Abstract
Mining high utility itemsets (HUIs) is a hot research topic in data mining. Algorithms based on evolutionary computation are attracting increasing attention because they have the advantage of avoiding the combinatorial explosion of the HUI search space. Among evolutionary methods used for mining HUIs, particle swarm optimization (PSO) is the most popular. Existing PSO-based HUI mining (HUIM) algorithms transform positions according to the result of applying the sigmoid function to the velocity. In this paper, we propose an HUIM algorithm based on set-based PSO (S-PSO) called HUIM-SPSO, which mainly considers elements in positions whose velocities are high. We introduce the modeling of HUIM using S-PSO, and explain HUIM-SPSO in detail. To reflect the diversity of the mining results, we propose the measure of the bit edit distance. Extensive experimental results show that the HUIM-SPSO algorithm is efficient and can discover more HUIs with a high degree of diversity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, W.-N., Zhang, J., Chung, H.S.H., Zhong, W.-L., Wu, W.-G., Shi, Y.-H.: A novel set-based particle swarm optimization method for discrete optimization problems. IEEE T. Evolut. Comput. 14(2), 278–300 (2010)
Fournier-Viger, P., et al.: The SPMF open-source data mining library version 2. In: Berendt, B., et al. (eds.) ECML PKDD 2016. LNCS, vol. 9853, pp. 36–40. Springer, Cham (2016)
Kannimuthu, S., Premalatha, K.: Discovery of high utility itemsets using genetic algorithm with ranked mutation. Appl. Artif. Intell. 28(4), 337–359 (2014)
Kennedy, J., Eberhart, R.: A discrete binary version of particle swarm algorithm. In: Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics, pp. 4104–4108 (1997)
Lin, J.C.-W., et al.: Mining high-utility itemsets based on particle swarm optimization. Eng. Appl. Artif. Intel. 55, 320–330 (2016)
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)
Liu, J., Wang, K., Fung, B.C.M.: Direct discovery of high utility itemsets without candidate generation. In: Proceedings of The 12th IEEE International Conference on Data Mining, pp. 984–989 (2012)
Liu, M., Qu, J.-F.: Mining high utility itemsets without candidate generation. In: Proceedings of The 21st ACM International Conference on Information and Knowledge Management, pp. 55–64 (2012)
Liu, Y., Liao, W., Choudhary, A.: A two-phase algorithm for fast discovery of high utility itemsets. In: Ho, T.B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 689–695. Springer, Heidelberg (2005). https://doi.org/10.1007/11430919_79
Song, W., Huang, C.: Discovering high utility itemsets based on the artificial bee colony algorithm. In: Phung, D., Tseng, V., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018. LNCS (LNAI), vol. 10939, pp. 3–14. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93040-4_1
Song, W., Liu, Y., Li, J.: Vertical mining for high utility itemsets. In: Proceedings of the 2012 IEEE International Conference on Granular Computing, pp. 429–434 (2012)
Song, W., Zhang, Z., Li, J.: A high utility itemset mining algorithm based on subsume index. Knowl. Inform. Syst. 49(1), 315–340 (2015). https://doi.org/10.1007/s10115-015-0900-1
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)
Yang, R., Xu, M., Jones, P., Samatova, N.: Real time utility-based recommendation for revenue optimization via an adaptive online top-k high utility itemsets mining model. In: Proceedings of The 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, pp. 1859–1866 (2017)
Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (61977001), the Great Wall Scholar Program (CIT&TCD20190305), and Beijing Urban Governance Research Center.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Song, W., Li, J. (2020). Discovering High Utility Itemsets Using Set-Based Particle Swarm Optimization. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham. https://doi.org/10.1007/978-3-030-65390-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-65390-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65389-7
Online ISBN: 978-3-030-65390-3
eBook Packages: Computer ScienceComputer Science (R0)