[1] Unil, Y., Ryang, H., Gangin, L., Fujita, H.: An efficient algorithm for mining high utility patterns from incremental databases with one database scan. Knowl.-Based Syst. 124, 188–206 (2017)

[2] Fournier-Viger, P., Jerry, L.C., Gueniche, T., Barhate, P.: Efficient incremental high utility itemset mining. In: Proceedings of 5th ASE International Conference on Big Data (2015)

[3] Lin, J.C.-W., Gan, W., Hong, T.-P., Pan, J.-S.: Incrementally updating high-utility itemsets with transaction insertion. In: Luo, X., Yu, J.X., Li, Z. (eds.) ADMA 2014. LNCS (LNAI), vol. 8933, pp. 44–56. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-14717-8_4

[4] Judae, L., Unil Yun, Y., Gangin, L., Eunchul, Y.: Efficient incremental high utility pattern mining based on pre-large concept. Eng. Appl. Artif. Intell. 72, 111–123 (2018)

[5] Heungmo, R., Unil, Y.: High utility pattern mining over data streams with sliding window technique. Expert Syst. Appl. 57, 214–231 (2016)

[6] Yun, U., Ryang, H.: Incremental high utility pattern mining with static and dynamic databases. Appl. Intell. 42(2), 323–352 (2014). https://doi.org/10.1007/s10489-014-0601-6

[7] Wu, C.-W., Fournier-Viger, P., Gu, J.-Y., Tseng, V.S.: Mining compact high utility itemsets without candidate generation. In: Fournier-Viger, P., Lin, J.C.-W., Nkambou, R., Vo, B., Tseng, V.S. (eds.) High-Utility Pattern Mining. SBD, vol. 51, pp. 279–302. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-04921-8_11

[8] Thu-Lan, D., Heri, R., Kjetil, N., Quang-Huy, D.: Towards efficiently mining closed high utility itemsets from incremental databases. Knowl.-Based Syst. 165, 13–29 (2019)

[9] Fournier-Viger, P., Zida, S., Lin, J.C.-W., Wu, C.-W., Tseng, V.S.: EFIM-closed: fast and memory efficient discovery of closed high-utility itemsets. In: MLDM 2016. LNCS (LNAI), vol. 9729, pp. 199–213. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41920-6_15

[10] Wu, C.W., Fournier-Viger, P., Gu, J.Y., Tseng, V.S.: Mining closed+ high utility itemsets without candidate generation. In: Proceedings of Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp. 187–194 (2015)

[11] Loan, N.T.T., Bao, V.D., Trinh, N.D.D., Bay, V.: Mining maximal high utility itemsets on dynamic profit databases. Cybern. Syst. 51(2), 140–160 (2020)

[12] Chun-Wei, L., Wensheng, G., Tzung-Pei, H., Binbin, Z.: An incremental high-utility mining algorithm with transaction insertion. Sci. World J. (2015)

[13] Lin, C.-W., Hong, T.-P., Lan, G.-C., Wong, J.-W., Lin, W.-Y.: Incrementally mining high utility patterns based on pre-large concept. Appl. Intell. 40(2), 343–357 (2013). https://doi.org/10.1007/s10489-013-0467-z

[14] Liu, J., Wang, K., Fung, B.: Direct discovery of high utility itemsets without candidate generation. In: International Conference on Data Mining, pp. 984–989 (2012)

[15] Frequent Itemset Mining Dataset Repository (2012). http://fimi.ua.ac.be/

[16] Fournier-Viger, P., Gomariz, A., Soltani, A., Lam, H., Gueniche, T.: Spmf: Open-source data mining platform (2014). http://www.philippe-fournier-viger.com/spmf