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Probabilistic Maximal Frequent Itemset Mining Over Uncertain Databases

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Database Systems for Advanced Applications (DASFAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9642))

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Abstract

We focus on the problem of mining probabilistic maximal frequent itemsets. In this paper, we define the probabilistic maximal frequent itemset, which provides a better view on how to obtain the pruning strategies. In terms of the concept, a tree-based index PMFIT is constructed to record the probabilistic frequent itemsets. Then, a depth-first algorithm PMFIM is proposed to bottom-up generate the results, in which the support and expected support are used to estimate the range of probabilistic support, which can infer the frequency of an itemset with much less runtime and memory usage; in addition, the superset pruning is employed to further reduce the mining cost. Theoretical analysis and experimental studies demonstrate that our proposed algorithm spends less computing time and memory, and significantly outperforms the TODIS-MAX[20] state-of-the-art algorithm.

H. Li—This research is supported by the National Natural Science Foundation of China(61100112,61309030), Beijing Higher Education Young Elite Teacher Project(YETP0987), Discipline Construction Foundation of Central University of Finance and Economics, Key project of National Social Science Foundation of China(13AXW010), 121 of CUFE Talent project Young doctor Development Fund in 2014 (QBJ1427).

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References

  1. Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Min. Knowl. Discov. 17, 55–86 (2007)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  3. Bayardo, R.J.: Efficiently mining long patterns from databases. In: Proceedings of SIGMOD (1998)

    Google Scholar 

  4. Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rulesd. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 398–416. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  5. Calders, T., Goethals, B.: Mining all non-derivable frequent itemsets. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 74–86. Springer, Heidelberg (2002)

    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. 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 

  8. Leung, C.K.-S., Mateo, M.A.F., Brajczuk, D.A.: A tree-based approach for frequent pattern mining from uncertain data. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 653–661. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  9. Aggarwal, C.C., Li, Y., Wang, J., Wang, J.: Frequent pattern mining with uncertain data. In: Proceedings of KDD (2009)

    Google Scholar 

  10. Leung, C.K.-S., Tanbeer, S.K.: Fast tree-based mining of frequent itemsets from uncertain data. In: Lee, S., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012, Part I. LNCS, vol. 7238, pp. 272–287. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  11. Leung, C.K.-S., MacKinnon, R.K.: BLIMP: a compact tree structure for uncertain frequent pattern mining. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2014. LNCS, vol. 8646, pp. 115–123. Springer, Heidelberg (2014)

    Google Scholar 

  12. Leung, C.K.S., Brajczuk, D.A.: Efficient algorithms for the mining of constrained frequent patterns from uncertain data. In: SIGKDD Explorer, vol. 11, No. 2, pp. 123-130 (2009)

    Google Scholar 

  13. Calders, T., Garboni, C., Goethals, B.: Efficient pattern mining of uncertain data with sampling. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010, Part I. LNCS, vol. 6118, pp. 480–487. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  14. Leung, C.K.S., Hao, B.: Mining of frequent itemsets from streams of uncertain data. In: Proceedings of ICDE (2009)

    Google Scholar 

  15. Leung, C.K.-S., Jiang, F.: Frequent pattern mining from time-fading streams of uncertain data. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2011. LNCS, vol. 6862, pp. 252–264. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  16. Nguyen, H.-L., Ng, W.-K., Woon, Y.-K.: Concurrent semi-supervised learning with active learning of data streams. In: Hameurlain, A., Küng, J., Wagner, R., Cuzzocrea, A., Dayal, U. (eds.) TLDKS VIII. LNCS, vol. 7790, pp. 113–136. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  17. Leung, C.K.-S., Hayduk, Y.: Mining frequent patterns from uncertain data with mapreduce for big data analytics. In: Feng, L., Bressan, S., Winiwarter, W., Song, W., Meng, W. (eds.) DASFAA 2013, Part I. LNCS, vol. 7825, pp. 440–455. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  18. Zhang, Q., Li, F., Yi, K.: Finding frequent items in probabilistic data. In: Proceedings of SIGMOD (2008)

