Skip to main content

A Note on Quality Measures for Fuzzy Association Rules

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2715))

Abstract

Several approaches generalizing association rules to fuzzy association rules have been proposed so far. While the formal specification of fuzzy associations is more or less straightforward, the evaluation of such rules by means of appropriate quality measures assumes an understanding of the semantic meaning of a fuzzy rule. In this respect, most existing proposals can be considered ad-hoc to some extent. In this paper, we suggest a theoretical basis of fuzzy association rules by generalizing the classification of the data stored in a database into positive, negative, and irrelevant examples of a rule.

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

Buying options

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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the 20th Conference on VLDB, Santiago, Chile, 1994.

    Google Scholar 

  2. C. Alsina. On a family of connectives for fuzzy sets. Fuzzy Sets and Systems, 16:231–235, 1985.

    Article  MathSciNet  Google Scholar 

  3. Wai-Ho Au and K.C.C. Chan. An effective algorithm for discovering fuzzy rules in relational databases. In Proceedings IEEE World Congress on Computational Intelligence, pages 1314–1319, 1998.

    Google Scholar 

  4. P. Bosc, D. Dubois, O. Pivert, and H. Prade. On fuzzy association rules based on fuzzy cardinalities. In Proc. IEEE Int. Fuzzy Systems Conference, Melbourne, 2001.

    Google Scholar 

  5. P. Bosc and O. Pivert. On some fuzzy extensions of association rules. In Proc. IFSA/NAFIPS-2001, Vancouver, Canada, 2001.

    Google Scholar 

  6. G. Chen, Q. Wei, and E.E. Kerre. Fuzzy data mining: Discovery of fuzzy generalized association rules. In G. Bordogna and G. Pasi, editors, Recent Issues on Fuzzy Databases. Springer-Verlag, 2000.

    Google Scholar 

  7. M. Delgado, D. Sanchez, and M.A. Vila. Acquisition of fuzzy association rules from medical data. In S. Barro and R. Marin, editors, Fuzzy Logic in Medicine. Physica Verlag, 2000.

    Google Scholar 

  8. D. Dubois and H. Prade. Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York, 1980.

    MATH  Google Scholar 

  9. D. Dubois and H. Prade. Fuzzy sets in data summaries — outline of a new approach. In Proceedings IPMU-2000, pages 1035–1040, Madrid, Spain, 2000.

    Google Scholar 

  10. J. Fodor and M. Roubens. Fuzzy Preference Modelling and Multicriteria Decision Support. Kluwer, 1994.

    Google Scholar 

  11. M.J. Frank. On the simulataneous associativity of f(x, y) and x + yf(x, y). Aeq. Math., 19:194–226, 1979.

    Article  MATH  Google Scholar 

  12. H. Hamacher. Über logische Aggregationen nichtbinär explizierter Entscheidungskriterien; Ein axiomatischer Beitrag zur normativen Entscheidungstheorie. R.G. Fischer Verlag, 1978.

    Google Scholar 

  13. E. Hüllermeier. Implication-based fuzzy association rules. In Proceedings PKDD-01, pages 241–252, Freiburg, Germany, September 2001.

    Google Scholar 

  14. E. Hüllermeier and J. Beringer. Mining implication-based fuzzy association rules in databases. In B. Bouchon-Meunier, L. Foulloy, and R.R. Yager, editors, Intelligent Systems for Information Processing: From Representation to Applications. Elsevier, 2003. To appear.

    Google Scholar 

  15. G.J. Klir and B. Yuan. Fuzzy Sets and Fuzzy Logic — Theory ad Applications. Prentice Hall, 1995.

    Google Scholar 

  16. C. Man Kuok, A. Fu, and M. Hon Wong. Mining fuzzy association rules in databases. SIGMOD Record, 27:41–46, 1998.

    Article  Google Scholar 

  17. W. Pedrycz. Data mining and fuzzy modeling. In Proc. of the Biennial Conference of the NAFIPS, pages 263–267, Berkeley, CA, 1996.

    Google Scholar 

  18. A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In VLDB-95, Zurich, 1995.

    Google Scholar 

  19. B. Schweizer and A. Sklar. Probabilistic Metric Spaces. North Holland, 1983.

    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

Dubois, D., Hüllermeier, E., Prade, H. (2003). A Note on Quality Measures for Fuzzy Association Rules. In: Bilgiç, T., De Baets, B., Kaynak, O. (eds) Fuzzy Sets and Systems — IFSA 2003. IFSA 2003. Lecture Notes in Computer Science, vol 2715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44967-1_41

Download citation

  • DOI: https://doi.org/10.1007/3-540-44967-1_41

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40383-8

  • Online ISBN: 978-3-540-44967-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics