Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Methodologies for Intelligent Systems

ISMIS 1999: Foundations of Intelligent Systems pp 172–180Cite as

Rough set based data exploration using ROSE system

  • Communications
  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1609))

Abstract

This article briefly describes the process of data exploration based on rough set theory and also proposes ROSE system as a useful toolkit for doing such data analysis on PC computers.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. U.M. Fayyad, K.B. Irani. On the Handling of Continuous-Valued Attributes in Decision Tree Generation, Machine Learning, Vol 8, 1992, 87–102.

    MATH  Google Scholar 

  2. S. Greco, B. Matarazzo, R. Slowinski: A new rough set approach to multicriteria and multiattribute classification. [In] L. Polkowski, A. Skowron. (eds.), Proc. of the First Internat. Conference on Rough Setc and Current Trends In Computing-RSCTS’98, Warsaw, Springer-Verlag, 1998, 60–67.

    Google Scholar 

  3. J.W. Grzymala-Busse. LERS-a system for learning from examples based on rough sets. In R. Slowinski, (ed.) Intelligent Decision Support, Kluwer Academic Publishers, 1992, 3–18.

    Google Scholar 

  4. K. Krawiec, R. Slowinski, D. Vanderpooten. Learning of decision rules from similarity based rough approximations, [In] A. Skowron, L. Polkowski (eds.), Rough Sets in Knowledge Discovery vol. 2, Physica Verlag, Heidelberg, 1998, 37–54.

    Google Scholar 

  5. R. Mienko, J. Stefanowski, K. Tuomi, D. Vanderpooten. Discovery-Oriented Induction of Decision Rules. Cahier du Lamsade no. 141, Paris, Universite de Paris Dauphine, spetembre 1996.

    Google Scholar 

  6. Z. Pawlak Rough sets. Int. J. Computer and Information Sci., 11, 1982, 341–356.

    Article  MATH  MathSciNet  Google Scholar 

  7. S. Romanski. Operation on families of sets for exhaustive search, given a monotonic function. In W. Beeri, C. Schmidt, N. Doyle (eds.), Proceedings of the 3rd Int. Conference on Data and Knowledge Bases, Jerusalem 1988, 310–322.

    Google Scholar 

  8. A. Skowron, Rauszer C. The discernibility matrices and functions in information systems in: Slowinski R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory. Kluwer Academic Publishers, 1992, 331–362.

    Google Scholar 

  9. R. Slowinski. Rough sets learning of preferential attitude in multi-criteria decision making. In Komorowski J., Ras Z.W. (eds.), Proc. of Int. Symp. on Methodologies for Intelligent Systems, Springer Verlag LNAI 689, 1993, 642–651.

    Google Scholar 

  10. R. Slowinski, J. Stefanowski. ‘RoughDAS’ and ‘RoughClass’ software implementations of the rough set approach. In R. Slowinski (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory. Kluwer Academic Publishers, 1992, 445–456.

    Google Scholar 

  11. R. Slowinski, J. Stefanowski. Rough classification with valued closeness relation. [In] E. Diday, Y. Lechavalier, M. Schrader, P. Bertrand, B. Burtschy (eds.), New Approaches in Classification and Data Analysis. Springer-Verlag, Berlin, 1994, 482–489.

    Google Scholar 

  12. R. Slowinski, J. Stefanowski. Rough set reasoning about uncertain data. Fundamenta Informaticae, 27 (2–3), 1996, 229–244.

    MATH  MathSciNet  Google Scholar 

  13. R. Slowinski, D. Vanderpooten: Similarity relation as a basis for rough approximations [In] P.P. Wang (ed.). Advances in Machine Intelligence & Soft-Computing. Bookwrights, Raleigh, NC, 1997, 17–33.

    Google Scholar 

  14. R. Slowinski, D. Vanderpooten: A generalized definition of rough approximations based on similarity. IEEE Transactions on Data and Knowledge Engineering (to appear).

    Google Scholar 

  15. J. Stefanowski. On rough set based approaches to induction of decision rules. [In] A. Skowron, L. Polkowski (eds.), Rough Sets in Knowledge Discovery Vol. 1, Physica Verlag, Heidelberg, 1998, 500–529

    Google Scholar 

  16. W. Ziarko. Analysis of Uncertain Information in The Framework of Variable Precision Rough Sets. Foundations of Computing And Decision Sciences Vol 18 (1993) No. 3-4, 381–396.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, Piotrowo 3A, 60-965, Poznań, Poland

    Bartłomiej Prędki & Szymon Wilk

Authors
  1. Bartłomiej Prędki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Szymon Wilk
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Zbigniew W. Raś Andrzej Skowron

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Prędki, B., Wilk, S. (1999). Rough set based data exploration using ROSE system. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1999. Lecture Notes in Computer Science, vol 1609. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095102

Download citation

  • DOI: https://doi.org/10.1007/BFb0095102

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65965-5

  • Online ISBN: 978-3-540-48828-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation