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.
Chapter PDF
Similar content being viewed by others
Keywords
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
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. Proc. of ACM SIGMOD (1993) 207–216
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. Proc. of the 20th VLDB Conference (1994) 487–499
Bayardo, R. J., Agrawal, R.: Mining the Most Interesting Rules. In Proc. of the 5th ACM SIGKDD (1999) 145–154
Bernadet M.: Basis of a Fuzzy Knowledge Discovery System. In Proc. of the 4th European Conference on PKDD (2000) 24–33
Clark, P., Boswell, P.: Rule Induction with CN2: Some Recent Improvements. In Machine Learning: Proc. of the Fifth European Conference (1991) 151–163
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) 132–143
Gyenesei, A.: Mining Weighted Association Rules for Fuzzy Quantitative Items. In Proc. of the 4th European Conference on PKDD (2000) 416–423
Gyenesei, A.: Determining Fuzzy Sets for Quantitative Attributes in Data Mining Problems. Proc. of Advances in Fuzzy Systems and Evol. Comp. (2001) 48–53
Hong, T-P., Kuo, C-S, Chi, S-C.: Mining association rules from quantitative data. Intelligent Data Analysis 3(5) (1999) 363–376
Hilderman, R.J., Hamilton, H.J.: Knowledge discovery and interestingness measures: A survey. Technical Report CS 99-04, University of Regina, Canada (1999)
Kullback, S., Leibler, R.A.: On information and sufficiency. Annals of Mathematical Statistics, 22, (1951) 79–86
Kuok, C.M., Fu, A., Wong, M.H.: Fuzzy association rules in databases. In ACM SIGMOD Record 27(1), (1998) 41–46
Morishita, S.: On Classification and Regression. In Proc. of the First Int. Conf. on Discovery Science-LNAI 1532 (1998) 40–57
Piatetsky-Shapiro, G., Frawley, W.J.: Knowledge Discovery in Databases. Chapter 13. AAAI Press/The MIT Press, Menlo Park, California (1991)
Shannon, C.E., Weawer, W.: Introduction to Probability and Statistics for Scientists and Engineers. McGraw-Hill (1997)
Smyth, P., Goodman, R.M.: Rule induction using information theory. In Knowledge Discovery in Databases, AAAI/MIT Press (1991) 159–176
Srikant, R., Agrawal, R.: Mining quantitative association rules in large relation tables. Proc. of ACM SIGMOD (1996) 1–12
Theil, H.: Economics and information theory. North-Holland (1967)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gyenesei, A., Teuhola, J. (2001). Interestingness Measures for Fuzzy Association Rules. In: De Raedt, L., Siebes, A. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2001. Lecture Notes in Computer Science(), vol 2168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44794-6_13
Download citation
DOI: https://doi.org/10.1007/3-540-44794-6_13
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42534-2
Online ISBN: 978-3-540-44794-8
eBook Packages: Springer Book Archive