    Google Scholar 

  19. Bernecker, T., Kriegel, H.P., Renz, M., Verhein, F., Zuefle, A.: Probabilistic frequent itemset mining in uncertain databases. In: Proceedings of SIGKDD (2009)

    Google Scholar 

  20. Sun, L., Cheng, R., Cheung, D.W., Cheng, J.: Mining uncertain data with probabilistic guarantees. In: Proceedings of KDD (2010)

    Google Scholar 

  21. Bernecker, T., Kriegel, H.-P., Renz, M., Verhein, F., Zuefle, A.: Probabilistic frequent pattern growth for itemset mining in uncertain databases. In: Ailamaki, A., Bowers, S. (eds.) SSDBM 2012. LNCS, vol. 7338, pp. 38–55. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  22. Wang, L., Cheng, R., Lee, S.D., Cheung, D.: Accelerating probabilistic frequent itemset mining: a model-based approach. In: Proceedings of CIKM (2010)

    Google Scholar 

  23. Wang, L., Cheung, D., Cheng, R., Lee, S.D., Yang, X.S.: Efficient mining of frequent item sets on large uncertain databases. Trans. Knowl. Data Min. 24(12), 2170–2183 (2012)

    Article  Google Scholar 

  24. Calders, T., Garboni, C., Goethals, B.: Approximation of frequentness probability of itemsets in uncertain data. In: Proceedings of ICDM (2010)

    Google Scholar 

  25. Tong, Y., Chen, L., Cheng, Y., Yu, P.S.: Mining frequent itemsets over uncertain databases. In: Proceedings of VLDB (2012)

    Google Scholar 

  26. Tang, P., Peterson, E.A.: Mining probabilistic frequent closed itemsets in uncertain databases. In: Proceedings of ACMSE (2011)

    Google Scholar 

  27. Peterson, E.A., Tang, P.: Fast approximation of probabilistic frequent closed itemsets. In: Proceedings of ACMSE (2012)

    Google Scholar 

  28. Tong, Y., Chen, L., Ding, B.: Discovering threshold-based frequent closed itemsets over probabilistic data. In: Proceedings of ICDE (2012)

    Google Scholar 

  29. Liu, C., Chen, L., Zhang, C.: Mining probabilistic representative frequent patterns from uncertain data. In: Proceedings of SDM (2013)

    Google Scholar 

  30. Liu, C., Chen, L., Zhang, C.: Summarizing probabilistic frequent patterns : a fast approach. In: Proceedings of KDD (2013)

    Google Scholar 

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Authors and Affiliations

  1. School of Information, Central University of Finance and Economics, Beijing, 100081, China

    Haifeng Li & Ning Zhang

Authors
  1. Haifeng Li
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  2. Ning Zhang
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Correspondence to Haifeng Li .

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Editors and Affiliations

  1. Georgia Institute of Technology, Atlanta, Georgia, USA

    Shamkant B. Navathe

  2. University of Texas at Dallas, Richardson, Texas, USA

    Weili Wu

  3. University of Minnesota, Minneapolis, Minnesota, USA

    Shashi Shekhar

  4. Renmin University, Beijing, China

    Xiaoyong Du

  5. Fudan University, Shanghai, China

    X. Sean Wang

  6. Rutgers, The State University of New Jer, New Brunswick, New Jersey, USA

    Hui Xiong

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Li, H., Zhang, N. (2016). Probabilistic Maximal Frequent Itemset Mining Over Uncertain Databases. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, X., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9642. Springer, Cham. https://doi.org/10.1007/978-3-319-32025-0_10

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  • DOI: https://doi.org/10.1007/978-3-319-32025-0_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32024-3

  • Online ISBN: 978-3-319-32025-0

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