Advertisement

Incremental Sequential Rule Mining with Streaming Input Traces

Conference paper
  • 759 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12109)

Abstract

Traditional static pattern mining techniques, such as association rule mining and sequential pattern mining, perform inefficiently when applied to streaming data when regular updates are required, since there is significant repetition in the computation. Incremental mining techniques instead reuse information that has been previously extracted, and apply newly received data to compute the updated set of patterns. This paper proposes a new algorithm for incrementally mining sequential rules with streaming data. An existing rule mining algorithm, ERMiner is presented, and an incremental extension, called IERMiner is proposed and demonstrated. Experiments show that IERMiner significantly decreases the run time required to update the set of patterns when compared to running ERMiner on the full dataset each time.

Keywords

Data mining Incremental sequential rule mining Streaming data 

References

  1. 1.
    Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Min. Knowl. Disc. 15(1), 55–86 (2007)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, ICDE 1995, pp. 3–14. IEEE Computer Society (1995)Google Scholar
  3. 3.
    Mooney, C.H., Roddick, J.F.: Sequential pattern mining - approaches and algorithms. ACM Comput. Surv. 45(2), 19:1–19:39 (2013)CrossRefGoogle Scholar
  4. 4.
    Fournier-Viger, P., Nkambou, R., Tseng, V.S.M.: RuleGrowth: mining sequential rules common to several sequences by pattern growth. In: Proceedings of the 2011 ACM Symposium on Applied Computing, SAC 2011, pp. 956–961. ACM (2011)Google Scholar
  5. 5.
    Fournier-Viger, P., Gueniche, T., Tseng, V.S.: Using partially-ordered sequential rules to generate more accurate sequence prediction. In: Zhou, S., Zhang, S., Karypis, G. (eds.) ADMA 2012. LNCS (LNAI), vol. 7713, pp. 431–442. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-35527-1_36CrossRefGoogle Scholar
  6. 6.
    Fournier-Viger, P., Gueniche, T., Zida, S., Tseng, V.S.: ERMiner: sequential rule mining using equivalence classes. In: Blockeel, H., van Leeuwen, M., Vinciotti, V. (eds.) IDA 2014. LNCS, vol. 8819, pp. 108–119. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-12571-8_10CrossRefGoogle Scholar
  7. 7.
    Kao, B., Zhang, M., Yip, C.L., Cheung, D.W., Fayyad, U.: Efficient algorithms for mining and incremental update of maximal frequent sequences. Data Min. Knowl. Disc. 10(2), 87–116 (2005)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Cheng, H., Yan, X., Han, J.: IncSpan: incremental mining of sequential patterns in large database. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 527–532. ACM (2004)Google Scholar
  9. 9.
    Masseglia, F., Poncelet, P., Teisseire, M.: Incremental mining of sequential patterns in large databases. Data Knowl. Eng. 46(1), 97–121 (2003)CrossRefGoogle Scholar
  10. 10.
    Parthasarathy, S., Zaki, M.J., Ogihara, M., Dwarkadas, S.: Incremental and interactive sequence mining. In: Proceedings of the Eighth International Conference on Information and Knowledge Management, pp. 251–258. ACM (1999)Google Scholar
  11. 11.
    Wang, K., Tan, J.: Incremental discovery of sequential patterns. In: 1996 ACM SIGMOD Data Mining Workshop: Research Issues on Data Mining and Knowledge Discovery (SIGMOD 1996), pp. 95–102 (1996)Google Scholar
  12. 12.
    Zheng, Q., Xu, K., Ma, S., Lv, W.: The algorithms of updating sequential patterns. arXiv preprint cs/0203027 (2002)Google Scholar
  13. 13.
    Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Apers, P., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 1–17. Springer, Heidelberg (1996).  https://doi.org/10.1007/BFb0014140CrossRefGoogle Scholar
  14. 14.
    Zaki, M.J.: Spade: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1–2), 31–60 (2001)CrossRefGoogle Scholar
  15. 15.
    Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 429–435. ACM (2002)Google Scholar
  16. 16.
    Pei, J., et al.: PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), p. 0215. IEEE Computer Society (2001)Google Scholar
  17. 17.
    Laxman, S., Sastry, P.S.: A survey of temporal data mining. In: SADHANA, Academy Proceedings in Engineering Sciences, vol. 31, pp. 173–198. The Indian Academy of Sciences (2006)Google Scholar
  18. 18.
    Drozdyuk, A.: Mining partially ordered sequential rules on unbounded data. Master’s thesis, University of New Brunswick, Canada (2018)Google Scholar

Copyright information

© Crown 2020

Authors and Affiliations

  1. 1.National Research Council CanadaOttawaCanada
  2. 2.National Research Council CanadaFrederictonCanada
  3. 3.University of New BrunswickFrederictonCanada

Personalised recommendations