Abstract
While traditional algorithms concern positive associations between binary or quantitative attributes of databases, this paper focuses on mining both positive and negative fuzzy association rules. We show how, by a deliberate choice of fuzzy logic connectives, significantly increased expressivity is available at little extra cost. In particular, rule quality measures for negative rules can be computed without additional scans of the database.
This work was partly supported by the National Natural Science Foundation of China (79925001/70231010), the MOE Funds for Doctoral Programs (20020003095), the Bilateral Scientific and Technological Cooperation Between China and Flanders (174B0201), and the Fund for Scientific Research Flanders.
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References
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proc. ACM–SIGMOD Int. Conf. on Management of Data, pp. 207–216 (1993)
Brin, S., Motwani, R., Silverstein, C.: Beyond Market Baskets: Generalizing Association Rules to Correlations. In: Proc. ACM SIGMOD on Management of Data, pp. 265–276 (1997)
Chen, G.Q., Yan, P., Kerre, E.E.: Computationally Efficient Mining for Fuzzy Implication-Based Association Rules in Quantitative Databases. International Journal of General Systems (to appear)
Cornelis, C.: Two–sidedness in the Representation and Processing of Imprecise Information (in Dutch), Ph.D. thesis
De Cock, M., Cornelis, C., Kerre, E.E.: Elicitation of Fuzzy Association Rules from Positive and Negative Examples (Submitted)
Dubois, D., Hüllermeier, E., Prade, H.: A note on Quality Measures for Fuzzy Association Rules. In: De Baets, B., Kaynak, O., Bilgiç, T. (eds.) IFSA 2003. LNCS, vol. 2715, pp. 346–353. Springer, Heidelberg (2003)
Gyenesei, A.: A Fuzzy Approach for Mining Quantitative Association Rules. TUCS technical report 336, University of Turku, Finland (2000)
Srikant, R., Agrawal, R.: Fast Algorithms for Mining Association Rules. In: Proc. VLDB Conference, pp. 487–499 (1994)
Srikant, R., Agrawal, R.: Mining Quantitative Association Rules in Large Relational Tables. In: Proc. ACM–SIGMOD Int. Conf. on Management of Data, pp. 1–12 (1996)
Wu, X., Zhang, C., Zhang, S.: Mining Both Positive and Negative Association Rules. In: Proc. 19th Int. Conf. on Machine Learning, pp. 658–665 (2002)
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© 2004 Springer-Verlag Berlin Heidelberg
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Yan, P., Chen, G., Cornelis, C., De Cock, M., Kerre, E. (2004). Mining Positive and Negative Fuzzy Association Rules. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_40
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DOI: https://doi.org/10.1007/978-3-540-30132-5_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23318-3
Online ISBN: 978-3-540-30132-5
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