Abstract
Fuzzy association analysis extracts relationships from data. The result of fuzzy association analysis depends on a chosen t-norm that is used for calculating confidence and support measures of mined association rules. We show that the set of mined association rules might change depending on the t-norm. We measure the distances of sets of mined rules with different t-norms and also with set of rules mined by crisp association analysis. We experiment with various datasets and partitioning methods to examine relationships of mined rules by different t-norms. Our experiments shed new light on application of fuzzy association mining and confirm that fuzzy association analysis usually brings signifficantly different results when compared to results given by crisp (non-fuzzy) association analysis.
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., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)
Bache, K., Lichman, M.: UCI machine learning repository (2013), http://archive.ics.uci.edu/ml
Dubois, D., Hüllermeier, E., Prade, H.: A systematic approach to the assessment of fuzzy association rules. Data Mining and Knowledge Discovery 13(2), 167–192 (2006)
Fagin, R., Kumar, R., Sivakumar, D.: Comparing top k lists. SIAM Journal on Discrete Mathematics 17(1), 134–160 (2003)
Hájek, P., Havránek, T.: Mechanizing Hypothesis Formation (Mathematical Foundations for a General Theory). Springer-Verlag (1978)
Hullermeier, E., Yi, Y.: In defense of fuzzy association analysis. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 37(4), 1039–1043 (2007)
Schweizer, B., Sklar, A.: Probabilistic Metric Spaces. North-Holland, New York (1983)
Sudkamp, T.: Examples, counterexamples, and measuring fuzzy associations. Fuzzy Sets and Systems 149(1), 57–71 (2005)
Verlinde, H., De Cock, M., Boute, R.: Fuzzy versus quantitative association rules: a fair data-driven comparison. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36(3), 679–684 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Kupka, J., Rusnok, P. (2015). The Role of a T-norm and Partitioning in Fuzzy Association Analysis. In: Grzegorzewski, P., Gagolewski, M., Hryniewicz, O., Gil, M. (eds) Strengthening Links Between Data Analysis and Soft Computing. Advances in Intelligent Systems and Computing, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-319-10765-3_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-10765-3_33
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10764-6
Online ISBN: 978-3-319-10765-3
eBook Packages: EngineeringEngineering (R0)