Abstract
In most approaches to mining association rules, interestingness relies on frequent items, i.e., rules are built using items that frequently occur in the transactions. However, in many cases, data sets contain unfrequent items that can reveal useful knowledge that most standard algorithms fail to mine. For example, if items are products, it might be that each of the products \(p_1\) and \(p_2\) does not sell very well (i.e., none of them appears frequently in the transactions) but, that selling products \(p_1\) or \(p_2\) is frequent (i.e., transactions containing \(p_1\) or \(p_2\) are frequent). Then, assuming that \(p_1\) and \(p_2\) are similar enough with respect to a given similarity measure, the set \(\{p_1, p_2\}\) can be considered for mining relevant rules of the form \(\{p_1, p_2\} \rightarrow \{p_3, p_4\}\) (assuming that \(p_3\) and \(p_4\) are unfrequent similar products such that \(\{p_3,p_4\}\) is frequent), meaning that most of customers buying \(p_1\) or \(p_2\), also buy \(p_3\) or \(p_4\). The goal of our work is to mine association rules of the form \(D_1 \rightarrow D_2\) such that \((i)\) \(D_1\) and \(D_2\) are disjoint homogeneous frequent itemsets made up with unfrequent items, and \((ii)\) the support and the confidence of the rule are respectively greater than or equal to given thresholds. The main contributions of this paper towards this goal are to set the formal definitions, properties and algorithms for mining such rules.
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References
Agrawal, R., Mannila, H., Srikant, R., Toivonen, R., Verkamo, A.I.: Fast discovery of association rules. In: Advances in Knowledge Discovery and Data Mining, pp. 309–328. AAAI-MIT Press (1996)
Berberidis, C., Vlahavas, I.P.: Detection and prediction of rare events in transaction databases. Int. J. Artif. Intell. Tools 16(5), 829–848 (2007)
Booker, Q.E.: Improving identity resolution in criminal justice data: an application of NORA and SUDA. J. Inform. Assur. Secur. 4, 403–411 (2009)
Bouasker, S., Hamrouni, T., Ben Yahia, S.: New exact concise representation of rare correlated patterns: application to intrusion detection. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012, Part II. LNCS, vol. 7302, pp. 61–72. Springer, Heidelberg (2012)
Hamrouni, T., Ben Yahia, S.: Generalization of association rules through disjunction. Ann. Math. Artif. Intell. 59(2), 201–222 (2010)
Han, J., Fu, Y.: Discovery of multiple-level association rules from large databases. In: PVLDB, pp. 420–431 (1995)
He, Z., Xu, X.: FP-Outlier: frequent pattern based outlier detection. Comput. Sci. Inf. Syst. 2(1), 103–118 (2005)
Hilali-Jaghdam, I., Jen, T.-Y., Laurent, D., Ben Yahia, S.: Mining frequent disjunctive selection queries. In: Hameurlain, A., Liddle, S.W., Schewe, K.-D., Zhou, X. (eds.) DEXA 2011, Part II. LNCS, vol. 6861, pp. 90–96. Springer, Heidelberg (2011)
Hussain, F., Liu, H., Suzuki, E., Lu, H.: Exception rule mining with a relative interestingness measure. In: Terano, T., Liu, H., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 86–97. Springer, Heidelberg (2000)
Koh, Y.S., Roundtree, N.: Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection. IGI Global, Hershey (2010)
Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: ACM International Conference on Knowledge Discovery and Data Mining, SIGKDD, pp. 337–341. ACM (1999)
Marinica, C., Guillet, F.: Knowledge-based interactive postmining of association rules using ontologies. IEEE Trans. Knowl. Data Eng. 22(6), 784–797 (2010)
Natarajan, R., Shekar, B.: A relatedness-based data-driven approach to determination of interestingness of association rules. In: ACM Symposium on Applied Computing (SAC), pp. 551–552. ACM (2005)
Omiecinski, E.R.: Alternative interest measures for mining associations in databases. IEEE Trans. Knowl. Data Eng. 15(1), 57–69 (2003)
Romero, C., Romero, J.R., Luna, J.M., Ventura, S.: Mining rare association rules from e-learning data. In: Proceedings of the 3rd International Conference on Educational Data Mining (EDM 2010), Pittsburgh, PA, USA, pp. 171–180 (2010)
Shekar, B., Natarajan, R.: A framework for evaluating knowledge-based interestingness of association rules. Fuzzy Optim. Decis. Making 3, 157–185 (2004)
Tao, F., Murtagh, F., Farid, M.: Weighted association rule mining using weighted support and significance framework. In: ACM International Conference on Knowledge Discovery and Data Mining, SIGKDD, pp. 661–666. ACM (2003)
Wang, K., He, Y., Cheung, D.M.: Mining confident rules without support requirement. In: ACM International Conference on Information and Knowledge Management, CIKM, pp. 89–96. ACM (2001)
Wang, K., Zhou, S., He, Y.: Growing decision trees on support-less association rules. In: ACM International Conference on Knowledge Discovery and Data Mining, SIGKDD, pp. 265–269. ACM (2000)
Wang, W., Yang, J., Yu, P.S.: Efficient mining of weighted association rules (WAR). In: ACM International Conference on Knowledge Discovery and Data Mining, SIGKDD, pp. 270–274. ACM (2000)
Xiong, H., Tan, P.N., Koumar, V.: Mining strong affinity association patterns in data sets with skewed support distribution. In: IEEE ICDM, pp. 387–394. ACM (2003)
Xiong, H., Tan, P.N., Koumar, V.: Hyperclique pattern discovery. Data Min. Knowl. Discov 13(2), 219–242 (2006)
Younes, N.B., Hamrouni, T., Ben Yahia, S.: Bridging conjunctive and disjunctive search spaces for mining a new concise and exact representation of correlated patterns. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds.) DS 2010. LNCS, vol. 6332, pp. 189–204. Springer, Heidelberg (2010)
Yun, H., Ha, D., Hwang, B., Ho Ryu, K.: Mining association rules on significant rare data using relative support. J. Syst. Softw. 67(3), 181–191 (2003)
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Hilali, I., Jen, TY., Laurent, D., Marinica, C., Yahia, S.B. (2014). Mining Interesting Disjunctive Association Rules from Unfrequent Items. In: Kawtrakul, A., Laurent, D., Spyratos, N., Tanaka, Y. (eds) Information Search, Integration, and Personalization. ISIP 2013. Communications in Computer and Information Science, vol 421. Springer, Cham. https://doi.org/10.1007/978-3-319-08732-0_7
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