Skip to main content
SpringerLink
Log in

Rough Sets: Trends, Challenges, and Prospects

  • Conference paper
  • First Online:
Rough Sets and Current Trends in Computing (RSCTC 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2005))

Included in the following conference series:

Abstract

The article presents a brief review of the past and the current state of the rough set-related research and provides some ideas about the perspectives of rough set methodology in the context of its likely impact on the future computing devices. The opinions presented are solely of the author and do not necessarily reflect the point of view of the majority of the rough set community.

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

Access this chapter

Log in via an institution
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. Pawlak, Z. Grzymata-Busse, J. Slowiriski, R. and Ziarko, W. (1995). Rough sets. Communications of the ACM, 38, 88–95.

    Article  Google Scholar 

  2. Pawlak, Z. (1991). Rough Sets-Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Boston, London, Dordrecht.

    MATH  Google Scholar 

  3. Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences, 11, 341–356.

    Article  MathSciNet  MATH  Google Scholar 

  4. Son, N. (1997). Rule induction from continuous data, in: P.P. Wang (ed.), Joint Conference of Information Sciences, March 1-5, Duke University, Vol. 3, 81–84.

    Google Scholar 

  5. Slowinski, R. (ed.) (1992). Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, Kluwer Academic Publishers, Boston, London, Dordrecht.

    Google Scholar 

  6. Ziarko, W. (ed.) (1994) Rough Sets, Fuzzy Sets and Knowledge Discovery, Springer Verlag, 326–334.

    Google Scholar 

  7. Yang, A., and Grzymala-Busse J. (1997). Modified algorithms LEM1 and LEM2 for rule induction form data with missing attribute values., In: P.P. Wang (ed.), Joint Conference of Information Sciences, March 1-5, Duke University, Vol. 3, 69–72.

    Google Scholar 

  8. Ziarko, W. (1993). Variable precision rough sets model.Journal of Computer and Systems Sciences, vol. 46, no. 1, 39–59.

    Article  MATH  MathSciNet  Google Scholar 

  9. Ziarko, W. Katzberg, J.(1993). Rough sets approach to system modelling and control algorithm acquisition. Proceedings of IEEE WASCANEX 93 Conference, Saskatoon, 154–163.

    Google Scholar 

  10. Ziarko, W. (1999) Decision making with probabilistic decision tables.Proceedings of the 7th Intl Workshop on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC'99, Yamaguchi, Japan 1999, Lecture Notes in AI 1711, Springer Verlag, 463–471.

    Google Scholar 

  11. Pawlak, Z. (2000) Rough sets and decision algorithms.Proceedings of the 2nd Intl Conference on Rough Sets and Current Trends in Computing, RSCTC'2000, Banff, Canada, 1–16.

    Google Scholar 

  12. S. K. Pal and A. Skowron (eds.) (1999) Rough Fuzzy Hybridization: A New Trend in Decision-Making, Springer-Verlag, Singapore.

    MATH  Google Scholar 

  13. L. Polkowski and A. Skowron (eds.) (1998) Rough Sets in Knowledge Discovery 1. Methodology and Applications, this Series vol. 18, Physica-Verlag, Heidelberg.

    Google Scholar 

  14. L. Polkowski and A. Skowron (eds.) (1998) Rough Sets in Knowledge Discovery 2. Applications, Case Studies and Software Systems, this Series, vol. 19, Physica-Verlag, Heidelberg.

    Google Scholar 

  15. T. Y. Lin and N. Cercone (eds.)(1997) Rough Sets and Data Mining. Analysis of Imprecise Data, Kluwer Academic Publishers, Dordrecht.

    Google Scholar 

  16. N. Zhong, A. Skowron, and S. Ohsuga (eds.)(1999) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing, Proceedings: the 7th International Workshop (RSFDGrC'99), Ube-Yamaguchi, Japan, November 1999, LNAI 1711, Springer-Verlag, Berlin.

    Google Scholar 

  17. M. Banerjee and S. K. Pal (1996) Roughness of a fuzzy set, Information Science 93(3/4)pp. 235–246.

    Google Scholar 

  18. S. Demri, E. Orlowska, and D. Vakarelov (1999) Indiscernibility and complementarity relations in Pawlak’s information systems, in: Liber Amicorum for Johan van Benthem’s 50th Birthday.

    Google Scholar 

  19. A. Czyzewski (1997) Learning algorithms for audio signal enhancement. Part 2: Implementation of the rough set method for the removal of hiss, J. Audio Eng. Soc. 45(11), pp. 931–943.

    Google Scholar 

  20. S. Greco, B. Matarazzo, and R. Slowinski (1999) Rough approximation of a preference relation by dominance relations, European Journal of Operational Research 117, 1999, pp. 63–83.

    Article  MATH  Google Scholar 

  21. J. W. Grzymala-Busse and J. Stefanowski (1997) Discretization of numerical attributes by direct use of the rule induction algorithm LEM2 with interval extension, in: Proceedings: the Sixth Symposium on Intelligent Information Systems (IIS'97), Zakopane, Poland, pp. 149–158.

