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Interestingness Measures for Fuzzy Association Rules

  • Attila Gyenesei
  • Jukka Teuhola
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2168)

Abstract

Data mining tries to discover interesting and surprising patterns among a given data set. An important task is to develop effective measures of interestingness for evaluating and ranking the discovered patterns. A good measure should give a high rank to patterns, which have strong evidence among data, but which yet are not too obvious. Thereby the initial set of patterns can be pruned before human inspection. In this paper we study interestingness measures for generalized quantitative association rules, where the attribute domains can be fuzzy. Several interestingness measures have been developed for the discrete case, and it turns out that many of them can be generalized to fuzzy association rules, as well. More precisely, our goal is to compare the fuzzy version of confidence to some other measures, which are based on statistics and information theory. Our experiments show that although the rankings of rules are relatively similar for most of the methods, also some anomalies occur. Our suggestion is that the information-theoretic measures are a good choice when estimating the interestingness of rules, both for fuzzy and non-fuzzy domains.

Keywords

Association Rule Potato Chip Mining Association Rule Conditional Entropy Quantitative Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. Proc. of ACM SIGMOD (1993) 207–216Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. Proc. of the 20th VLDB Conference (1994) 487–499Google Scholar
  3. 3.
    Bayardo, R. J., Agrawal, R.: Mining the Most Interesting Rules. In Proc. of the 5th ACM SIGKDD (1999) 145–154Google Scholar
  4. 4.
    Bernadet M.: Basis of a Fuzzy Knowledge Discovery System. In Proc. of the 4th European Conference on PKDD (2000) 24–33Google Scholar
  5. 5.
    Clark, P., Boswell, P.: Rule Induction with CN2: Some Recent Improvements. In Machine Learning: Proc. of the Fifth European Conference (1991) 151–163Google Scholar
  6. 6.
    Gray, B., Orlowska, M.E.: Ccaiia: clustering categorical attributes into interesting association rules. In Proc. of the 2th Pacific-Asia Conf. on Knowledge Discovery and Data Mining (1998) 132–143Google Scholar
  7. 7.
    Gyenesei, A.: Mining Weighted Association Rules for Fuzzy Quantitative Items. In Proc. of the 4th European Conference on PKDD (2000) 416–423Google Scholar
  8. 8.
    Gyenesei, A.: Determining Fuzzy Sets for Quantitative Attributes in Data Mining Problems. Proc. of Advances in Fuzzy Systems and Evol. Comp. (2001) 48–53Google Scholar
  9. 9.
    Hong, T-P., Kuo, C-S, Chi, S-C.: Mining association rules from quantitative data. Intelligent Data Analysis 3(5) (1999) 363–376zbMATHCrossRefGoogle Scholar
  10. 10.
    Hilderman, R.J., Hamilton, H.J.: Knowledge discovery and interestingness measures: A survey. Technical Report CS 99-04, University of Regina, Canada (1999)Google Scholar
  11. 11.
    Kullback, S., Leibler, R.A.: On information and sufficiency. Annals of Mathematical Statistics, 22, (1951) 79–86zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Kuok, C.M., Fu, A., Wong, M.H.: Fuzzy association rules in databases. In ACM SIGMOD Record 27(1), (1998) 41–46CrossRefGoogle Scholar
  13. 13.
    Morishita, S.: On Classification and Regression. In Proc. of the First Int. Conf. on Discovery Science-LNAI 1532 (1998) 40–57Google Scholar
  14. 14.
    Piatetsky-Shapiro, G., Frawley, W.J.: Knowledge Discovery in Databases. Chapter 13. AAAI Press/The MIT Press, Menlo Park, California (1991)Google Scholar
  15. 15.
    Shannon, C.E., Weawer, W.: Introduction to Probability and Statistics for Scientists and Engineers. McGraw-Hill (1997)Google Scholar
  16. 16.
    Smyth, P., Goodman, R.M.: Rule induction using information theory. In Knowledge Discovery in Databases, AAAI/MIT Press (1991) 159–176Google Scholar
  17. 17.
    Srikant, R., Agrawal, R.: Mining quantitative association rules in large relation tables. Proc. of ACM SIGMOD (1996) 1–12Google Scholar
  18. 18.
    Theil, H.: Economics and information theory. North-Holland (1967)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Attila Gyenesei
    • 1
  • Jukka Teuhola
    • 1
  1. 1.Turku Centre for Computer Science (TUCS)University of Turku, Department of Computer ScienceTurkuFinland

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