Skip to main content
SpringerLink
Log in

An efficient approach for finding weighted sequential patterns from sequence databases

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Weighted sequential pattern mining has recently been discussed in the field of data mining. Different from traditional sequential pattern mining, this kind of mining considers different significances of items in real applications, such as cost or profit. Most of the related studies adopt the maximum weighted upper-bound model to find weighted sequential patterns, but they generate a large number of unpromising candidate subsequences. In this study, we thus propose an efficient approach for finding weighted sequential patterns from sequence databases. In particular, a tightening strategy in the proposed approach is proposed to obtain more accurate weighted upper-bounds for subsequences in mining. Through the experimental evaluation, the results also show the proposed approach has good performance in terms of pruning effectiveness and execution efficiency.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Agrawal R, Srikant R (1994) Fast algorithm for mining association rules. In: Proceedings of the international conference on very large data bases, pp 487–499

  2. Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the ieee international conference on data engineering, pp 3–14

  3. Agrawal R, Srikant R, Vu Q (1997) Mining association rules with item constraints. In: Proceedings of the 3rd international conference on knowledge discovery in databases and data mining, pp 66–73

  4. Agrawal R, Imielinksi T, Swami A (1993) Mining association rules between sets of items in large database. In: Proceedings of The ACM SIGMOD international conference on management of data, pp 207–216

  5. Agrawal R, Imielinski T, Swami A (1993) Database mining: a performance perspective. IEEE Trans Knowl Data Eng 5(6):914–925

    Article  Google Scholar 

  6. Ahmed CF, Tanbeer SK, Jeong BS (2010) A novel approach for mining high-utility sequential patterns in sequence databases. ETRI J 32(5):676–686

    Article  Google Scholar 

  7. Ahmed CF, Tanbeer SK, Jeong BS (2009) Efficient mining of weighted frequent patterns over data streams. In: Proceedings of IEEE international conference on high performance computing and communications (HPCC), pp 400–406

  8. Ahmed CF, Tanbeer SK, Jeong BS, Lee YK (2008) Handling dynamic weights in weighted frequent pattern mining. Inst Electr Inf Commun Eng (IEICE) 91-D(11):2578–2588

    Google Scholar 

  9. Ahmed CF, Tanbeer SK, Jeong BS, Lee YK, Choi HJ (2012) Single-pass incremental and interactive mining for weighted frequent patterns. Expert Syst Appl 39(9):7976–7994

    Article  Google Scholar 

  10. Chang JH (2011) Mining weighted sequential patterns in a sequence database with a time-interval weight. Knowl Based Syst 24(1):1–9

    Article  Google Scholar 

  11. Frequent itemset mining implementations repository, Available at http://fimi.cs.helsinki.fi/

  12. IBM Quest Data Mining Project, Quest Synthetic Data Generation Code, Available at http://www.almaden.ibm.com/cs/quest/syndata.html

  13. Le B, Nguyen H, Vo B (2010) Efficient algorithms for mining frequent weighted itemsets from weighted items databases. In: Proceedings of international conference on computing & communication technologies, research, innovation, and vision for the future (RIVF), pp 1–6

  14. Liu Y, Liao W, Choudhary A (2005) A fast high utility itemsets mining algorithm. In: Proceedings of the utility-based data mining workshop, pp 90–99

  15. Pei J, Han J, Asi BM, Wang J, Chen Q (2004) Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans Knowl Data Eng 16(11):1424–1440

    Article  Google Scholar 

  16. Shie BE, Yu PS, Tseng VS (2013) Mining interesting user behavior patterns in mobile commerce environments. Appl Intell 38(3):418–435

    Article  Google Scholar 

  17. Song W, Liu Y, Li J (2014) Mining high utility itemsets by dynamically pruning the tree structure. Appl Intell 40(1):29–43

    Article  Google Scholar 

  18. Srikant R, Agrawal R (1996) Mining sequential patterns: generalizations and performance improvements. In: Proceedings of the 5th international conference extending database technology, pp 3–17

  19. Yen SJ, Lee YS (2013) Mining non-redundant time-gap sequential patterns. Appl Intell 39(4):727–738

    Article  MathSciNet  Google Scholar 

  20. Yun U, Leggett JJ (2005) WFIM: weighted frequent itemset mining with a weight range and a minimum weight. In: Proceedings of the 5th SIAM international conference on data mining, pp 636–640

  21. Yun U, Leggett JJ (2006) WSpan: weighted sequential pattern mining in large sequence databases. In: Proceedings of the 3rd international ieee conference on intelligent systems, pp 512–517

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

    Article  Google Scholar 

  23. Yun U (2009) On pushing weight constraints deeply into frequent itemset mining. Intell Data Anal 13(3):359–383

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Sciences, Fuqling Branch of Fujian Normal University, Fuzhou, Fujian, China

    Guo-Cheng Lan

  2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan

    Tzung-Pei Hong & Hong-Yu Lee

  3. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, 804, Taiwan

    Tzung-Pei Hong

Authors
  1. Guo-Cheng Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tzung-Pei Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong-Yu Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tzung-Pei Hong.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lan, GC., Hong, TP. & Lee, HY. An efficient approach for finding weighted sequential patterns from sequence databases. Appl Intell 41, 439–452 (2014). https://doi.org/10.1007/s10489-014-0530-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-014-0530-4

Keywords

Search

Navigation