Abstract
Association rule mining can uncover the most frequent patterns from large datasets. This algorithm such as Apriori, however, is time-consuming task. In this paper we examine the issue of maintaining association rules from newly streaming dataset in temporal databases. More importantly, we have focused on the temporal databases of which storage are restricted to relatively small sized. In order to deal with this problem, temporal constraints estimated by linear regression is applied to dataset filtering, which is a repeated task deleting records conflicted with these constraints. For conducting experiments, we simulated datasets made by synthetic data generator.
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References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. of the ACM SIGMOD Conference on Management of Data, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. of the 20th VLDB Conference (1994)
Han, J., Pei, J.: Mining Frequent Patterns by Pattern-Growth: Methodology and Implications. ACM SIGKDD Explorations, 31–36 (2000)
Lee, S.D., Cheung, D.W.: Maintenance of Discovered Association Rules: When to Update? In: Proc. of ACM SIGMOD Workshop on Data Mining and Knowledge Discovery, DMKD (1997)
Cheung, D.W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of Discovered Rules in Large Databases: An Incremental Updating Technique. In: Proc. of Int. Conf. on Data Engineering, pp. 106–114 (1996)
Cheung, D.W., Lee, S.D., Kao, B.: A General Incremental Technique for Maintaining Discovered Association Rules. In: Proc. of Int. Conf. on Database Systems for Advanced Applications (DASFAA), pp. 185–194 (1997)
Zheng, Q., Xu, K., Ma, S.: When to Update the Sequential Patterns of Stream Data? In: Whang, K.-Y., Jeon, J., Shim, K., Srivastava, J. (eds.) PAKDD 2003. LNCS (LNAI), vol. 2637, Springer, Heidelberg (2003)
Hidber, C.: Online Association Rule Mining. In: Proc. of the ACM SIGMOD Conference on Management of Data, pp. 145–156 (1999)
Pudi, V., Haritsa, J.: How Good are Association-rule Mining Algorithm? In: Proc. of the 18th Int. Conf. on Data Engineering (2002)
Wojciechowski, M., Zakrzewicz, M.: Dataset Filtering Techniques in Constraint-Based Frequent Pattern Mining. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds.) Pattern Detection and Discovery. LNCS (LNAI), vol. 2447, pp. 77–91. Springer, Heidelberg (2002)
Papoulis, A., Pillai, S.U.: Probability, Random Variables and Stochastic Processes, 4th edn. McGraw-Hill, New York (2002)
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Jung, J.J., Jo, GS. (2005). Dataset Filtering Based Association Rule Updating in Small-Sized Temporal Databases. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424925_118
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DOI: https://doi.org/10.1007/11424925_118
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25863-6
Online ISBN: 978-3-540-32309-9
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