Skip to main content
SpringerLink
Log in
Book cover

International Conference on Database and Expert Systems Applications

DEXA 1998: Database and Expert Systems Applications pp 841–850Cite as

Rule extraction using rough sets when membership values are intervals

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1460))

Abstract

As corporate data is more and more becoming an invaluable asset for decision makers, technologies such as data warehouses and data mining are an essential part of a company's information infrastructure. Methods for mining coiporate data and extracting rules from it are well established for crisp and traditional fuzzy data. This paper aims at extending the traditional fuzzy approach for situations where membership values are intervals.

The research of this author was partially supported by grants from the National Science Foundation CDA-9522157 and the Army Research Office DAAH-0495-10250.

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. Arciszewski, T. and Ziarko, W., Adaptive expert system for preliminary engineering design. Proc. 6th International Workshop on Expert Systems and their Applications, Avignon, France, 1(1986), 696–712.

    Google Scholar 

  2. Bernard, R., The Corporate Intranet, Wiley & Sons.

    Google Scholar 

  3. Bobrow.G.G., Mittal, S., Stefik, M.J., Expert systems: perils and promises, Com. ACM 29(1986), 880–894.

    Article  Google Scholar 

  4. Caben, P., Hadjinian, P., Stadler, R., Verhees, J. and Zanasi, A., Discovering Data Mining, Prentice Hall.

    Google Scholar 

  5. Cheeseman, P., Induction of models under uncertainty. Proc. ACM SIGART Internat. Symposium on Methodologies for Intelligent Systems, Knoxville, Tennessee, (1986), 130–144.

    Google Scholar 

  6. Fibak, J., Slowinski, K. and Slowinski, R.,The application of rough set theory to the verification of indications for treatment of duodenal ulcer by HSV, Proc. 6th International Workshop on Expert Systems and their Applications, Avignon, France, 1(1986), 587–599.

    Google Scholar 

  7. Griswold, S., Building a Corporate Intranet, Prima Publishing.

    Google Scholar 

  8. Groth, R., Data Mining, Prentice Hall.

    Google Scholar 

  9. Gryzmala-Busse, J.W., Knowledge acquisition under uncertainty — rough set approach, Journal of Intelligent and Robotic Systems1(1988), 3–16.

    Article  Google Scholar 

  10. Guengerich, S., Graham, D., Miller, M. and McDonald, S., Building the Corporate Intranet, Wiley & Sons.

    Google Scholar 

  11. Hammergren, T., Data Warehousing, Thomson Computer Press.

    Google Scholar 

  12. Kleyle, R. and de Korvin, A., A unified model for data acquisition and decision making, The Journal of the Amer. Soc. For Info. Science 15(1990), 149–161.

    Google Scholar 

  13. Komorowski, J. and Zytkow, J. (eds.), Principles of Data Mining and Knowledge Discovery, Proc. of 1st European Symposium, PKDD '97, Springer 1997.

    Google Scholar 

  14. de Korvin, A., Kleyle, R., Lea, R., An evidential approach to problem solving when a large number of knowledge systems are available, The Item. Journal of Intelligent Systems 5(1990), 293–306.

    MATH  Google Scholar 

  15. Kyas, O., Corporate Intranets, Thomson Computer Press.

    Google Scholar 

  16. Mamdani, A., Efstathiou, J. and Peng, D., Inference under uncertain expert systems, Proc. 5th Tech. Conf. BCS, SIG Expert Systems (1985), 181–194.

    Google Scholar 

  17. Mrozek, A., Information systems and control algorithms, Bull. Polish Acad, Sci., Technical Sci. 33(1985), 195–204.

    MATH  MathSciNet  Google Scholar 

  18. Mrozek, A., Rough sets and some aspects of expert system realization, Proc. 7th International Workshop on Expert Systems and their Applications, Avignon, France, 1(1987), 597–611.

    Google Scholar 

  19. Pawlak, Z., Rough sets. Basic Notions, Institute Comp. Sci., Polish Acad. Sci. Rep No. 431, Warsaw (1981).

    Google Scholar 

  20. Pawlak, Z., Classification of objects by means of attributes, Institute Comp. Sci., Polish Acad. Sci. Rep No. 429, Warsaw (1981).

    Google Scholar 

  21. Pawlak, Z., Rough sets, Int. J. Information Computer Sci. 11(1982), 341–256.

    Article  MATH  MathSciNet  Google Scholar 

  22. Pawlak, Z., Rough classifications, Int. J. Man-machine Studies 20(1983), 496–483.

    Google Scholar 

  23. Pawlak, Z., Rough sets and fuzzy sets, Fuzzy sets and systems, 17(1985), 99–102.

    Article  MATH  MathSciNet  Google Scholar 

  24. Shafer, G., Mathematical Theory of Evidence, Princeton University Press, (1976).

    Google Scholar 

  25. Strat, T.M. Decision analysis using belief functions, Int. J. of Approximate Reasoning 4(1990), 291–417.

    Article  Google Scholar 

  26. Wiederhold, G.C. Walker, M., Blum, R. and Downs, S., Acquisition of knowledge from data, Proc. ACM SIGART Int. Symp. On Methodologies for Intelligent Systems, Knoxville, Tennessee, (1986), 78–84.

    Google Scholar 

  27. Yager, R.R., Approximate reasoning as a basis for rule based expert systems, IEEE Trans. On Systems, Man and Cybernetics 14(1984), 635–643.

    Google Scholar 

  28. Yager, R.R., Decision making under Dempster-Shafer uncertainties, Iona College, Machine Intelligence Tech. Report MII-915.

    Google Scholar 

  29. Zadeh, L.A., Fuzzy sets and information granularity, Advances in Fuzzy Set Theory and Applications (1979), 3–18.

    Google Scholar 

  30. Zadeh, L.A., Possibility theory and soft data analysis, Mathematical Frontiers of the Social and Policy Sciences, Eds. L. Cobb and R.M. Thrall, 1981, Westview Press: Boulder, Colorado.

    Google Scholar 

  31. Zadeh, L.A., The rule of fuzzy logic in the management of uncertainty in expert systems, Fuzzy Sets and Systems 11(1983), 119–227.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Sciences & Technology, University of Houston-Downtown, One Main Street, 77002, Houston, TX, USA

    A. de Korvin

  2. Institut fur Angewandte Informatik und Informationssysteme, Universitat Wien, Liebiggasse 4, A-1010, Wien, Austria

    G. Quirchmayr

  3. College of Business, University of Houston -Downtown, One Main Street, 77002, Houston, TX, USA

    S. Hashemi

  4. Department of Mathematical SciencesIndiana, University - Purdue University, 402 North Blackford Street, 46205-2820, Indianapolis, IN, USA

    R. Kleyle

Authors
  1. A. de Korvin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Quirchmayr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Hashemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. R. Kleyle
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Gerald Quirchmayr Erich Schweighofer Trevor J.M. Bench-Capon

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

de Korvin, A., Quirchmayr, G., Hashemi, S., Kleyle, R. (1998). Rule extraction using rough sets when membership values are intervals. In: Quirchmayr, G., Schweighofer, E., Bench-Capon, T.J. (eds) Database and Expert Systems Applications. DEXA 1998. Lecture Notes in Computer Science, vol 1460. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0054539

Download citation

  • DOI: https://doi.org/10.1007/BFb0054539

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64950-2

  • Online ISBN: 978-3-540-68060-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation