Skip to main content
SpringerLink
Log in

Efficient Mining of Pareto-Front High Expected Utility Patterns

  • 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

In this paper, we present a model called MHEUPM to efficiently mine the interesting high expected utility patterns (HEUPs) by employing the multi-objective evolutionary framework. The model considers both uncertainty and utility factors to discover meaningful HEUPMs without requiring pre-defined threshold values (such as minimum utility and minimum uncertainty). The effectiveness of the model is validated using two encoding methodologies. The proposed MHEUPM model can discover a set of HEUPs within a limited period. The efficiency of the proposed model is determined through rigorous analysis and compared to the standard pattern-mining methods in terms of hypervolume, convergence, and number of the discovered patterns.

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., et al.: Fast algorithms for mining association rules. In: Proceedings 20th International Conference Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)

    Google Scholar 

  2. Fournier-Viger, P., Wu, C.W., 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 

  3. Lin, Y.C., Wu, C.W., Tseng, V.S.: Mining high utility itemsets in big data. In: Cao, T., Lim, E.P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9078, pp. 649–661. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18032-8_51

    Chapter  Google Scholar 

  4. Ryang, H., Yun, U.: Top-k high utility pattern mining with effective threshold raising strategies. Knowl.-Based Syst. 76, 109–126 (2015)

    Article  Google Scholar 

  5. Chennupati Saideep, K.R., Toyoda, M., Kitsuregawa, M.: Finding periodic-frequent patterns in temporal databases using periodic summaries. Data Sci. Pattern Recogn. 3(2), 24–46 (2019)

    Google Scholar 

  6. Zhang, L., Fu, G., Cheng, F., Qiu, J., Su, Y.: A multi-objective evolutionary approach for mining frequent and high utility itemsets. Appl. Soft Comput. 62, 974–986 (2018)

    Article  Google Scholar 

  7. Frank, J., Cooper, K.: Multiobjective feature selection: classification using educational datasets in an ensemble validation scheme. Data Sci. Pattern Recogn. 3(1), 9–34 (2019)

    Google Scholar 

  8. Xingsi Xue, H.Y., Zhang, J.: Using population-based incremental learning algorithm for matching class diagrams. Data Sci. Pattern Recogn. 3(1), 1–8 (2019)

    Google Scholar 

  9. Ahmed, U., Aleem, M., Noman Khalid, Y., Arshad Islam, M., Azhar Iqbal, M.: RALB-HC: a resource-aware load balancer for heterogeneous cluster. Concurrency Comput. Pract. Exp., e5606 (2019)

    Google Scholar 

  10. Lin, J.C.W., et al.: Mining high-utility itemsets based on particle swarm optimization. Eng. Appl. Artif. Intell. 55, 320–330 (2016)

    Google Scholar 

  11. Dorigo, M., Stützle, T.: Ant colony optimization: overview and recent advances. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics, pp. 311–351. Springer, Boston (2019). https://doi.org/10.1007/978-1-4419-1665-5_8

    Chapter  Google Scholar 

  12. Lin, J.C.W., Yang, L., Fournier-Viger, P., Hong, T.P., Voznak, M.: A binary PSO approach to mine high-utility itemsets. Soft Comput. 21(17), 5103–5121 (2017)

    Google Scholar 

  13. Wu, J.M.T., Zhan, J., Lin, J.C.W.: An ACO-based approach to mine high-utility itemsets. Knowl.-Based Syst. 116, 102–113 (2017)

    Google Scholar 

  14. Hooshsadat, M., Bayat, S., Naeimi, P., Mirian, M.S., Zaiane, O.R.: UAPRIORI: an algorithm for finding sequential patterns in probabilistic data. In: Uncertainty Modeling in Knowledge Engineering and Decision Making, pp. 907–912. World Scientific (2012)

    Google Scholar 

  15. Zida, S., Fournier-Viger, P., Lin, J.C.W., Wu, C.W., Tseng, V.S.: EFIM: a highly efficient algorithm for high-utility itemset mining. In: Sidorov, G., Galicia-Haro, S.N. (eds.) MICAI 2015. LNCS (LNAI), vol. 9413, pp. 530–546. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27060-9_44

    Chapter  Google Scholar 

  16. Fournier-Viger, P., et al.: The SPMF open-source data mining library version 2. In: Berendt, B., et al. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9853, pp. 36–40. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46131-1_8

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, 5063, Bergen, Norway

    Usman Ahmed, Jerry Chun-Wei Lin & Suresh Kumar Mukhiya

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

    Jimmy Ming-Tai Wu

  3. SINTEF Digital, Mathematics and Cybernetics, Oslo, Norway

    Youcef Djenouri

  4. Department of Mathematics and Computer Science, Brandon University, Brandon, Canada

    Gautam Srivastava

Authors
  1. Usman Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jerry Chun-Wei Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jimmy Ming-Tai Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Youcef Djenouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gautam Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Suresh Kumar Mukhiya
    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

Ahmed, U., Lin, J.CW., Wu, J.MT., Djenouri, Y., Srivastava, G., Mukhiya, S.K. (2020). Efficient Mining of Pareto-Front High Expected Utility Patterns. 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_74

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-55789-8_74

  • 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