Abstract
Most existing data mining algorithms focused on mining the information from the static database. In this paper, the principle of pre-large is used to update the newly discovered HAUIs and reduce the time of the rescanning process. To further improve the performance of the suggested algorithm, two new upper-bounds are also proposed to decrease the number of candidates for HAUIs. The experimental results show the proposed algorithm has excellent performance and good potential to be applied in real applications.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: The 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)
Ahmed, C.F., Tanbeer, S.K., Jeong, B.S., Lee, Y.K.: Efficient tree structures for high utility pattern mining in incremental databases. IEEE Trans. Knowl. Data Eng. 21(12), 1708–1721 (2009)
Cheung, D.W., Han, J., Ng, V.T., Wang, C.Y.: Maintenance of discovered association rules in large databases: an incremental updating technique. In: The International Conference on Data Engineering, pp. 106–114 (2002)
Chen, M.S., Park, J.S., Yu, P.: Efficient data mining for path traversal patterns. IEEE Trans. Knowl. Data Eng. 10(2), 209–221 (1998)
Chen, C.M., Xiang, B., Liu, Y., Wang, K.H.: A secure authentication protocol for intermet of vehicles. IEEE Access 7, 12047–12057 (2019)
Fournier-Viger, P., et al.: The SPMF open-source data mining library version 2. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 36–40 (2016)
Gan, W., Lin, J.C.W., Fournier-Viger, P., Chao, H.C., Yu, S.: A survey of parallel sequential pattern mining. ACM Trans. Knowl. Disc. Data 13(3), 1–34 (2019). Article 25
Gan, W., Lin, J.C.W., Fournier-Viger, P., Chao, H.C., Tseng, V., Yu, P.: A survey of utility-oriented pattern mining. IEEE Trans. Knowl. Data Eng. (2019)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Disc. 8, 53–87 (2004)
Hong, T.P., Lin, C.W., Wu, Y.L.: Incrementally fast updated frequent pattern trees. Expert Syst. Appl. 34, 2424–2435 (2008)
Hong, T.P., Wang, C.Y., Tao, Y.H.: A new incremental data mining algorithm using pre-large itemsets. Intell. Data Anal. 5(2), 111–129 (2001)
Hong, T.P., Lee, C.H., Wang, S.L.: An incremental mining algorithm for high average-utility itemsets. In: The International Symposium on Pervasive Systems, Algorithms, and Networks, pp. 421–425 (2009)
Hong, T.P., Lee, C.H., Wang, S.L.: Effective utility mining with the measure of average utility. Expert Syst. Appl. 38(7), 8259–8265 (2011)
Kim, N.V.: Some determinants affecting purchase intention of domestic products at local markets in Tien Giang province, Vietnam. Data Sci. Pattern Recogn. 1(2), 43–52 (2017)
Lin, C.W., Hong, T.P., Lu, W.H.: The pre-FUFP algorithm for incremental mining. Expert Syst. Appl. 36(5), 9498–9505 (2009)
Lin, C.W., Lan, G.C., Hong, T.P.: An incremental mining algorithm for high utility itemsets. Expert Syst. Appl. 39(8), 7173–7180 (2009)
Lin, C.W., Hong, T.P., Lu, W.H.: Efficiently mining high average utility itemsets with a tree structure. In: The Asian Conference on Intelligent Information and Database Systems, pp. 131–139 (2010)
Lin, C.W., Hong, T.P., Lu, W.H.: An effective tree structure for mining high utility itemsets. Expert Syst. Appl. 38(6), 7419–7424 (2011)
Liu, M., Qu, J.: Mining high utility itemsets without candidate generation. In: ACM International Conference on Information and Knowledge Management, pp. 55–64 (2012)
Lan, G.C., Hong, T.P., Tseng, V.: Efficient mining high average-utility itemsets with an improved upper-bound strategy. Int. J. Inform. Technol. Decis. Making 11, 1009–1030 (2012)
Lin, C.W., Hong, T.P., Lin, W.Y., Lan, G.C.: Efficient updating of sequential patterns with transaction insertion. Intell. Data Anal. 18, 1013–1026 (2014)
Lu, T., Vo, B., Nguyen, H.T., Hong, T.P.: A new method for mining high average utility itemsets. In: IFIP International Conference on Computer Information Systems and Industrial Management, pp. 33–42 (2015)
Liu, J., Wang, K., Fung, B.C.M.: Mining high utility patterns in one phase without generating candidates. IEEE Trans. Knowl. Data Eng. 28(5), 1245–1257 (2016)
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)
Su, J.H., Chang, W.Y., Tseng, V.S.: Integrated mining of social and collaborative information for music recommendation. Data Sci. Pattern Recogn. 1(1), 13–30 (2017)
Tseng, V., Shie, B.E., Wu, C.W., Yu, P.: Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Trans. Knowl. Data Eng. 25, 1772–1786 (2013)
Wang, Z., Chen, K., He, L.: AsySIM: modeling asymmetric social influence for rating prediction. Data Sci. Pattern Recogn. 2(1), 25–40 (2018)
Wu, J.M.T., Lin, J.C.W., Tamrakar, A.: High-utility itemset mining with effective pruning strategies. ACM Trans. Knowl. Disc. Data 13(6), 1–22 (2019)
Yao, H., Hamilton, H.J., Butz, C.J.: A foundational approach to mining itemset utilities from databases. In: The SIAM International Conference on Data Mining, pp. 215–221 (2004)
Zida, S., Fournier-Viger, P., Lin, J.C.-W., Wu, C.-W., Tseng, V.S.: EFIM: a fast and memory efficient algorithm for high-utility itemset mining. Knowl. Inf. Syst. 51(2), 595–625 (2016). https://doi.org/10.1007/s10115-016-0986-0
Liu, Y., Liao, W.K., Choudhary, A.: A two-phase algorithm for fast discovery of high utility itemsets. In: The Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp. 689–695 (2005)
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Wu, J.MT., Teng, Q., Lin, J.CW., Fournier-Viger, P., Cheng, CF. (2020). Maintenance of Prelarge High Average-Utility Patterns in Incremental Databases. 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_75
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