Skip to main content

Mining Sequential Patterns in Uncertain Databases Using Hierarchical Index Structure

  • Conference paper
  • First Online:
Advances in Knowledge Discovery and Data Mining (PAKDD 2021)


In this uncertain world, data uncertainty is inherent in many applications and its importance is growing drastically due to the rapid development of modern technologies. Nowadays, researchers have paid more attention to mine patterns in uncertain databases. A few recent works attempt to mine frequent uncertain sequential patterns. Despite their success, they are incompetent to reduce the number of false-positive pattern generation in their mining process and maintain the patterns efficiently. In this paper, we propose multiple theoretically tightened pruning upper bounds that remarkably reduce the mining space. A novel hierarchical structure is introduced to maintain the patterns in a space-efficient way. Afterward, we develop a versatile framework for mining uncertain sequential patterns that can effectively handle weight constraints as well. Besides, with the advent of incremental uncertain databases, existing works are not scalable. There exist several incremental sequential pattern mining algorithms, but they are limited to mine in precise databases. Therefore, we propose a new technique to adapt our framework to mine patterns when the database is incremental. Finally, we conduct extensive experiments on several real-life datasets and show the efficacy of our framework in different applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. 1.

    uWSequence[12] defines the upper bound of expected support as \(expSupport^{top}(\alpha )\) = \(maxPr(\alpha _{m-1})\times maxPr(i_{m}) \times sup_{i_{m}}\) where \(sup_{i_{m}}\) is the support count of \(i_{m}\).

  2. 2.

  3. 3.

    For the Retail market-basket dataset, we used the first one-fifth transactions (1st month) as the initial portion and then 4 increments to represent the next 4 months.


  1. Ahmed, A.U., Ahmed, C.F., Samiullah, M., Adnan, N., Leung, C.K.S.: Mining interesting patterns from uncertain databases. Inf. Sci. 354, 60–85 (2016)

    Google Scholar 

  2. Cheng, H., Yan, X., Han, J.: IncSpan: incremental mining of sequential patterns in large database. In: ACM SIGKDD, pp. 527–532 (2004)

    Google Scholar 

  3. Ge, J., Xia, Y., Wang, J.: Mining uncertain sequential patterns in iterative MapReduce. In: Cao, T., et al. (eds.) PAKDD 2015, Part II. LNCS (LNAI), vol. 9078, pp. 243–254. Springer, Cham (2015).

    Chapter  Google Scholar 

  4. Ishita, S.Z., Noor, F., Ahmed, C.F.: An efficient approach for mining weighted sequential patterns in dynamic databases. In: Perner, P. (ed.) ICDM 2018. LNCS (LNAI), vol. 10933, pp. 215–229. Springer, Cham (2018).

    Chapter  Google Scholar 

  5. Le, T., Vo, B., Huynh, V.N., Nguyen, N.T., Baik, S.W.: Mining top-k frequent patterns from uncertain databases. Appl. Intell. 50, 1487–1497 (2020).

  6. Li, Z., Chen, F., Wu, J., Liu, Z., Liu, W.: Efficient weighted probabilistic frequent itemset mining in uncertain databases. Expert Syst. e12551 (2020)

    Google Scholar 

  7. Lin, C.W., Hong, T.P.: A new mining approach for uncertain databases using CUFP trees. Expert Syst. Appl. 39(4), 4084–4093 (2012)

    Google Scholar 

  8. Lin, J.C.-W., Gan, W., Fournier-Viger, P., Hong, T.-P., Tseng, V.S.: Weighted frequent itemset mining over uncertain databases. Appl. Intell. 44(1), 232–250 (2015).

  9. Lyu, X., Ma, H.: An efficient incremental mining algorithm for discovering sequential pattern in wireless sensor network environments. Sensors 19(1), 29 (2019)

    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 (LNAI), vol. 6635, pp. 210–221. Springer, Heidelberg (2011).

    Chapter  Google Scholar 

  11. Pei, J., et al.: Mining sequential patterns by pattern-growth: the PrefixSpan approach. IEEE TKDE 16(11), 1424–1440 (2004)

    Google Scholar 

  12. Rahman, M.M., Ahmed, C.F., Leung, C.K.S.: Mining weighted frequent sequences in uncertain databases. Inf. Sci. 479, 76–100 (2019)

    Google Scholar 

  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).

    Chapter  Google Scholar 

  14. Yun, U.: A new framework for detecting weighted sequential patterns in large sequence databases. Knowl.-Based Syst. 21(2), 110–122 (2008)

    Google Scholar 

  15. Zhao, Z., Yan, D., Ng, W.: Mining probabilistically frequent sequential patterns in large uncertain databases. IEEE TKDE 26(5), 1171–1184 (2013)

    Google Scholar 

Download references


This work is partially supported by NSERC (Canada) and University of Manitoba.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Chowdhury Farhan Ahmed .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Roy, K.K., Moon, M.H.H., Rahman, M.M., Ahmed, C.F., Leung, C.K. (2021). Mining Sequential Patterns in Uncertain Databases Using Hierarchical Index Structure. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12713. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75764-9

  • Online ISBN: 978-3-030-75765-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics