Abstract
Fuzzy utility mining (FUM) techniques are used to mine high fuzzy utility itemsets for market analysis. They consider items that include purchase quantities, unit profits, and linguistic terms representing quantity information. Although FUM facilitates market analysis, it has the measurement problem in which the fuzzy utility value for an itemset may be higher than that for its subset. In the past, a tree-based mining method was proposed to find fuzzy average-utility itemsets using a two-stage strategy tree-based method with an average-utility measure. It was, however, computationally expensive because two-stage processing was needed. To handle this, we propose a single-stage tree-structure-based method that uses an external list for each node in the tree to find fuzzy average-utility itemsets efficiently. Experimental results show that the proposed method outperforms the former approach in terms of execution time.
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References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: The ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: The 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)
Yao, H., Hamilton, H., Butz, C.: A foundational approach to mining itemset utilities from databases. In: The 4th SIAM International Conference on Data Mining, pp. 211–225 (2004)
Liu, Y., Liao, W.-K., 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
Lan, G.C., Hong, T.P., Lin, Y.H., Wang, S.L.: Fuzzy utility mining with upper-bound measure. Appl. Soft Comput. 30, 767–777 (2015)
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)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: The ACM SIGMOD International Conference on Management of Data, vol. 29, pp. 1–12 (2000)
Hong, T.P., Ku, M.P., Huang, W.M., Li, S.M., Lin, J.C.W.: A tree-based fuzzy average-utility mining algorithm. In: IEEE International Conference on Data Mining (2020)
Hong, T.P., Ku, M.P., Huang, W.M., Li, S.M., Lin, J.C.W.: Mining high fuzzy average-utility itemsets. In: The International Conference on System Science and Engineering (2020)
IBM Quest Data Mining Projection, Quest synthetic data generation code (1996). http://www.almaden.ibm.com/cs/quest/syndata.htm
Wu, J.M.-T., Teng, Q., Lin, J.C.-W., Fournier-Viger, P., Cheng, C.-F.: Maintenance of prelarge high average-utility patterns in incremental databases. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds.) IEA/AIE 2020. LNCS (LNAI), vol. 12144, pp. 884–895. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-55789-8_75
Truong, T.C., Duong, H.V., Le, B., Fournier-Viger, P.: Efficient high average-utility itemset mining using novel vertical weak upper-bounds. Knowl. Based Sys. 183, (2019). https://doi.org/10.1016/j.knosys.2019.07.018
Yildirim, I., Celik, M.: An efficient tree-based algorithm for mining high average-utility itemsets. IEEE ACCESS 7, 144245–144263 (2019). https://doi.org/10.1109/ACCESS.2019.2945840
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Hong, TP., Ku, MP., Chiu, HW., Huang, WM., Li, SM., Lin, J.CW. (2021). A Single-Stage Tree-Structure-Based Approach to Determine Fuzzy Average-Utility Itemsets. 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_6
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