of the International Conference on Computer Software and Applications, Oxford, England, Aug 26–29, 2002, pp. 1164–1169

    Google Scholar 

  14. T. Y. Lin, “Granular Computing on Binary Relations I: Data Mining and Neighborhood Systems.” In: Rough Sets In Knowledge Discovery, A. Skowron and L. Polkowski (eds), Springer-Verlag, 1998, 107–121

    Google Scholar 

  15. T. Y. Lin, “Granular Computing on Binary Relations II: Rough Set Representations and Belief Functions.” In: Rough Sets In Knowledge Discovery, A. Skoworn and L. Polkowski (eds), Springer-Verlag, 1998, 121–140.

    Google Scholar 

  16. T. Y. Lin, “Granular Computing: Fuzzy Logic and Rough Sets.” In: Computing with words in information/intelligent systems, L.A. Zadeh and J. Kacprzyk (eds), Springer-Verlag, 183–200, 1999

    Google Scholar 

  17. T. Y. Lin, “Data Mining: Granular Computing Approach.” In: Methodologies for Knowledge Discovery and Data Mining, Lecture Notes in Artificial Intelligence 1574, Third Pacific-Asia Conference, Beijing, April 26–28, 1999, 24–33.

    Google Scholar 

  18. Tsau Young Lin, “Data Mining and Machine Oriented Modeling: A Granular Computing Approach,” Journal of Applied Intelligence, Kluwer, Vol 13, No 2, 2000, 113–124.

    Article  Google Scholar 

  19. T. Y. Lin, Neighborhood Systems and Relational Database. In: Proceedings of 1988 ACM Sixteen Annual Computer Science Conference, February 23–25, 1988, 725

    Google Scholar 

  20. Topological Data Models and Approximate Retrieval and Reasoning, in: Proceedings of 1989 ACM Seventeenth Annual Computer Science Conference, February 21–23, Louisville, Kentucky, 1989, 453.

    Google Scholar 

  21. T. Y. Lin, “Neighborhood Systems and Approximation in Database and Knowledge Base Systems,” Proceedings of the Fourth International Symposium on Methodologies of Intelligent Systems, Poster Session, Charlotte, North Carolina, October 12–15, pp. 75–86, 1989.

    Google Scholar 

  22. T. Y. Lin, and M. Hadjimichaelm, Non-classificatory Generalization in Data Mining. In: Proceedings of The Fourth Workshop on Rough Sets, Fuzzy Sets and Machine Discovery, Tokyo, Japan, November 8–10, 404–411, 1996.

    Google Scholar 

  23. T. Y. Lin, Ning Zhong, J. Duong, S. Ohsuga, “Frameworks for Mining Binary Relations in Data.” In: Rough sets and Current Trends in Computing, Lecture Notes on Artificial Intelligence 1424, A. Skoworn and L. Polkowski (eds), Springer-Verlag, 1998, 387–393.

    Google Scholar 

  24. T. Y. Lin, “Generating Concept Hierarchies/Networks: Mining Additional Semantics in Relational Data.” In: Advances in Knowledge Discovery and Data Mining, Lecture Notes in Artificial Intelligence # 2035, 2001, pp. 174–185 (5th Pacific-Asia Conference, Hong Kong, April 2001)

    Google Scholar 

  25. T. Y. Lin, and Q. Liu, Rough Approximate Operators-Axiomatic Rough Set Theory. In: Rough Sets, Fuzzy Sets and Knowledge Discovery, W. Ziarko (ed), Springer-Verlag, 256–260, 1994. Also in: The Proceedings of Second International Workshop on Rough Sets and Knowledge Discovery, Banff, Oct. 12–15, 255–257, 1993.

    Google Scholar 

  26. T. Y. Lin, “Chinese Wall Security Policy — An Aggressive Model”, Proceedings of the Fifth Aerospace Computer Security Application Conference, December 4–8, 1989, pp. 286–293.

    Google Scholar 

  27. John Kelly. General topology, Van Nostrand, 1955.

    Google Scholar 

  28. Yao, Y.Y. and Zhong, N. Granular Computing using Information Tables, in: Data Mining, Rough Sets and Granular Computing, Lin, T.Y., Yao, Y.Y. and Zadeh, L.A. (Eds.), Physica-Verlag, Heidelberg, pp. 102–124, 2002.

    Google Scholar 

  29. Yao, Y.Y. and Yao, J.T. Granular computing as a basis for consistent classification problems, Proceedings of PAKDD’02 Workshop on Foundations of Data Mining, Communications of Institute of Information and Computing Machinery, Taiwan, 5, 101–106, 2002.

    Google Scholar 

  30. Lotfi Zadeh, The Key Roles of Information Granulation and Fuzzy logic in Human Reasoning. In: 1996 IEEE International Conference on Fuzzy Systems, September 8–11, 1, 1996.

    Google Scholar 

  31. Lotfi Zadeh, Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, Fuzzy Sets and Systems 90(1997), 111–127.

    Article  MATH  MathSciNet  Google Scholar 

  32. L.A. Zadeh, Fuzzy Sets and Information Granularity, in: M. Gupta, R. Ragade, and R. Yager, (Eds), Advances in Fuzzy Set Theory and Applications, North-Holland, Amsterdam, 1979, 3–18.

    Google Scholar 

  33. M. Viveros, Extraction of Knowledge from Databases, Thesis, California State University at Northridge, 1989.

    Google Scholar 

  34. David D. C. Brewer and Michael J. Nash: “The Chinese Wall Security Policy” IEEE Symposium on Security and Privacy, Oakland, May, 1988, pp. 206–214

    Google Scholar 

  35. Z. Pawlak, “On Conflicts,” Int J. of Man-Machine Studies, 21 pp. 127–134, 1984

    Article  MATH  Google Scholar 

  36. Z. Pawlak, Analysis of Conflicts, Joint Conference of Information Science, Research Triangle Park, North Carolina, March 1–5, 1997, 350–352.

    Google Scholar 

  37. Lotfi A. Zadeh “Some Reflections on Information Granulation and its Centrality in Granular Computing, Computing with Words, the Computational Theory of Perceptions and Precisiated Natural Language,” in: Data Mining, Rough Sets and Granular Computing, T. Y. Lin, Y. Y. Yao, L. Zadeh (eds), Physica-Verlag, 2002

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lin, T.Y. (2003). Granular Computing. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_3

Download citation

  • DOI: https://doi.org/10.1007/3-540-39205-X_3

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-14040-5

  • Online ISBN: 978-3-540-39205-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics