Skip to main content
SpringerLink
Log in

Maintenance of Prelarge High Average-Utility Patterns in Incremental Databases

  • Conference paper
  • First Online:
Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices (IEA/AIE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12144))

Abstract

Most existing data mining algorithms focused on mining the information from the static database. In this paper, the principle of pre-large is used to update the newly discovered HAUIs and reduce the time of the rescanning process. To further improve the performance of the suggested algorithm, two new upper-bounds are also proposed to decrease the number of candidates for HAUIs. The experimental results show the proposed algorithm has excellent performance and good potential to be applied in real applications.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

References

  1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: The 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)

    Google Scholar 

  2. Ahmed, C.F., Tanbeer, S.K., Jeong, B.S., Lee, Y.K.: Efficient tree structures for high utility pattern mining in incremental databases. IEEE Trans. Knowl. Data Eng. 21(12), 1708–1721 (2009)

    Article  Google Scholar 

  3. Cheung, D.W., Han, J., Ng, V.T., Wang, C.Y.: Maintenance of discovered association rules in large databases: an incremental updating technique. In: The International Conference on Data Engineering, pp. 106–114 (2002)

    Google Scholar 

  4. Chen, M.S., Park, J.S., Yu, P.: Efficient data mining for path traversal patterns. IEEE Trans. Knowl. Data Eng. 10(2), 209–221 (1998)

    Article  Google Scholar 

  5. Chen, C.M., Xiang, B., Liu, Y., Wang, K.H.: A secure authentication protocol for intermet of vehicles. IEEE Access 7, 12047–12057 (2019)

    Article  Google Scholar 

  6. Fournier-Viger, P., et al.: The SPMF open-source data mining library version 2. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 36–40 (2016)

    Google Scholar 

  7. Gan, W., Lin, J.C.W., Fournier-Viger, P., Chao, H.C., Yu, S.: A survey of parallel sequential pattern mining. ACM Trans. Knowl. Disc. Data 13(3), 1–34 (2019). Article 25

    Article  Google Scholar 

  8. Gan, W., Lin, J.C.W., Fournier-Viger, P., Chao, H.C., Tseng, V., Yu, P.: A survey of utility-oriented pattern mining. IEEE Trans. Knowl. Data Eng. (2019)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  10. Hong, T.P., Lin, C.W., Wu, Y.L.: Incrementally fast updated frequent pattern trees. Expert Syst. Appl. 34, 2424–2435 (2008)

    Article  Google Scholar 

  11. Hong, T.P., Wang, C.Y., Tao, Y.H.: A new incremental data mining algorithm using pre-large itemsets. Intell. Data Anal. 5(2), 111–129 (2001)

    Article  Google Scholar 

  12. Hong, T.P., Lee, C.H., Wang, S.L.: An incremental mining algorithm for high average-utility itemsets. In: The International Symposium on Pervasive Systems, Algorithms, and Networks, pp. 421–425 (2009)

    Google Scholar 

  13. Hong, T.P., Lee, C.H., Wang, S.L.: Effective utility mining with the measure of average utility. Expert Syst. Appl. 38(7), 8259–8265 (2011)

    Article  Google Scholar 

  14. Kim, N.V.: Some determinants affecting purchase intention of domestic products at local markets in Tien Giang province, Vietnam. Data Sci. Pattern Recogn. 1(2), 43–52 (2017)

    Google Scholar 

  15. Lin, C.W., Hong, T.P., Lu, W.H.: The pre-FUFP algorithm for incremental mining. Expert Syst. Appl. 36(5), 9498–9505 (2009)

    Article  Google Scholar 

  16. Lin, C.W., Lan, G.C., Hong, T.P.: An incremental mining algorithm for high utility itemsets. Expert Syst. Appl. 39(8), 7173–7180 (2009)

    Article  Google Scholar 

  17. Lin, C.W., Hong, T.P., Lu, W.H.: Efficiently mining high average utility itemsets with a tree structure. In: The Asian Conference on Intelligent Information and Database Systems, pp. 131–139 (2010)

