Abstract
Certainty factor and lift are known evaluation measures of association rules. These measures, nevertheless, do not guarantee accurate evaluation of strength of dependence between rule’s constituents. In particular, even if there is a strongest possible positive or negative dependence between rule’s constituents X and Y, these measures may reach values quite close to the values characteristic for rule’s constituents independence. In this paper, we first re-examine both certainty factor and lift. Then, in order to better evaluate dependence between rule’s constituents, we offer and examine a new measure – a dependence factor. Unlike in the case of the certainty factor, when defining our measure, we take into account the fact that for a given rule X → Y, the minimal conditional probability of the occurrence of Y given X may be greater than 0, while its maximal possible value may less than 1. In the paper, a number of properties and relations of all investigated measures are derived.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Int. Conf. on Management of Data, pp. 207–216 (1993)
Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: ACM SIGMOD 1997 Int. Conf. on Management of Data, pp. 255–264 (1997)
Hilderman, R.J., Hamilton, H.J.: Evaluation of interestingness measures for ranking discovered knowledge. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS (LNAI), vol. 2035, pp. 247–259. Springer, Heidelberg (2001)
Lallich, S., Teytaud, O., Prudhomme, E.: Association rule interestingness: measure and statistical validation. Quality Measures in Data Mining 2007, 251–275 (2006)
Lenca, P., Meyer, P., Vaillant, B., Lallich, S.: On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid. European Journal of Operational Research 184, 610–626 (2008)
Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. In: Knowledge Discovery in Databases, pp. 229–248. AAAI/MIT Press (1991)
Shortliffe, E., Buchanan, B.: A model of inexact reasoning in medicine. Mathematical Biosciences 23, 351–379 (1975)
Silberschatz, A., Tuzhilin, A.: on subjective measures of interestingness in knowledge discovery. In Proc. of KDD 1995, pp. 275–281 (1995)
Suzuki, E.: Pitfalls for categorizations of objective interestingness measures for rule discover. In: Gras, R., Suzuki, E., Guillet, F., Spagnolo, F. (eds.) Statistical Implicative Analysis. SCI, vol. 127, pp. 383–395. Springer, Heidelberg (2008)
Suzuki, E.: Interestingness measures - limits, desiderata, and recent results. In: QIMIE/PAKDD (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Kryszkiewicz, M. (2015). Dependence Factor for Association Rules. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9012. Springer, Cham. https://doi.org/10.1007/978-3-319-15705-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-15705-4_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15704-7
Online ISBN: 978-3-319-15705-4
eBook Packages: Computer ScienceComputer Science (R0)