Abstract
Association rule mining (ARM), as a basic data mining task, aims to find association rules that satisfy predefined parameters from a given database. However, traditional ARM algorithms always generate a huge number of rules in many cases, which will greatly limit the usefulness of the mining results. Considering the unintuitiveness of setting parameters, algorithms have been designed to mine the top-k rules with the highest support that meet a minimum confidence. But it is not comprehensive to evaluate the strength of rules only by support and confidence. In some specific applications, the balance of rules also plays a decisive role in the actual effect. To address this issue, this paper proposes a top-k frequent balanced association rule mining algorithm named TFBRM (Top-k Frequent Balanced Rule Miner), which uses support, kulczynski (kulc) and imbalance ratio (IR) as measures. The algorithm employs three effective pruning strategies to reduce the search space. Experiments were conducted to evaluate the efficiency of TFBRM, and TFBRM has good scalability, which can be used in many applications.
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
Anand, H.S., Vinodchandra, S.S.: Association rule mining using treap. Int. J. Mach. Learn. Cybern. 9(4), 589–597 (2016). https://doi.org/10.1007/s13042-016-0546-7
Aqra, I., Ghani, N.A., Maple, C., Machado, J., Safa, N.S.: Incremental algorithm for association rule mining under dynamic threshold. Appl. Sci. 9(24), 5398 (2019)
Bustio-Martínez, L., Letras-Luna, M., Cumplido, R., Hernández-León, R., Feregrino-Uribe, C., Bande-Serrano, J.M.: Using hashing and lexicographic order for frequent itemsets mining on data streams. J. Parallel Distrib. Comput. 125, 58–71 (2019)
Chun-Wei Lin, J., Gan, W., Fournier-Viger, P., Hong, T.P., Tseng, V.S.: Fast algorithms for mining high-utility itemsets with various discount strategies. Adv. Eng. Inform. 30(2), 109–126 (2016)
Czibula, G., Czibula, I.G., Miholca, D.L., Crivei, L.M.: A novel concurrent relational association rule mining approach. Expert Syst. Appl. 125, 142–156 (2019)
Djenouri, Y., Belhadi, A., Fournier-Viger, P.: Extracting useful knowledge from event logs: a frequent itemset mining approach. Knowl.-Based Syst. 139, 132–148 (2018)
Fournier-Viger, P., Wu, C.-W., Tseng, V.S.: Mining top-k association rules. In: Kosseim, L., Inkpen, D. (eds.) AI 2012. LNCS (LNAI), vol. 7310, pp. 61–73. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30353-1_6
Fournier-Viger, P., Zhang, Y., Chun-Wei Lin, J., Fujita, H., Koh, Y.S.: Mining local and peak high utility itemsets. Inf. Sci. 481, 344–367 (2019)
Gan, W., Lin, J.C.W., Fournier-Viger, P., Chao, H.C., Hong, T.P., Fujita, H.: A survey of incremental high-utility itemset mining. Wiley Int. Rev. Data Min. Knowl. Disc. 8(2), e1242 (2018)
Han, X., Liu, X., Chen, J., Lai, G., Gao, H., Li, J.: Efficiently mining frequent itemsets on massive data. IEEE Access 7, 31409–31421 (2019)
Huang, M.-J., Sung, H.-S., Hsieh, T.-J., Wu, M.-C., Chung, S.-H.: Applying data-mining techniques for discovering association rules. Soft. Comput. 24(11), 8069–8075 (2019). https://doi.org/10.1007/s00500-019-04163-4
Mai, T., Vo, B., Nguyen, L.T.T.: A lattice-based approach for mining high utility association rules. Inf. Sci. 399, 81–97 (2017)
Moslehi, F., Haeri, A., Martínez-Álvarez, F.: A novel hybrid GA-PSO framework for mining quantitative association rules. Soft. Comput. 24(6), 4645–4666 (2020)
Nguyen, L.T.T., Vo, B., Nguyen, L.T.T., Fournier-Viger, P., Selamat, A.: ETARM: an efficient top-k association rule mining algorithm. Appl. Intell. 48(5), 1148–1160 (2017). https://doi.org/10.1007/s10489-017-1047-4
Sharmila, S., Vijayarani, S.: Association rule mining using fuzzy logic and whale optimization algorithm. Soft. Comput. 25(2), 1431–1446 (2020). https://doi.org/10.1007/s00500-020-05229-4
Sohrabi, M.K., Roshani, R.: Frequent itemset mining using cellular learning automata. Comput. Hum. Behav. 68, 244–253 (2017)
Telikani, A., Gandomi, A.H., Shahbahrami, A.: A survey of evolutionary computation for association rule mining. Inf. Sci. 524, 318–352 (2020)
Varol Altay, E., Alatas, B.: Intelligent optimization algorithms for the problem of mining numerical association rules. Physica A: Stat. Mech. Appl. 540, 123142 (2020)
Wang, L., Meng, J., Xu, P., Peng, K.: Mining temporal association rules with frequent itemsets tree. Appl. Soft Comput. J. 62, 817–829 (2018)
Webb, G.I.: Filtered-top-k association discovery. Wiley Interdisc. Rev.-Data Mining Knowl. Discov. 1(3), 183–192 (2011)
Webb, G.I., Zhang, S.: K-optimal rule discovery. Data Min. Knowl. Disc. 10(1), 39–79 (2005)
Wen, F., Zhang, G., Sun, L., Wang, X., Xu, X.: A hybrid temporal association rules mining method for traffic congestion prediction. Comput. Ind. Eng. 130(6), 779–787 (2019)
Acknowledgments
This research is sponsored by the Science and Technology Planning Project of Sichuan Province under Grant No. 2020YFG0054, and the Joint Funds of the Ministry of Education of China.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, X., Niu, X., Kuang, J., Yang, S., Liu, P. (2021). Fast Mining of Top-k Frequent Balanced Association Rules. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12798. Springer, Cham. https://doi.org/10.1007/978-3-030-79457-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-79457-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-79456-9
Online ISBN: 978-3-030-79457-6
eBook Packages: Computer ScienceComputer Science (R0)