    Google Scholar 

  18. Lin, C.W., Hong, T.P., Lu, W.H.: An effective tree structure for mining high utility itemsets. Expert Syst. Appl. 38(6), 7419–7424 (2011)

    Article  Google Scholar 

  19. Liu, M., Qu, J.: Mining high utility itemsets without candidate generation. In: ACM International Conference on Information and Knowledge Management, pp. 55–64 (2012)

    Google Scholar 

  20. Lan, G.C., Hong, T.P., Tseng, V.: Efficient mining high average-utility itemsets with an improved upper-bound strategy. Int. J. Inform. Technol. Decis. Making 11, 1009–1030 (2012)

    Article  Google Scholar 

  21. Lin, C.W., Hong, T.P., Lin, W.Y., Lan, G.C.: Efficient updating of sequential patterns with transaction insertion. Intell. Data Anal. 18, 1013–1026 (2014)

    Article  Google Scholar 

  22. Lu, T., Vo, B., Nguyen, H.T., Hong, T.P.: A new method for mining high average utility itemsets. In: IFIP International Conference on Computer Information Systems and Industrial Management, pp. 33–42 (2015)

    Google Scholar 

  23. Liu, J., Wang, K., Fung, B.C.M.: Mining high utility patterns in one phase without generating candidates. IEEE Trans. Knowl. Data Eng. 28(5), 1245–1257 (2016)

    Article  Google Scholar 

  24. Lin, J.C.W., Ren, S., Fournier-Viger, P., Hong, T.P.: EHAUPM: efficient high average-utility pattern mining with tighter upper-bounds. IEEE Access 5, 12927–12940 (2017)

    Article  Google Scholar 

  25. Su, J.H., Chang, W.Y., Tseng, V.S.: Integrated mining of social and collaborative information for music recommendation. Data Sci. Pattern Recogn. 1(1), 13–30 (2017)

    Google Scholar 

  26. Tseng, V., Shie, B.E., Wu, C.W., Yu, P.: Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Trans. Knowl. Data Eng. 25, 1772–1786 (2013)

    Article  Google Scholar 

  27. Wang, Z., Chen, K., He, L.: AsySIM: modeling asymmetric social influence for rating prediction. Data Sci. Pattern Recogn. 2(1), 25–40 (2018)

    Google Scholar 

  28. Wu, J.M.T., Lin, J.C.W., Tamrakar, A.: High-utility itemset mining with effective pruning strategies. ACM Trans. Knowl. Disc. Data 13(6), 1–22 (2019)

    Article  Google Scholar 

  29. Yao, H., Hamilton, H.J., Butz, C.J.: A foundational approach to mining itemset utilities from databases. In: The SIAM International Conference on Data Mining, pp. 215–221 (2004)

    Google Scholar 

  30. Zida, S., Fournier-Viger, P., Lin, J.C.-W., Wu, C.-W., Tseng, V.S.: EFIM: a fast and memory efficient algorithm for high-utility itemset mining. Knowl. Inf. Syst. 51(2), 595–625 (2016). https://doi.org/10.1007/s10115-016-0986-0

    Article  Google Scholar 

  31. Liu, Y., Liao, W.K., Choudhary, A.: A two-phase algorithm for fast discovery of high utility itemsets. In: The Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp. 689–695 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Jimmy Ming-Tai Wu & Qian Teng

  2. Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  3. School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  4. Department of Computer Science and Information Engineering, Tamkang University, New Taipei, Taiwan

    Chien-Fu Cheng

Authors
  1. Jimmy Ming-Tai Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qian Teng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jerry Chun-Wei Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Philippe Fournier-Viger
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chien-Fu Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jerry Chun-Wei Lin .

Editor information

Editors and Affiliations

  1. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  2. Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  3. Texas State University, San Marcos, TX, USA

    Moonis Ali

  4. Iwate Prefectural University, Takizawa, Japan

    Jun Sasaki

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wu, J.MT., Teng, Q., Lin, J.CW., Fournier-Viger, P., Cheng, CF. (2020). Maintenance of Prelarge High Average-Utility Patterns in Incremental Databases. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_75

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-55789-8_75

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-55788-1

  • Online ISBN: 978-3-030-55789-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation