Advertisement

A Survey of High Utility Sequential Pattern Mining

  • Tin Truong-ChiEmail author
  • Philippe Fournier-Viger
Chapter
Part of the Studies in Big Data book series (SBD, volume 51)

Abstract

The problem of mining high utility sequences aims at discovering subsequences having a high utility (importance) in a quantitative sequential database. This problem is a natural generalization of several other pattern mining problems such as discovering frequent itemsets in transaction databases, frequent sequences in sequential databases, and high utility itemsets in quantitative transaction databases. To extract high utility sequences from a quantitative sequential database, the sequential ordering between items and their utility (in terms of criteria such as purchase quantities and unit profits) are considered. High utility sequence mining has been applied in numerous applications. It is much more challenging than the aforementioned problems due to the combinatorial explosion of the search space when considering sequences, and because the utility measure of sequences does not satisfy the downward-closure property used in pattern mining to reduce the search space. This chapter introduces the problem of high utility sequence mining, the state-of-art algorithms, applications, present related problems and research opportunities. A key contribution of the chapter is to also provide a theoretical framework for comparing upper-bounds used by high utility sequence mining algorithms. In particular, an interesting result is that an upper-bound used by the popular USpan algorithm is not an upper-bound. The consequence is that USpan is an incomplete algorithm, and potentially other algorithms extending USpan.