    Google Scholar 

  22. K. Krawiec, R. Slowiriski, and D. Vanderpooten (1998) Learning of decision rules from similarity based rough approximations, in: L. Polkowski, A. Skowron (eds.), Rough Sets in Knowledge Discovery 2. Applications, Case Studies and Software Systems, Physica-Verlag, Heidelberg, pp. 37–54.

    Google Scholar 

  23. T. Y. Lin, Ning Zhong, J. J. Dong, and S. Ohsuga (1998) An incremental, probabilistic rough set approach to rule discovery, in: Proceedings: the FUZZ-IEEE International Conference, 1998 IEEE World Congress on Computational Intelligence (WCCI'98), Anchorage, Alaska.

    Google Scholar 

  24. E. Martienne and M. Quafafou (1998) Learning fuzzy relational descriptions using the logical framework and rough set theory, in: Proceedings: the 7th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'98), IEEE Neural Networks Council.

    Google Scholar 

  25. Nguyen Hung Son and Nguyen Sinh Hoa (1997) Discretization methods with backtracking, in: Proceedings: the 5th European Congress on Intelligent Techniques and Soft Computing (EUFIT'97), Aachen, Germany, Verlag Mainz, Aachen, pp. 201–205.

    Google Scholar 

  26. W. Pedrycz (1999), Shadowed sets: bridging fuzzy and rough sets, in: S. K. Pal and A. Skowron (eds.), Rough Fuzzy Hybridization: A New Trend in Decision-Making, Springer-Verlag, Singapore, pp. 179–199.

    Google Scholar 

  27. J. E. Peters, A. Skowron, and Z. Suraj (1999) An application of rough set methods in control design, in: Proceedings: the Workshop on Concurrency, Specification and Programming (CS&P'99), Warsaw, Poland, pp.214–235.

    Google Scholar 

  28. Munakata, T. (1997) Rough control: a perspective, In: T. Y. Lin and N. Cercone (eds.), Rough Sets and Data Mining. Analysis for Imprecise Data. Kluwer Academic Publishers, Dordrecht,pp. 77–88.

    Google Scholar 

  29. Slowinski, K. (1992) Rough classification of HSV patients. In: R. Slowiriski (ed.), Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, Kluwer Academic Publishers, Dordrecht,pp. 77–93.

    Google Scholar 

  30. Slowinski, K., Sharif, E. S. (1993) Rough sets approach to analysis of data of diat-nostic peritoneal lavage applied for multiple injuries patients. In: W. Ziarko (ed.), Rough Sets, Fuzzy Sets and Knowledge Discovery. Proceedings of the International Workshop on Rough Sets and Knowledge Discovery (RSKD'93), Banff, Alberta, Canada, October 12-15, Springer-Verlag, pp. 420–425.

    Google Scholar 

  31. Szladow, A., and Ziarko W. (1993) Adaptive process control using rough sets. Proceedings of the International Conference of Instrument Society of America, ISA/93, Chicago, pp. 1421–1430.

    Google Scholar 

  32. Tsumoto, S. Ziarko, W. Shan. N. Tanaka, H.(1995) Knowledge discovery in clinical databases based on variable precision rough sets model. Proc. of the Nineteenth Annual Symposium on Computer Applications in Medical Care, New Orleans, 1995, Journal of American Medical Informatics Association Supplement,pp. 270–274.

    Google Scholar 

  33. Wasilewska, A., Banerjee, M. (1995) Rough sets and topological quasi-Boolean algebras. Proceedings of CSC'95 Workshop on Rough Sets and Database Mining, Nashville, pp.54–59.

    Google Scholar 

  34. Wong, S. K. M., Wang, L. S., Yao, Y. Y.(1995) On modeling uncertainty with interval structures. Computational Intelligence: an International Journal 11/2, pp. 406–426.

    Article  MathSciNet  Google Scholar 

  35. Vakarelov, D. (1991) A modal logic for similarity relations in Pawlak knowledge representation systems. Fundamenta Informaticae 15, pp. 61–79.

    MATH  MathSciNet  Google Scholar 

  36. Mrozek, A.(1992) Rough sets in computer implementation of rule-based control of industrial processes. In: R. Slowiriski (ed.), Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory. Kluwer Academic Publishers, Dordrecht, pp. 19–31.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University of Regina, S4S 0A2, Saskatchewan, Regina, Canada

    Wojciech Ziarko

Authors
  1. Wojciech Ziarko
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Regina Regina, S4S 0A2, Saskatchewan, Canada

    Wojciech Ziarko  & Yiyu Yao  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ziarko, W. (2001). Rough Sets: Trends, Challenges, and Prospects. In: Ziarko, W., Yao, Y. (eds) Rough Sets and Current Trends in Computing. RSCTC 2000. Lecture Notes in Computer Science(), vol 2005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45554-X_1

Download citation

  • DOI: https://doi.org/10.1007/3-540-45554-X_1

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43074-2

  • Online ISBN: 978-3-540-45554-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation