Abstract
We consider sequential pattern mining in situations where there is uncertainty about which source an event is associated with. We model this in the probabilistic database framework and consider the problem of enumerating all sequences whose expected support is sufficiently large. Unlike frequent itemset mining in probabilistic databases [C. Aggarwal et al. KDD’09; Chui et al., PAKDD’07; Chui and Kao, PAKDD’08], we use dynamic programming (DP) to compute the probability that a source supports a sequence, and show that this suffices to compute the expected support of a sequential pattern. Next, we embed this DP algorithm into candidate generate-and-test approaches, and explore the pattern lattice both in a breadth-first (similar to GSP) and a depth-first (similar to SPAM) manner. We propose optimizations for efficiently computing the frequent 1-sequences, for re-using previously-computed results through incremental support computation, and for elmiminating candidate sequences without computing their support via probabilistic pruning. Preliminary experiments show that our optimizations are effective in improving the CPU cost.
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References
Aggarwal, C.C. (ed.): Managing and Mining Uncertain Data. Springer, Heidelberg (2009)
Aggarwal, C.C., Li, Y., Wang, J., Wang, J.: Frequent pattern mining with uncertain data. In: Elder et al. [9], pp. 29–38
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Yu, P.S., Chen, A.L.P. (eds.) ICDE, pp. 3–14. IEEE Computer Society, Los Alamitos (1995)
Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: KDD, pp. 429–435 (2002)
Bernecker, T., Kriegel, H.P., Renz, M., Verhein, F., Züfle, A.: Probabilistic frequent itemset mining in uncertain databases. In: Elder et al. [9], pp. 119–128
Chui, C.K., Kao, B.: A decremental approach for mining frequent itemsets from uncertain data. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 64–75. Springer, Heidelberg (2008)
Chui, C.K., Kao, B., Hung, E.: Mining frequent itemsets from uncertain data. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 47–58. Springer, Heidelberg (2007)
Cormode, G., Li, F., Yi, K.: Semantics of ranking queries for probabilistic data and expected ranks. In: ICDE, pp. 305–316. IEEE, Los Alamitos (2009)
Elder, J.F., Fogelman-Soulié, F., Flach, P.A., Zaki, M.J. (eds.): Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28-July 1. ACM, New York (2009)
Gunopulos, D., Khardon, R., Mannila, H., Saluja, S., Toivonen, H., Sharm, R.S.: Discovering all most specific sentences. ACM Trans. DB Syst. 28(2), 140–174 (2003)
Hassanzadeh, O., Miller, R.J.: Creating probabilistic databases from duplicated data. The VLDB Journal 18(5), 1141–1166 (2009)
Hua, M., Pei, J., Zhang, W., Lin, X.: Ranking queries on uncertain data: a probabilistic threshold approach. In: Wang [21], pp. 673–686
Khoussainova, N., Balazinska, M., Suciu, D.: Probabilistic event extraction from RFID data. In: ICDE, pp. 1480–1482. IEEE, Los Alamitos (2008)
Kohavi, R., Brodley, C., Frasca, B., Mason, L., Zheng, Z.: KDD-Cup 2000 organizers’ report: Peeling the onion. SIGKDD Explorations 2(2), 86–98 (2000)
Muzammal, M., Raman, R.: Mining sequential patterns from probabilistic databases. Tech. Rep. CS-10-002, Dept. of Comp. Sci. Univ. of Leicester, UK (2010), http://www.cs.le.ac.uk/people/mm386/pSPM.pdf
Muzammal, M., Raman, R.: On probabilistic models for uncertain sequential pattern mining. In: Cao, L., Feng, Y., Zhong, J. (eds.) ADMA 2010, Part I. LNCS, vol. 6440, pp. 60–72. Springer, Heidelberg (2010)
Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: Mining sequential patterns by pattern-growth: The PrefixSpan approach. IEEE Trans. Knowl. Data Eng. 16(11), 1424–1440 (2004)
Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)
Suciu, D., Dalvi, N.N.: Foundations of probabilistic answers to queries. In: Özcan, F. (ed.) SIGMOD Conference, p. 963. ACM, New York (2005)
Sun, X., Orlowska, M.E., Li, X.: Introducing uncertainty into pattern discovery in temporal event sequences. In: ICDM, pp. 299–306. IEEE Computer Society, Los Alamitos (2003)
Wang, J.T.L. (ed.): Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, Vancouver, BC, Canada, June 10-12. ACM, New York (2008)
Yang, J., Wang, W., Yu, P.S., Han, J.: Mining long sequential patterns in a noisy environment. In: Franklin, M.J., Moon, B., Ailamaki, A. (eds.) SIGMOD Conference, pp. 406–417. ACM, New York (2002)
Zaki, M.J.: SPADE: An efficient algorithm for mining frequent sequences. Machine Learning 42(1/2), 31–60 (2001)
Zhang, Q., Li, F., Yi, K.: Finding frequent items in probabilistic data. In: Wang [21], pp. 819–832
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Muzammal, M., Raman, R. (2011). Mining Sequential Patterns from Probabilistic Databases. In: Huang, J.Z., Cao, L., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 6635. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20847-8_18
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DOI: https://doi.org/10.1007/978-3-642-20847-8_18
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