Skip to main content
SpringerLink
Log in

Mining closed high utility patterns with negative utility in dynamic databases

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

High utility itemsets mining algorithms with negative utility have more practical applications because they can handle datasets containing negative items. Existing algorithms that consider negative items assume that the database is static and contain a lot of redundant itemsets information in the result set. To solve these problems, a first algorithm for mining closed high utility itemsets containing negative items in dynamic databases, called CHUInd, is proposed. Dynamic list index structure designed in the algorithm to quickly access and update the information stored in the list based on the index values. Memory reuse strategy is applied to reduce memory usage and quickly update item information during batch insertion. Extensive experimental evaluations on real datasets show the efficiency as well as the feasibility of the algorithm, which exhibits excellent performance in terms of both runtime memory usage.

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
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Ahmed CF, Tanbeer SK, Jeong BS, et al. (2009) Efficient tree structures for high utility pattern mining in incremental databases. IEEE Trans Knowl Data Eng 21(12):1708–1721

    Article  Google Scholar 

  2. Chu CJ, Tseng VS, Liang T (2009) An efficient algorithm for mining high utility itemsets with negative item values in large databases. Appl Math Comput 215(2):767–778

    MATH  Google Scholar 

  3. Dam TL, Li K, Fournier-Viger P, et al. (2019) Cls-miner: efficient and effective closed high-utility itemset mining. Front Comput Sci 13(2):357–381

    Article  Google Scholar 

  4. Dam TL, Ramampiaro H, Nørvåg K, et al. (2019) Towards efficiently mining closed high utility itemsets from incremental databases. Knowl-Based Syst 165:13–29

    Article  Google Scholar 

  5. Djenouri Y, Comuzzi M (2017) Combining apriori heuristic and bio-inspired algorithms for solving the frequent itemsets mining problem. Inf Sci 420:1–15

    Article  Google Scholar 

  6. Fournier-Viger P, Zida S (2015) Foshu: faster on-shelf high utility itemset mining–with or without negative unit profit. In: Proceedings of the 30th annual ACM symposium on applied computing, pp 857–864

  7. Fournier-Viger P, Wu CW, Zida S et al (2014) Fhm: faster high-utility itemset mining using estimated utility co-occurrence pruning. In: International symposium on methodologies for intelligent systems. Springer, pp 83–92

  8. Fournier-Viger P, Lin JCW, Gueniche T, et al. (2015) Efficient incremental high utility itemset mining. In: Proceedings of the ASE BigData & SocialInformatics, vol 2015, pp 1–6

  9. Fournier-Viger P, Zida S, Lin JCW et al (2016) Efim-closed: fast and memory efficient discovery of closed high-utility itemsets. In: International conference on machine learning and data mining in pattern recognition. Springer, pp 199–213

  10. Han X, Liu X, Li J, et al. (2021) Efficient top-k high utility itemset mining on massive data. Inf Sci 557:382–406

    Article  MathSciNet  MATH  Google Scholar 

  11. Kim D, Yun U (2017) Efficient algorithm for mining high average-utility itemsets in incremental transaction databases. Appl Intell 47(1):114–131

    Article  MathSciNet  Google Scholar 

  12. Krishna GJ, Ravi V (2021) High utility itemset mining using binary differential evolution: an application to customer segmentation. Expert Syst Appl 181:115,122

    Article  Google Scholar 

  13. Lan GC, Hong TP, Huang JP, et al. (2014) On-shelf utility mining with negative item values. Expert Syst Appl 41(7):3450–3459

    Article  Google Scholar 

  14. Lin CW, Lan GC, Hong TP (2012) An incremental mining algorithm for high utility itemsets. Expert Syst Appl 39(8):7173–7180

    Article  Google Scholar 

  15. Lin JCW, Fournier-Viger P, Gan W (2016) Fhn: an efficient algorithm for mining high-utility itemsets with negative unit profits. Knowl-Based Syst 111:283–298

