Skip to main content
Springer Nature Link
Log in

Updating high-utility pattern trees with transaction modification

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Traditional association-rule mining only concerns the occurrence frequencies of the items in a binary database. In real-world applications, customers may buy several copies of the purchased items. Other factors such as profit, quantity, or price should be concerned to measure the utilities of the purchased items. High-utility itemsets mining was thus proposed to consider the factors of quantity and profit. Two-phase model was the most commonly way to keep the transaction-weighted utilization downward closure property, thus reducing the numerous candidates in utility mining. Most methods for finding high-utility itemsets are used to handle a static database. In practical applications, transactions are changed whether insertion, deletion, or modification. Some itemsets may arise as the new high-utility itemsets or become invalid knowledge in the updated database. In this paper, a maintenance Fast Updated High Utility Pattern tree for transaction MODification (FUP-HUP-tree-MOD) algorithm is thus proposed to effective maintain and update the built HUP tree for mining high-utility itemsets in dynamic databases without candidate generation. Experiments are conducted to show better performance of the proposed algorithm compared to the two-phase algorithm and the HUP tree algorithm in batch mode.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Abdullah Z, Herawan T, Deris M (2010) Mining significant least association rules using fast slp-growth algorithm. Lect Notes Comput Sci 6059:324–336

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Agrawal R, Srikant R (1994) “Fast algorithms for mining association rules in large databases.” The Int Conf Very Large Data Bases: 487–499

  4. Chan R, Yang Q, Shen YD (2003) “Mining high utility itemsets,” IEEE Int Conf Data Min: 19–26

  5. Chen MS, Han J, Yu PS (1996) Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 8:866–883

    Article  Google Scholar 

  6. Cheung DWL, Han J, Ng V, Wong CY (1996) “Maintenance of discovered association rules in large databases: An incremental updating technique.” Int Conf Data Eng:106–114

  7. Cheung DWL, Lee SD, Kao B (1997) “A general incremental technique for maintaining discovered association rules.” The Int Conf Database Syst Adv Appl: 185–194

  8. Frequent itemset mining dataset repository. Available: http://fimi.ua.ac.be/data/ (2012)

  9. Gharib TF, Nassar H, Taha M, Abrahamd A (2010) An efficient algorithm for incremental mining of temporal association rules. Data Knowl Eng 69:800–815

    Article  Google Scholar 

  10. Han J, Pei J, Yin Y, Mao R (2004) Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl Disc 8:53–87

    Article  MathSciNet  Google Scholar 

  11. Hong TP, Lin CW, Wu YL (2008) Incrementally fast updated frequent pattern trees. Expert Syst Appl 34:2424–2435

    Article  Google Scholar 

  12. Hong TP, Lin CW, Wu YL (2008) “An efficient fufp-tree mainteance algorithm for record modification,”. Int J Innov Comput Inf Control 4:2875–2887

    Google Scholar 

  13. Li YC, Yeh JS, Chang CC (2005) Direct candidates generation: a novel algorithm for discovering complete share-frequent itemsets. Fuzzy Syst Knowl Discov 3614:551–560

    Article  Google Scholar 

  14. Li YC, Yeh JS, Chang CC (2005) “Direct candidates generation: A novel algorithm for discovering complete share-frequent itemsets.” Lect Notes Comput Sci: 551–560

  15. Li YC, Yeh JS, Chang CC (2005) “A fast algorithm for mining share-frequent itemsets.” Lect Notes Comput Sci: 417–428

  16. Lin CW, Hong TP, Lu WH (2009) The pre-fufp algorithm for incremental mining. Expert Syst Appl 36:9498–9505

    Article  Google Scholar 

  17. Lin CW, Hong TP, Lu WH (2010) “Maintaining high utility pattern trees in dynamic databases.” Int Conf Comput Eng Appl: 304–308

  18. Lin CW, Hong TP, Lu WH (2011) An effective tree structure for mining high utility itemsets. Expert Syst Appl 38:7419–7424

    Article  Google Scholar 

  19. Lin CW, Lan GC, Hong TP, Kong L (2014) Mining high utility itemsets based on transaction deletion. Lect Notes Electr Eng 260:983–990

    Article  Google Scholar 

  20. Lin CW, Lan GC, Hong TP (2012) An incremental mining algorithm for high utility itemsets. Expert Syst Appl 39:7173–7180

    Article  Google Scholar 

  21. Liu Y, Liao WK, Choudhary A (2005) “A two-phase algorithm for fast discovery of high utility itemsets.” Adv Knowl Discov Data Min: 689–695

  22. Liu M, Qu J (2012) “Mining high utility itemsets without candidate generation>” ACM Int Conf Inf Knowl Manag: 55–64

  23. Liu J, Wang K, Fung BCM (2012) “Direct discovery of high utility itemsets without candidate generation.” IEEE Int Conf Data Min: 984–989

  24. Microsoft. Example database foodmart of microsoft analysis services. Available: http://msdn.microsoft.com/en-us/library/aa217032(SQL.80).aspx

  25. Nath B, Bhattacharyya DK, Ghosh A (2013) “Incremental association rule mining: A survey,” WIREs Data Mining Knowledge Discovery, vol. 3

  26. Song W, Liu Y, Li J (2013) “Mining high utility itemsets by dynamically pruning the tree structure.” Appl Intell: 1–15

  27. Tseng VS, Bai-En S, Cheng-Wei W, Yu PS (2013) Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Trans Knowl Data Eng 25:1772–1786

    Article  Google Scholar 

  28. Wu CW, Lin YF, Yu PS, Tseng VS (2013) “Mining high utility episodes in complex event sequences.” ACM Int Conf Knowl Discov Data Min: 536–544

  29. Yao H, Hamilton HJ (2006) Mining itemset utilities from transaction databases. Data Knowl Eng 59:603–626

    Article  Google Scholar 

  30. Yao H, Hamilton HJ, Butz CJ (2004) “A foundational approach to mining itemset utilities from databases.” SIAM Int Conf Data Min: 211–225

  31. Yuna U, Ryanga H, Ryub KH (2014) High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates. Expert Syst Appl 41:3861–3878

    Article  Google Scholar 

Download references

Acknowledgments

This research was partially supported by the Shenzhen Peacock Project, China, under grant KQC201109020055A, by the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology under grant HIT.NSRIF.2014100, and by the Shenzhen Strategic Emerging Industries Program under grant ZDSY20120613125016389.

Author information

Authors and Affiliations

  1. Innovative Information Industry Research Center (IIIRC), School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Chun-Wei Lin & Wensheng Gan

  2. Shenzhen Key Laboratory of Internet Information Collaboration, School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Chun-Wei Lin

  3. Medical School, Shenzhen University, Shenzhen, China

    Binbin Zhang

  4. Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan

    Bo-Wei Chen

  5. Department of Multimedia, Sungkyul University, Anyang-si, South Korea

    Seungmin Rho

  6. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, Republic of China

    Tzung-Pei Hong

  7. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, Republic of China

    Tzung-Pei Hong

Authors
  1. Chun-Wei Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Binbin Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Wensheng Gan
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Bo-Wei Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Seungmin Rho
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Tzung-Pei Hong
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Binbin Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, CW., Zhang, B., Gan, W. et al. Updating high-utility pattern trees with transaction modification. Multimed Tools Appl 75, 4887–4912 (2016). https://doi.org/10.1007/s11042-014-2178-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-2178-9

Keywords