Skip to main content
SpringerLink
Log in

Efficient Mining of Top-K Cross-Level High Utility Itemsets

  • Conference paper
  • First Online:
Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1925))

Included in the following conference series:

  • 410 Accesses

Abstract

High utility itemset (HUI) mining extracts frequent itemsets with high utility values from transactional databases. Traditional algorithms have limitations in detecting relationships between items and categories across multiple levels of a taxonomy-based database. Cross-level algorithms have been proposed to address this issue while top-k algorithms find the top-k HUIs with the highest utility values. Fast and Efficient Algorithm for Cross-level high-utility Pattern mining (FEACP) and Top-K Cross-level high utility itemset mining (TKC) algorithms were proposed for HUI mining with high efficiency. However, they suffer from scalability and efficiency issues when dealing with large datasets. To overcome these limitations, we propose a new algorithm called TKC-E (Efficient Top-K Cross-level high utility itemset mining), which combines the strengths of FEACP and TKC while applying efficient strategies to identify cross-level HUIs in taxonomy-based databases, resulting in significantly improved scalability and efficiency. Experimental results show that TKC-E outperforms TKC in terms of processing speed and memory usage, with up to 2.4 times memory and 60 times runtime improvements on sparse and dense datasets, respectively.

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 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

References

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

    Google Scholar 

  2. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22(2), 207–216 (1993)

    Article  Google Scholar 

  3. Fournier-Viger, P., Chun-Wei Lin, J., Truong-Chi, T., Nkambou, R.: A survey of high utility itemset mining. In: Fournier-Viger, P., Lin, J.-W., Nkambou, R., Vo, B., Tseng, V.S. (eds.) High-Utility Pattern Mining. SBD, vol. 51, pp. 1–45. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-04921-8_1

    Chapter  Google Scholar 

  4. Fournier-Viger, P., Cheng-Wei, Wu., Zida, S., Tseng, V.S.: FHM: faster high-utility itemset mining using estimated utility co-occurrence pruning. In: Andreasen, T., Christiansen, H., Cubero, J.-C., Raś, Z.W. (eds.) ISMIS 2014. LNCS (LNAI), vol. 8502, pp. 83–92. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08326-1_9

    Chapter  Google Scholar 

  5. Tseng, V.S., Wu, C.-W., Shie, B.-E., Yu, P.S.: UP-growth: an efficient algorithm for high utility itemset mining. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 253–262. Association for Computing Machinery, New York (2010)

    Google Scholar 

  6. Liu, M., Qu, J.: Mining high utility itemsets without candidate generation. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012), pp. 55–64. Association for Computing Machinery, New York (2012)

    Google Scholar 

  7. Cagliero, L., Chiusano, S., Garza, P., Ricupero, G.: Discovering high-utility itemsets at multiple abstraction levels. In: Kirikova, M., Nørvåg, K., Papadopoulos, G.A., Gamper, J., Wrembel, R., Darmont, J., Rizzi, S. (eds.) ADBIS 2017. CCIS, vol. 767, pp. 224–234. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67162-8_22

    Chapter  Google Scholar 

  8. Fournier-Viger, P., Wang, Y., Lin, J.-W., Luna, J.M., Ventura, S.: Mining cross-level high utility itemsets. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds.) IEA/AIE 2020. LNCS (LNAI), vol. 12144, pp. 858–871. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-55789-8_73

    Chapter  Google Scholar 

  9. Tung, N.T., Nguyen, L.T.T., Nguyen, T.D.D., Fourier-Viger, P., Nguyen, N.-T., Vo, B.: Efficient mining of cross-level high-utility itemsets in taxonomy quantitative databases. Inf. Sci. 587, 41–62 (2022). https://doi.org/10.1016/j.ins.2021.12.017

    Article  Google Scholar 

  10. Wu, C.W., Shie, B.-E., Tseng, V.S., Yu, P.S.: Mining top-K high utility itemsets. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012 (2012)

    Google Scholar 

  11. Tseng, V.S., Wu, C.-W., Fournier-Viger, P., Yu, P.S.: Efficient algorithms for mining top-K high utility itemsets. IEEE Trans. Knowl. Data Eng. 28(1), 54–67 (2016)

    Article  Google Scholar 

  12. Nouioua, M., Wang, Y., Fournier-Viger, P., Lin, J.C.-W., Wu, J. M.-T.: TKC: mining top-K cross-level high utility itemsets. In: Proceedings of the International Conference on Data Mining Workshops, Sorrento, Italy, pp. 673–682 (2020)

    Google Scholar 

  13. Tram, N.N., Hung, P.D.: Analysing hot Facebook users posts’ sentiment using deep learning. In: Hassanien, A.E., Bhattacharyya, S., Chakrabati, S., Bhattacharya, A., Dutta, S. (eds.) Emerging Technologies in Data Mining and Information Security. AISC, vol. 1300, pp. 561–569. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-4367-2_53

    Chapter  Google Scholar 

  14. Phan, D.H., Do, Q.D.: Analysing effects of customer clustering for customer’s account balance forecasting. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds.) ICCCI 2020. LNCS (LNAI), vol. 12496, pp. 255–266. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63007-2_20

    Chapter  Google Scholar 

  15. Hai, P.N., Hieu, H.T., Hung, P.D.: An empirical examination on forecasting VN30 short-term uptrend stocks using LSTM along with the Ichimoku cloud trading strategy. In: Sharma, H., Shrivastava, V., Kumari Bharti, K., Wang, L. (eds.) Communication and Intelligent Systems. LNNS, vol. 461, pp. 235–244. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-2130-8_19

    Chapter  Google Scholar 

  16. Hung, P.D., Son, D.N., Diep, V.T.: Building a recommendation system for travel location based on user check-ins on social network. In: Joshi, A., Mahmud, M., Ragel, R.G. (eds.) ICTCS 2022. LNNS, vol. 623, pp. 713–724. Springer, Singapore (2023). https://doi.org/10.1007/978-981-19-9638-2_62

    Chapter  Google Scholar 

  17. Nam, L.H., Hung, P.D., Vinh, B.T., Diep, V.T.: Practical fair queuing algorithm for message queue system. In: Joshi, A., Mahmud, M., Ragel, R.G. (eds.) ICTCS 2021. LNNS, vol. 400, pp. 421–429. Springer, Singapore (2023). https://doi.org/10.1007/978-981-19-0095-2_40

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, FPT University, Hanoi, Vietnam

    Nguyen Tuan Truong, Nguyen Khac Tue, Nguyen Duc Chinh, Le Dinh Huynh & Phan Duy Hung

  2. Hanoi University of Science and Technology, Hanoi, Vietnam

    Vu Thu Diep

Authors
  1. Nguyen Tuan Truong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nguyen Khac Tue
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nguyen Duc Chinh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Le Dinh Huynh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vu Thu Diep
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Phan Duy Hung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Vu Thu Diep or Phan Duy Hung .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Industry and Trade, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Suwon-si, Korea (Republic of)

    Tai M. Chung

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Truong, N.T., Tue, N.K., Chinh, N.D., Huynh, L.D., Diep, V.T., Hung, P.D. (2023). Efficient Mining of Top-K Cross-Level High Utility Itemsets. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2023. Communications in Computer and Information Science, vol 1925. Springer, Singapore. https://doi.org/10.1007/978-981-99-8296-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-8296-7_9

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8295-0

  • Online ISBN: 978-981-99-8296-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation