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Fast and Exact Mining of Probabilistic Data Streams

  • Reza Akbarinia
  • Florent Masseglia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8188)

Abstract

Discovering Probabilistic Frequent Itemsets (PFI) is very challenging since algorithms designed for deterministic data are not applicable in probabilistic data. The problem is even more difficult for probabilistic data streams where massive frequent updates need to be taken into account while respecting data stream constraints. In this paper, we propose FEMP (Fast and Exact Mining of Probabilistic data streams), the first solution for exact PFI mining in data streams with sliding windows. FEMP allows updating the frequentness probability of an itemset whenever a transaction is added or removed from the observation window. Using these update operations, we are able to extract PFI in sliding windows with very low response times. Furthermore, our method is exact, meaning that we are able to discover the exact probabilistic frequentness distribution function for any monitored itemset, at any time. We implemented FEMP and conducted an extensive experimental evaluation over synthetic and real-world data sets; the results illustrate its very good performance.

Keywords

Probabilistic Data Streams Probabilistic Frequent Itemsets Sliding Windows 

References

  1. 1.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22, 207–216 (1993)CrossRefGoogle Scholar
  2. 2.
    Akbarinia, R., Valduriez, P., Verger, G.: Efficient Evaluation of SUM Queries Over Probabilistic Data. IEEE Transactions on Knowledge and Data Engineering (2012)Google Scholar
  3. 3.
    Bernecker, T., Kriegel, H.-P., Renz, M., Verhein, F., Zuefle, A.: Probabilistic frequent itemset mining in uncertain databases. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 119–128. ACM, New York (2009)CrossRefGoogle Scholar
  4. 4.
    Calders, T., Garboni, C., Goethals, B.: Approximation of frequentness probability of itemsets in uncertain data. In: Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM 2010, pp. 749–754. IEEE, Washington, DC (2010)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Dalvi, N., Suciu, D.: Efficient query evaluation on probabilistic databases. The VLDB Journal 16, 523–544 (2007)CrossRefGoogle Scholar
  7. 7.
    Giannella, C., Han, J., Pei, J., Yan, X., Yu, P.: Mining Frequent Patterns in Data Streams at Multiple Time Granularities. In: Kargupta, H., Joshi, A., Sivakumar, K., Yesha, Y. (eds.) Next Generation Data Mining. AAAI/MIT (2003)Google Scholar
  8. 8.
    Kranen, P., Seidl, T.: Harnessing the strengths of anytime algorithms for constant data streams. Data Min. Knowl. Discov. 19, 245–260 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Leung, C.K.-S., Brajczuk, D.A.: Efficient algorithms for the mining of constrained frequent patterns from uncertain data. SIGKDD Explor. Newsl. 11, 123–130 (2010)CrossRefGoogle Scholar
  10. 10.
    Leung, C.K.-S., Jiang, F.: Frequent itemset mining of uncertain data streams using the damped window model. In: Proceedings of the 2011 ACM Symposium on Applied Computing, SAC 2011, pp. 950–955. ACM, New York (2011)CrossRefGoogle Scholar
  11. 11.
    Leung, C.-S., Hao, B.: Mining of frequent itemsets from streams of uncertain data. In: Proceedings of IEEE 25th International Conference on Data Engineering (ICDE), pp. 1663–1670 (2009)Google Scholar
  12. 12.
    Sun, L., Cheng, R., Cheung, D.W., Cheng, J.: Mining uncertain data with probabilistic guarantees. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, pp. 273–282. ACM, New York (2010)Google Scholar
  13. 13.
    Teng, W.-G., Chen, M.-S., Yu, P.S.: A Regression-Based Temporal Pattern Mining Scheme for Data Streams. In: VLDB, pp. 93–104 (2003)Google Scholar
  14. 14.
    Wang, L., Cheng, R., Lee, S.D., Cheung, D.: Accelerating probabilistic frequent itemset mining: a model-based approach. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 429–438. ACM, New York (2010)CrossRefGoogle Scholar
  15. 15.
    Liu, Y.-H.: Mining frequent patterns from univariate uncertain data. Data and Knowledge Engineering 71(1), 47–68 (2012)CrossRefGoogle Scholar
  16. 16.
    Zhang, C., Masseglia, F., Lechevallier, Y.: ABS: The anti bouncing model for usage data streams. In: Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM 2010, pp. 1169–1174. IEEE Computer Society, Washington, DC (2010)CrossRefGoogle Scholar
  17. 17.
    Zhang, Q., Li, F., Yi, K.: Finding frequent items in probabilistic data. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, pp. 819–832. ACM, New York (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Reza Akbarinia
    • 1
  • Florent Masseglia
    • 1
  1. 1.Zenith team (INRIA-UM2), LIRMMMontpellierFrance

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