Abstract
Temporal regularity of pattern appearance can be regarded as an important criterion for measuring the interestingness in several applications like market basket analysis, web administration, gene data analysis, network monitoring, and stock market. Even though there have been some efforts to discover periodic patterns in time-series and sequential data, none of the existing works is appropriate for discovering the patterns that occur regularly in a transactional database. Therefore, in this paper, we introduce a novel concept of mining regular patterns from transactional databases and propose an efficient data structure, called Regular Pattern tree (RP-tree in short), that enables a pattern growth-based mining technique to generate the complete set of regular patterns in a database for a user-given regularity threshold. Our comprehensive experimental study shows that RP-tree is both time and memory efficient in finding regular pattern.
This study was supported by a grant of the Korea Health 21 R&D Project, Ministry for Health, Welfare and Family Affairs, Republic of Korea (A020602).
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Agrawal, R., Imielinski, T., Swami, A.N.: Mining Association Rules Between Sets of Items in Large Databases. In: ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: ACM SIGMOD International Conference on Management of Data, pp. 1–12. (2000)
Ozden, B., Ramaswamy, S., Silberschatz, A.: Cyclic Association Rules. In: 14th International Conference on Data Engineering, pp. 412–421 (1998)
Tanbeer, S.K., Ahmed, C.F., Jeong, B.-S., Lee, Y.-K.: CP-tree: A Tree Structure for Single-Pass Frequent Pattern Mining. In: PAKDD (accepted to be published, 2008)
Chi, Y., Wang, H., Yu, P.S., Muntz, R.R.: Catch the Moment: Maintaining Closed Frequent Itemsets Over a Data Stream Sliding Window. Knowledge and Information System 10(3), 265–294 (2006)
Maqbool, F., Bashir, S., Baig, A.R.: E-MAP: Efficiently Mining Asynchronous Periodic Patterns. International Journal of Computer Science and Network Security 6(8A), 174–179 (2006)
Zhi-Jun, X., Hong, C., Li, C.: An Efficient Algorithm for Frequent Itemset Mining on Data Streams. In: Perner, P. (ed.) International Conference on Management of Data, pp. 474–491 (2006)
Elfeky, M.G., Aref, W.G., Elmagarmid, A.K.: Periodicity Detection in Time Series Databases. IEEE Transactions on Knowledge and Data Engineering 17(7), 875–887 (2005)
Lee, G., Yang, W., Lee, J.-M.: A Parallel Algorithm for Mining Multiple Partial Periodic Patterns. Information Sciences 176, 3591–3609 (2006)
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© 2008 Springer-Verlag Berlin Heidelberg
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Tanbeer, S.K., Ahmed, C.F., Jeong, BS., Lee, YK. (2008). RP-Tree: A Tree Structure to Discover Regular Patterns in Transactional Database. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_25
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DOI: https://doi.org/10.1007/978-3-540-88906-9_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88905-2
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