References

  1. 1.
    Ahmed, C.F., Tanbeer, S.K., Jeong, B.S.: Mining high utility web access sequences in dynamic web log data. In: 2010 11th ACIS International Conference Software Engineering AI Networking and Parallel/Distributed Computing (SNPD) (2010a)Google Scholar
  2. 2.
    Ahmed, C.F., Tanbeer, S.K., Jeong, B.S.: A novel approach for mining high-utility sequential patterns in sequence databases. ETRI J. 32, 676–686 (2010b)CrossRefGoogle Scholar
  3. 3.
    Shie, B.E., Hsiao, H., Tseng, V.S., Yu, P.S.: Mining high utility mobile sequential patterns in mobile commerce environments. In: DASFAA (2011)Google Scholar
  4. 4.
    Shie, B.E., Cheng, J.H., Chuang, K.T., Tseng, V.S.: A one-phase method for mining high utility mobile sequential patterns in mobile commerce environments. In: Advanced Research in Applied Artificial Intelligence, pp. 616–626 (2012)CrossRefGoogle Scholar
  5. 5.
    Shie, B.E., Yu, P.S., Tseng, V.S.: Mining interesting user behavior patterns in mobile commerce environments. Appl. Intell. 38, 418–435 (2013)CrossRefGoogle Scholar
  6. 6.
    Zihayat, M., Davoudi, H., An, A.: Top-k utility-based gene regulation sequential pattern discovery. In: Bioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference (2016a)Google Scholar
  7. 7.
    Dalmas, B., Fournier-Viger, P., Norre, S.: TWINCLE: a constrained sequential rule mining algorithm for event logs. In: Proceedings 9th International KES Conference (IDT-KES 2017). Springer (2017)Google Scholar
  8. 8.
    Lan, G.C., Hong, T.P., Tseng, V.S., Wang, S.L.: Applying the maximum utility measure in high utility sequential pattern mining. Expert Syst. Appl. 41(11), 5071–5081 (2014)CrossRefGoogle Scholar
  9. 9.
    Yin, J., Zheng, Z., Cao, L.: USpan: an efficient algorithm for mining high utility sequential patterns. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2012)Google Scholar
  10. 10.
    Alkan, O.K., Karagoz, P.: CRoM and HuspExt: improving efficiency of high utility sequential pattern extraction. IEEE Trans. Knowl. Data Eng. 27(10), 2645–2657 (2015)CrossRefGoogle Scholar
  11. 11.
    Wang, J.Z., Huang, J.L., Chen, Y.C.: On efficiently mining high utility sequential patterns. Knowl. Inf. Syst. 49(2), 597–627 (2016)CrossRefGoogle Scholar
  12. 12.
    Lin, J.C.W., Zhang, J., Fournier-Viger, P.: High-utility sequential pattern mining with multiple minimum utility thresholds. In: Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data (2017)CrossRefGoogle Scholar
  13. 13.
    Liu, Y., Liao, W.K., Choudhary, A.N.: A two-phase algorithm for fast discovery of high utility itemsets. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Hanoi, Vietnam (2005)CrossRefGoogle Scholar
  14. 14.
    Yin, J., Zheng, Z., Cao, L., Song, Y., Wei, W.: Efficiently mining top-k high utility sequential patterns. In: 2013 IEEE 13th International Conference on Data Mining (ICDM) (2013)Google Scholar
  15. 15.
    Xu, T., Dong, X., Xu, J., Dong, X.: Mining high utility sequential patterns with negative item values. Int. J. Pattern Recogn. Artif. Intell. 31(10), 1–17 (2017) (1750035)CrossRefGoogle Scholar
  16. 16.
    Dinh, T., Huynh, V.N., Le, B.: Mining periodic high utility sequential patterns. In: In Asian Conference on Intelligent Information and Database Systems (2017)Google Scholar
  17. 17.
    Dinh, T., Quang, M.N., Le, B.: A Novel approach for hiding high utility sequential patterns. In: Proceedings International Symposium Information and Communication Technology (2015)Google Scholar
  18. 18.
    Zaki, M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1), 31–60 (2001)CrossRefGoogle Scholar
  19. 19.
    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, KDD 2002, New York, NY (2002)Google Scholar
  20. 20.
    Gomariz, A., Campos, M., Marin, R., Goethals, B.: ClaSP: an efficient algorithm for mining frequent closed sequences. In: Proceedings of 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia (2013)CrossRefGoogle Scholar
  21. 21.
    Fournier-Viger, P., Gomariz, A., Campos, M., Thomas, R.: Fast vertical mining of sequential patterns using co-occurrence information. In: Proceedings of 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014 (2014)CrossRefGoogle Scholar
  22. 22.
    Bac, L., Hai, D., Tin, T., Fournier-Viger, P.: FCloSM, FGenSM: two efficient algorithms for mining frequent closed and generator sequences using the local pruning strategy. In: Knowledge and Information Systems (2017)Google Scholar
  23. 23.
    Hai, D., Tin, T., Bac, L.: Efficient algorithms for simultaneously mining concise representations of sequential patterns based on extended pruning conditions. Eng. Appl. Artif. Intell. 67, 197–210 (2018)Google Scholar
  24. 24.
    Yin, J. Z. Z. C. L. S. Y. a. W. W.: Efficiently mining top-k high utility sequential patterns. In: 2013 IEEE 13th International Conference on Data Mining (ICDM) (2013)Google Scholar
  25. 25.
    Zhang, B., Lin, J.C.W., Fournier-Viger, P., Li, T.: Mining of high utility-probability sequential patterns from uncertain databases. PLoS One 12(7), 1–21 (2017)Google Scholar
  26. 26.
    Wu, C.W., Lin, Y.F., Yu, P.S., Tseng, V.S.: Mining high utility episodes in complex event sequences. In: KDD 2013 Conference (2013)Google Scholar
  27. 27.
    Dave, U., Shah, J.: Efficient mining of high utility sequential pattern from incremental sequential dataset. Int. J. Comput. Appl. 122(12), 22–28 (2015)Google Scholar
  28. 28.
    Zihayat, M., Wu, C.W., An, A., Tseng, V.S.: Mining high utility sequential patterns from evolving data streams. In: Proceedings of the ASE Big Data and Social Informatics 2015 (2015)Google Scholar
  29. 29.
    Zihayat, M., Hut, Z.Z., An, A., Hut, Y.: Distributed and parallel high utility sequential pattern mining. In: Big Data (Big Data), 2016 IEEE International Conference (2016b)Google Scholar
  30. 30.
    Zida, S., Fournier-Viger, P., Wu, C.W., Lin, J.C.W., Tseng, V.S.: Efficient mining of high utility sequential rules. In: Proceedings 11th International on Conference on Machine Learning and Data Mining (MLDM 2015). Springer, LNAI 9166 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of DalatDalatVietnam
  2. 2.Harbin Institute of Technology (Shenzhen)ShenzhenChina

Personalised recommendations