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Towards Efficient Sequential Pattern Mining in Temporal Uncertain Databases

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Advances in Knowledge Discovery and Data Mining (PAKDD 2015)

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Abstract

Uncertain sequence databases are widely used to model data with inaccurate or imprecise timestamps in many real world applications. In this paper, we use uniform distributions to model uncertain timestamps and adopt possible world semantics to interpret temporal uncertain database. We design an incremental approach to manage temporal uncertainty efficiently, which is integrated into the classic pattern-growth SPM algorithm to mine uncertain sequential patterns. Extensive experiments prove that our algorithm performs well in both efficiency and scalability.

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References

  1. Aggarwal, C.C., Yu, P.S.: A survey of uncertain data algorithms and applications. IEEE Trans. on Knowl. and Data Eng. 21(5), 609–623 (2009)

    Article  Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB, pp. 487–499 (1994)

    Google Scholar 

  3. Bernecker, T., Kriegel, H.-P., Renz, M., Verhein, F., Zuefle, A.: Probabilistic frequent itemset mining in uncertain databases. In: SIGKDD, pp. 119–128 (2009)

    Google Scholar 

  4. Dyreson, C.E., Snodgrass, R.T.: Supporting valid-time indeterminacy. In: TODS (1998)

    Google Scholar 

  5. 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)

    Chapter  Google Scholar 

  6. 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)

    Chapter  Google Scholar 

  7. Jestes, J., Cormode, G., Li, F., Yi, K.: Semantics of ranking queries for probabilistic data. IEEE Transactions on Knowledge and Data Engineering 23(12), 1903–1917 (2011)

    Article  Google Scholar 

  8. Li, Y., Bailey, J., Kulik, L., Pei, J.: Mining probabilistic frequent spatio-temporal sequential patterns with gap constraints from uncertain databases. In: ICDM, pp. 448–457 (2013)

    Google Scholar 

  9. Miliaraki, I., Berberich, K., Gemulla, R., Zoupanos, S.: Mind the gap: Large-scale frequent sequence mining. In: SIGKDD, pp. 797–808 (2013)

    Google Scholar 

  10. Muzammal, M., Raman, R.: Mining sequential patterns from probabilistic databases. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 210–221. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  11. Pei, J., Han, J., Mortazavi-asl, B., Pinto, H., Chen, Q., Dayal, U., Chun Hsu, M.: Prefixspan: mining sequential patterns efficiently by prefix-projected pattern growth. In: ICDE, pp. 215–224 (2001)

    Google Scholar 

  12. Tong, Y., Chen, L., Cheng, Y., Yu, P.S.: Mining frequent itemsets over uncertain databases. Proceeding of the VLDB Endowment 5, 1650–1661 (2012)

    Article  Google Scholar 

  13. Zhang, H., Diao, Y., Immerman, N.: Recognizing patterns in streams with imprecise timestamps. Proc. VLDB Endow. 3(1–2), 244–255 (2010)

    Article  Google Scholar 

  14. Zhao, Z., Yan, D., Ng, W.: Mining probabilistically frequent sequential patterns in uncertain databases. In: EDBT, pp. 74–85 (2012)

    Google Scholar 

  15. Zhao, Z., Yan, D., Ng, W.: Mining probabilistically frequent sequential patterns in large uncertain databases. IEEE Transactions on Knowledge and Data Engineering 26, 1171–1184 (2013)

    Article  Google Scholar 

  16. Zhou, Y., Ma, C., Guo, Q., Shou, L., Chen, G.: Sequence pattern matching over time-series data with temporal uncertainty. In: EDBT, pp. 205–216 (2014)

    Google Scholar 

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Correspondence to Jiaqi Ge .

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Ge, J., Xia, Y., Wang, J. (2015). Towards Efficient Sequential Pattern Mining in Temporal Uncertain Databases. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9078. Springer, Cham. https://doi.org/10.1007/978-3-319-18032-8_21

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  • DOI: https://doi.org/10.1007/978-3-319-18032-8_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18031-1

  • Online ISBN: 978-3-319-18032-8

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