Abstract
The high-utility itemset mining (HUIM) has a critical influence on data mining, and the number of transactions and the profit of items are considered together in HUIM. Nevertheless, HUIM has a disadvantage: it prefers to find itemsets that contain more items, even if the itemsets contain many items with low utility. The recently proposed high average-utility itemset mining (HAUIM) redefines a fairer measurement based on the HUIM to solve the problem. The importance of different itemsets is measured by calculating their average utility, which is defined as total utility divided by length. With the expansion of business, traditional exact algorithms cannot meet the requirements of runtime when datasets are becoming increasingly large and complex. To address this issue, the algorithm proposed in this paper, called GHAUPM-NPS, uses the framework of genetic algorithm to achieve a better balance in terms of performance and the completeness of results. Furthermore, a novel pruning strategy is proposed to accelerate the runtime of the algorithm by neglecting the unpromising itemsets. Sufficient experiments on multiple data show that the proposed algorithm is superior to traditional exact algorithms regarding runtime.
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References
Bhuvaneswari, M., Balaganesh, N., Muneeswaran, K.: H-map-based technique for mining high average utility itemset. IETE J. Res. 47(4), 1–13 (2022)
Fournier-Viger, P., Chun-Wei Lin, J., Truong-Chi, T., Nkambou, R.: A survey of high utility itemset mining. In: High-Utility Pattern Mining: Theory, Algorithms and Applications, pp. 1–45. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-04921-8_1
Fournier-Viger, P., et al.: The SPMF open-source data mining library version 2. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, 19–23 September 2016, Proceedings, Part III 16, pp. 36–40. Springer (2016)
Han, M., Gao, Z., Li, A., Liu, S., Mu, D.: An overview of high utility itemsets mining methods based on intelligent optimization algorithms. Knowl. Inf. Syst. 64(11), 2945–2984 (2022)
Hong, T.P., Lee, C.H., Wang, S.L.: Mining high average-utility itemsets. In: 2009 IEEE International Conference on Systems, Man and Cybernetics, pp. 2526–2530. IEEE (2009)
Kannimuthu, S., Premalatha, K.: Discovery of high utility itemsets using genetic algorithm with ranked mutation. Appl. Artif. Intell. 28, 337–359 (2014). https://doi.org/10.1080/08839514.2014.891839
Kim, H., et al.: Efficient approach of high average utility pattern mining with indexed list-based structure in dynamic environments. Inf. Sci. 657, 119924 (2024)
Lai, F., Zhang, X., Chen, G., Gan, W.: Mining periodic high-utility itemsets with both positive and negative utilities. Eng. Appl. Artif. Intell. 123, 106182 (2023)
Lin, C.W., Hong, T.P., Lu, W.H.: Efficiently mining high average utility itemsets with a tree structure. In: Intelligent Information and Database Systems: Second International Conference, ACIIDS, Hue City, 24–26 March 2010. Proceedings, Part I 2, pp. 131–139. Springer (2010)
Lin, J.C.W., Li, T., Fournier-Viger, P., Hong, T.P., Zhan, J., Voznak, M.: An efficient algorithm to mine high average-utility itemsets. Adv. Eng. Inform. 30, 233–243 (2016). https://doi.org/10.1016/j.aei.2016.04.002
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). https://doi.org/10.1109/ACCESS.2017.2717438
Lin, J.C.W., Yang, L., Fournier-Viger, P., Hong, T.P., Voznak, M.: A binary pso approach to mine high-utility itemsets. Soft Computing 21, 5103–5121 (9 2017). https://doi.org/10.1007/s00500-016-2106-1
Liu, M., Qu, J.: Mining high utility itemsets without candidate generation. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 55–64 (2012)
Lu, T., Vo, B., Nguyen, H.T., Hong, T.P.: A new method for mining high average utility itemsets. In: CISIM 2014. LNCS, vol. 8838, pp. 33–42. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45237-0_5
Nawaz, M.S., Fournier-Viger, P., Yun, U., Wu, Y., Song, W.: Mining high utility itemsets with hill climbing and simulated annealing. ACM Trans. Manag. Inf. Syst. (TMIS) 13, 1–22 (2021)
Qu, J.F., Fournier-Viger, P., Liu, M., Hang, B., Hu, C.: Mining high utility itemsets using prefix trees and utility vectors. IEEE Trans. Knowl. Data Eng. (2023)
Song, W., Huang, C.: Mining high utility itemsets using bio-inspired algorithms: a diverse optimal value framework. IEEE Access 6, 19568–19582 (2018)
Song, W., Liu, L., Huang, C.: Generalized maximal utility for mining high average-utility itemsets. Knowl. Inf. Syst. 63, 2947–2967 (2021). https://doi.org/10.1007/s10115-021-01614-z
Truong, T., Duong, H., Le, B., Fournier-Viger, P.: Efficient vertical mining of high average-utility itemsets based on novel upper-bounds. IEEE Trans. Knowl. Data Eng. 31(2), 301–314 (2018)
Wang, L., Cao, Q., Zhang, Z., Mirjalili, S., Zhao, W.: Artificial rabbits optimization: a new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Eng. Appl. Artif. Intell. 114, 105082 (2022)
Yun, U., Kim, D.: Mining of high average-utility itemsets using novel list structure and pruning strategy. Future Gen. Comput. Syst. 68, 346–360 (3 2017). https://doi.org/10.1016/j.future.2016.10.027
Zhan, Z.H., Li, J.Y., Kwong, S., Zhang, J.: Learning-aided evolution for optimization. IEEE Trans. Evolution. Comput. (2022)
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Chen, Q., Fang, W. (2024). High Average-Utility Pattern Mining Based on Genetic Algorithm with a Novel Pruning Strategy. In: Huang, DS., Zhang, X., Chen, W. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14862. Springer, Singapore. https://doi.org/10.1007/978-981-97-5578-3_1
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