Abstract
Many algorithms have been proposed to find high utility itemsets (sets of items that yield a high profit) in customer transactions. Though, it is useful to analyze customer behavior, it ignores information about item categories. To consider a product taxonomy and find high utility itemsets describing relationships between items and categories, the ML-HUI Miner was recently proposed. But it cannot find cross-level itemsets (itemsets mixing items from different taxonomy levels), and it is inefficient as it does not use relationships between categories to reduce the search space. This paper addresses these issues by proposing a novel problem called cross-level high utility itemset mining, and an algorithm named CLH-Miner. It relies on novel upper bounds to efficiently search for high utility itemsets when considering a taxonomy. An experimental evaluation with real retail data shows that the algorithm is efficient and can discover insightful patterns describing customer purchases.
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References
Cagliero, L., Cerquitelli, T., Garza, P., Grimaudo, L.: Misleading generalized itemset discovery. Expert Syst. Appl. 41, 1400–1410 (2014)
Cagliero, L., Chiusano, S., Garza, P., Ricupero, G.: Discovering high-utility itemsets at multiple abstraction levels. In: Proceedings of 21st European Conference on Advances in Databases and Information Systems, pp. 224–234 (2017)
Fournier-Viger, P., Cheng, C., Lin, J.C.-W., Yun, U., Kiran, R.U.: TKG: efficient mining of top-K frequent subgraphs. In: Madria, S., Fournier-Viger, P., Chaudhary, S., Reddy, P.K. (eds.) BDA 2019. LNCS, vol. 11932, pp. 209–226. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37188-3_13
Fournier-Viger, P., et al.: The SPMF open-source data mining library version 2. In: Berendt, B., et al. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9853, pp. 36–40. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46131-1_8
Fournier-Viger, P., Chun-Wei Lin, J., Truong-Chi, T., Nkambou, R.: A survey of high utility itemset mining. In: Fournier-Viger, P., Lin, J.C.-W., Nkambou, R., Vo, B., Tseng, V.S. (eds.) High-Utility Pattern Mining. SBD, vol. 51, pp. 1–45. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-04921-8_1
Fournier-Viger, P., Lin, J.C.W., Vo, B., Chi, T.T., Zhang, J., Le, B.: A survey of itemset mining. WIREs Data Min. Knowl. Discov. 7(4), e1207 (2017)
Fournier-Viger, P., Wu, C.-W., Zida, S., Tseng, V.S.: FHM: faster high-utility itemset mining using estimated utility co-occurrence pruning. In: Andreasen, T., Christiansen, H., Cubero, J.-C., Raś, Z.W. (eds.) ISMIS 2014. LNCS (LNAI), vol. 8502, pp. 83–92. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08326-1_9
Fournier-Viger, P., Yang, P., Lin, J.C.-W., Yun, U.: HUE-span: fast high utility episode mining. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds.) ADMA 2019. LNCS (LNAI), vol. 11888, pp. 169–184. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35231-8_12
Fournier-Viger, P., Yang, Y., Yang, P., Lin, J.C.W., Yun, U.: Tke: Mining top-k frequent episodes. In: Proceedings of 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 832–845. Springer (2020)
Han, J., Fu, Y.: Mining multiple-level association rules in large databases. IEEE Trans. Knowl. Data Eng. 11, 798–804 (1999)
Hashem, T., Ahmed, C.F., Samiullah, M., Akther, S., Jeong, B.S., Jeon, S.: An efficient approach for mining cross-level closed itemsets and minimal association rules using closed itemset lattices. Expert Syst. Appl. 41, 2914–2938 (2014)
Hipp, J., Myka, A., Wirth, R., Güntzer, U.: A new algorithm for faster mining of generalized association rules. In: Proceedings of 2nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 74–82 (1998)
Liu, Y., Keng Liao, W., Choudhary, A.N.: A two-phase algorithm for fast discovery of high utility itemsets. In: Proceedings of 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 689–695 (2005)
Lui, C.L., Chung, K.F.L.: Discovery of generalized association rules with multiple minimum supports. In: Proceedings of 4th European Conference Principles of Data Mining and Knowledge Discovery, pp. 510–515 (2000)
Luna, J.M., Fournier-Viger, P., Ventura, S.: Frequent itemset mining: a 25 years review. WIREs Data Min. Knowl. Discov. 9(6), e1329 (2019)
Mao, Y.X., Shi, B.L.: AFOPT-Tax: an efficient method for mining generalized frequent itemsets. In: Proceedings of 2nd International Conference on Intelligent Information and Database Systems, pp. 82–92 (2010)
Leong Ong, K., Ng, W.K., Lim, E.P.: Mining multi-level rules with recurrent items using fp’-tree. In: Proceedings of 3rd International Conference Information Communication and Signal Processing (2001)
Pramudiono, I.: Fp-tax: tree structure based generalized association rule mining. In: Proceedings of ACM/SIGMOD International Workshop on Research Issues on Data Mining and Knowledge Discovery, pp. 60–63 (2004)
Qu, J.-F., Liu, M., Fournier-Viger, P.: Efficient algorithms for high utility itemset mining 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. 131–160. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-04921-8_5
Rajkumar, N.D., Karthik, M.R., Sivanandam, S.N.: Fast algorithm for mining multilevel association rules. In: Proceedings of 2003 TENCON Conference on Convergent Technologies for Asia-Pacific Region, vol. 2, pp. 688–692 (2003)
Srikant, R., Agrawal, R.: Mining generalized association rules. In: Proceedings of 21th International Conference on Very Large Data Bases (1995)
Sriphaew, K., Theeramunkong, T.: A new method for finding generalized frequent itemsets in generalized association rule mining. In: Proceedings of 7th International Conference Knowledge Based Intelligent Information and Engineering Systems, pp. 476–484 (2002)
Xu, Y., Zeng, M., Liu, Q., Wang, X.: A genetic algorithm based multilevel association rules mining for big datasets. Math. Prob. Eng. 2014, 9 (2014)
Yun, U., Kim, D., Yoon, E., Fujita, H.: Damped window based high average utility pattern mining over data streams. Knowl.-Based Syst. 144, 188–205 (2018)
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Fournier-Viger, P., Wang, Y., Lin, J.CW., Luna, J.M., Ventura, S. (2020). Mining Cross-Level High Utility Itemsets. 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_73
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