Abstract
This paper addresses the problem of mining exceptions from multidimensional databases. The goal of our proposed model is to find association rules that become weaker in some specific subsets of the database. The candidates for exceptions are generated combining previously discovered multidimensional association rules with a set of significant attributes specified by the user. The exceptions are mined only if the candidates do not achieve an expected support. We describe a method to estimate these expectations and propose an algorithm that finds exceptions. Experimental results are also presented.
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References
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: 20th VLDB Intl. Conf. (1994)
Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases. In: Dept. of Inform. and Computer Science, University of California, Irvine (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Goncalves, E.C., Plastino, A.: Mining Strong Associations and Exceptions in the STULONG Data Set. In: 6th ECML/PKDD Discovery Challenge (2004)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2001)
Hussain, F., Liu, H., Suzuki, E., Lu, H.: Exception Rule Mining with a Relative Interestingness Measure. In: Terano, T., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, Springer, Heidelberg (2000)
Savasere, A., Omiecinski, E., Navathe, S.: Mining for Strong Negative Associations in a Large Database of Customer Transactions. In: 14th ICDE Intl. Conf. (1998)
Suzuki, E., Zytkow, J.M.: Unified Algorithm for Undirected Discovery of Exception Rules. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 169–180. Springer, Heidelberg (2000)
Wu, X., Zhang, C., Zhang, S.: Mining both Positive and Negative Association Rules. In: 19th ICML Intl. Conf. (2002)
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© 2004 Springer-Verlag Berlin Heidelberg
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Gonçalves, E.C., Mendes, I.M.B., Plastino, A. (2004). Mining Exceptions in Databases. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_104
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DOI: https://doi.org/10.1007/978-3-540-30549-1_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24059-4
Online ISBN: 978-3-540-30549-1
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