Abstract
Recently, negative association rule mining has received some attention and proved to be useful. This paper proposes an extended form for negative association rules and defines extended negative association rules. Furthermore, a corresponding algorithm is devised for mining extended negative association rules. The extended form is more general and expressive than the three existing forms. The proposed mining algorithm overcomes some limitations of previous mining methods, and experimental results show that it is efficient on simple and sparse datasets when minimum support is high to some degree. Our work will extend related applications of negative association rules to a broader range.
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References
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of the ACM SIGMOD Intl. Conf. on Management of Data, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast Algorithm for Mining Association Rules. In: Proceedings of VLDB, pp. 487–499 (1994)
Brin, S., Motwani, R., Silverstein, C.: Beyond Market Basket: Generalizing Association Rules to Correlations. In: Proceedings of the ACM SIGMOD, pp. 256–276 (1997)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining Frequent Patterns Without Candidate Generation: A Frequent-pattern Tree Approach. Data Mining and Knowledge Discovery 8, 53–87 (2004)
Pietracaprina, A., Zandolin, D.: Mining Frequent Itemsets Using Patricia Tries. In: Proceedings of IEEE FIMI (2003)
Savasere, A., Omiecinski, E., Navathe, S.: Mining for Strong Negative Associations in a Large Database of Customer Transactions. In: Proceedings of ICDE, pp. 494–502 (1998)
Thiruvady, D.R., Webb, G.I.: Mining Negative Rules in Large Databases Using GRD. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 161–165. Springer, Heidelberg (2004)
Webb, G.I., Zhang, S.: Beyond Association Rules: Generalized Rule Discovery. In: Proceedings of KDD, pp. 14–17 (2002)
Wu, X., Zhang, C., Zhang, S.: Mining Both Positive and Negative Association Rules. In: Proceedings of ICML, pp. 658–665 (2002)
Wu, X., Zhang, C., Zhang, S.: Efficient Mining of Both Positive and Negative Association Rules. ACM Transactions on Information Systems 22(3), 381–405 (2004)
Frequent Itemset Mining Dataset Repository, http://fimi.cs.helsinki.fi/data//
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Gan, M., Zhang, M., Wang, S. (2006). Extended Negative Association Rules and the Corresponding Mining Algorithm. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_17
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DOI: https://doi.org/10.1007/11739685_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
Online ISBN: 978-3-540-33585-6
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