    Article  Google Scholar 

  16. Lin JCW, Djenouri Y, Srivastava G, et al. (2021) A predictive ga-based model for closed high-utility itemset mining. Appl Soft Comput 108:107,422

    Article  Google Scholar 

  17. Liu C, Guo C (2021) Mining top-n high-utility operation patterns for taxi drivers. Expert Syst Appl 170:114,546

    Article  Google Scholar 

  18. Liu J, Ju X, Zhang X, et al. (2019) Incremental mining of high utility patterns in one phase by absence and legacy-based pruning. IEEE Access 7:74,168–74,180

    Article  Google Scholar 

  19. Liu X, Wen S, Zuo W (2020) Effective sanitization approaches to protect sensitive knowledge in high-utility itemset mining. Appl Intell 50(1):169–191

    Article  Google Scholar 

  20. Mirbagheri SM, Hamilton HJ (2021) Mining high utility patterns in interval-based event sequences. Data Knowl Eng 135:101,924

    Article  Google Scholar 

  21. Muralidhar A, Venkatasubbu P (2021) Hupm-muo: high utility pattern mining under multiple utility objectives. Int J Comput Aided Eng Technol 14(3):385–407

    Article  Google Scholar 

  22. Nguyen TD, Nguyen LT, Vu L, et al. (2021) Efficient algorithms for mining closed high utility itemsets in dynamic profit databases. Expert Syst Appl 186:115,741

    Article  Google Scholar 

  23. Nouioua M, Fournier-Viger P, Wu CW, et al. (2021) Fhuqi-miner: fast high utility quantitative itemset mining. Appl Intell 51(10):6785–6809

    Article  Google Scholar 

  24. Singh K, Shakya HK, Singh A, et al. (2018) Mining of high-utility itemsets with negative utility. Expert Syst 35(6):e12,296

    Article  Google Scholar 

  25. Sumalatha S, Subramanyam R (2020) Distributed mining of high utility time interval sequential patterns using mapreduce approach. Expert Syst Appl 141:112,967

    Article  Google Scholar 

  26. Sun R, Han M, Zhang C, et al. (2021) Mining of top-k high utility itemsets with negative utility. J Intell Fuzzy Syst 40(3):5637–5652

    Article  Google Scholar 

  27. Wu CW, Fournier-Viger P, Gu JY, et al. (2015) Mining closed+ high utility itemsets without candidate generation. In: 2015 conference on technologies and applications of artificial intelligence (TAAI). IEEE, pp 187–194

  28. Yildirim I, Celik M (2020) Mining high-average utility itemsets with positive and negative external utilities. New Gener Comput 38(1):153–186

    Article  Google Scholar 

  29. Yun U, Ryang H, Lee G, et al. (2017) An efficient algorithm for mining high utility patterns from incremental databases with one database scan. Knowl-Based Syst 124:188–206

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Nature Science Foundation of China under Grant 62062004, in part by the Ningxia Natural Science Foundation Project under Grant 2020AAC03216, in part by the Computer Application Technology Autonomous Region Key Discipline Project under Grant PY1902, and in part by the Postgraduate Innovation Project of Northern Minzu University under Grant YCX21082.

Funding

The authors have no relevant financial or non-financial interests to disclose.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China

    Meng Han, Ni Zhang, Le Wang, Xiaojuan Li & Haodong Cheng

Authors
  1. Meng Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ni Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Le Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaojuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Haodong Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meng Han.

Ethics declarations

Ethics approval and consent to participate

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Consent for Publication

Authors are responsible for correctness of the statements provided in the manuscript.

Competing interests

The authors have no competing interests to declare that are relevant to the content of this article.

Conflict of Interests

The authors have no financial or proprietary interests in any material discussed in this article.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, M., Zhang, N., Wang, L. et al. Mining closed high utility patterns with negative utility in dynamic databases. Appl Intell 53, 11750–11767 (2023). https://doi.org/10.1007/s10489-022-03876-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03876-8

Keywords

Search

Navigation