Abstract
Erasable-itemset mining has become a popular research topic and is usually used for product production planning in the industry. If some products in a factory may be removed without critically affecting production profits, the set composed of them is called an erasable itemset. Erasable-itemset mining is to find all the removable material sets for saving funds. This paper extends the concept of erasable itemsets to consider customer behavior with a sequence of orders. We consider the scenario that when an item (material) is not purchased, a product using that material cannot be manufactured, and clients will cancel all their orders if at least one such order exists. We propose a modified erasable-itemset mining algorithm for solving the above problem. Finally, experiments with varying thresholds are conducted to evaluate the execution time and mining results of the proposed algorithm considering customer behavior.
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References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: The 27th 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 Very Large Data Bases Conference, pp. 487–499 (1994)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: The 11th International Conference on Data Engineering, pp. 3–14 (1995)
Athira, S., Poojitha, K., Prathibhamol, C.: An efficient solution for multi-label classification problem using apriori algorithm (MLC-A). In: The 6th International Conference on Advances in Computing, Communications and Informatics, pp. 14–18 (2017)
Cheung, D.W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of discovered association rules in large databases: an incremental updating technique. In: The 12th International Conference on Data Engineering, pp. 106–114 (1996)
D’andreagiovanni, M., Baiardi, F., Lipilini, J., Ruggieri, S., Tonelli, F.: Sequential pattern mining for ICT risk assessment and management. J. Log. Algebr. Methods Program. 102, 1–16 (2019)
Deng, Z.H., Fang, G.D., Wang, Z.H., Xu, X.R.: Mining erasable itemsets. In: The 8th International Conference on Machine Learning and Cybernetics, pp. 67–73 (2009)
Deng, Z.H., Xu, X.R.: Fast mining erasable itemsets using NC_sets. Expert Syst. Appl. 39(4), 4453–4463 (2012)
Hong, T.P., Lin, K.Y., Lin, C.W., Vo, B.: An incremental mining algorithm for erasable itemsets. In: The 15th IEEE International Conference on Innovations in Intelligent Systems and Applications (2017)
Hong, T.P., Chen, L.H., Wang, S.L., Lin, C.W., Vo, B.: Quasi-erasable itemset mining. In: The 5th IEEE International Conference on Big Data, pp. 1816–1820 (2017)
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., Huang, W.M., Lan, G.C., Chiang, M.C., Lin, C.W.: A bitmap approach for mining erasable itemsets. IEEE Access 9, 106029–106038 (2021)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. ACM SIGMOD Rec. 29(2), 1–12 (2000)
Huang, C.F., Chen, Y.C., Chen, A.P.: An association mining method for time series and its application in the stock prices of TFT-LCD industry. In: The 4th Industrial Conference on Data Mining, pp. 117–126 (2004)
Kundu, S., Bhar, A., Chatterjee, S., Bhattacharyya, S.: Multimedia data mining and its relevance today—an overview. Int. J. Res. Eng., Sci. Manag. 2(5), 994–998 (2019)
Le, T., Vo, B.: MEI: an efficient algorithm for mining erasable itemsets. Eng. Appl. Artif. Intell. 27, 155–166 (2014)
Le, T., Vo, B., Coenen, F.: An efficient algorithm for mining erasable itemsets using the difference of NC-Sets. In: The 43th IEEE International Conference on Systems, Man, and Cybernetics, pp. 2270–2274 (2013)
Nawaz, M.S., Fournier-Viger, P., Song, W., Lin, J.C.W., Noack, B.: Investigating crossover operators in genetic algorithms for high-utility itemset mining. In: The 13th Asian Conference on Intelligent Information and Database Systems, pp. 16–28 (2021)
Singh, K., Singh, S.S., Kumar, A., Biswas, B.: TKEH: an efficient algorithm for mining top-k high utility itemsets. Appl. Intell. 49(3), 1078–1097 (2018). https://doi.org/10.1007/s10489-018-1316-x
Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: The 5th International Conference on Extending Database Technology, pp. 1–17 (1996)
Vo, B., Le, T., Pedrycz, W., Nguyen, G., Baik, S.W.: Mining erasable itemsets with subset and superset itemset constraints. Expert Syst. Appl. 69, 50–61 (2017)
Wang, X., Wang, F., Yan, S., Liu, Z.: Application of sequential pattern mining algorithm in commodity management. J. Electron. Commer. Organ. 16(3), 94–106 (2018)
IBM Quest Data Mining Projection: Quest synthetic data generation code. http://www.almaden.ibm.com/cs/quest/syndata.htm (1996)
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Hong, TP., Chen, YL., Huang, WM., Tsai, YC. (2023). Erasable-Itemset Mining for Sequential Product Databases. In: Abraham, A., Hong, TP., Kotecha, K., Ma, K., Manghirmalani Mishra, P., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2022. Lecture Notes in Networks and Systems, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-031-27409-1_51
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