Abstract
The goal of the high-utility itemset mining task is to discover combinations of items which that yield high profits from transactional databases. HUIM is a useful tool for retail stores to analyze customer behaviors. However, it ignores the categorization of items. To solve this issue, the ML-HUI Miner algorithm was presented. It combines item taxonomy with the HUIM task and is able to discover insightful itemsets, which are not found in traditional HUIM approaches. Although ML-HUI Miner is efficient in discovering itemsets from multiple abstraction levels, it is a sequential algorithm. Thus, it cannot utilize the powerful multi-core processors, which are currently available widely. This paper addresses this issue by extending the algorithm into a multi-core version, called the MCML-Miner algorithm (Multi-Core Multi-Level high-utility itemset Miner), to help reduce significantly the mining time. Each level in the taxonomy will be assigned a separate processor core to explore concurrently. Experiments on real-world datasets show that the MCML-Miner up to several folds faster than the original algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nguyen, N.T.: Advanced Methods for Inconsistent Knowledge Management. AIKP. Springer, London (2008). https://doi.org/10.1007/978-1-84628-889-0
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22(2), 207–216 (1993)
Yao, H., Hamilton, H.J., Butz, G.J.: A foundational approach to mining itemset utilities from databases. In: SIAM International Conference on Data Mining, vol. 4, pp. 482–486 (2004)
Srikant, R., Agrawal, R.: Mining generalized association rules. Future Gener. Comput. Syst. 13(2–3), 161–180 (1997)
Hipp, J., Myka, A., Wirth, R., Güntzer, U.: A new algorithm for faster mining of generalized association rules. In: Żytkow, J.M., Quafafou, M. (eds.) PKDD 1998. LNCS, vol. 1510, pp. 74–82. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0094807
Vo, B., Le, B.: Fast algorithm for mining generalized association rules. Int. J. Database Theory 2(3), 19–21 (2009)
Cagliero, L., Chiusano, S., Garza, P., Ricupero, G.: Discovering high-utility itemsets at multiple abstraction levels. In: Kirikova, M., et al. (eds.) ADBIS 2017. CCIS, vol. 767, pp. 224–234. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67162-8_22
Fournier-Viger, P., Wang, Y., Lin, J.C.-W., Luna, J.M., Ventura, S.: Mining cross-level high utility itemsets. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds.) IEA/AIE 2020. LNCS (LNAI), vol. 12144, pp. 858–871. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-55789-8_73
Fournier-Viger, P., Lin, J.C.W., Vo, B., Chi, T.T., Zhang, J., Le, H.B.: A survey of itemset mining. In: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 7, no. 4 (2017)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Disc. 8(1), 53–87 (2004). https://doi.org/10.1023/B:DAMI.0000005258.31418.83
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
Tseng, V.S., Wu, C.W., Shie, B.E., Yu, P.S.: UP-Growth: an efficient algorithm for high utility itemset mining. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 253–262 (2010)
Liu, M., Qu, J.: Mining high utility itemsets without candidate generation. In: 21st ACM International Conference on Information and Knowledge Management, pp. 55–64 (2012)
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
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
Nguyen, L.T.T., Nguyen, P., Nguyen, T.D.D., Vo, B., Fournier-Viger, P., Tseng, V.S.: Mining high-utility itemsets in dynamic profit databases. Knowl.-Based Syst. 175, 130–144 (2019)
Nouioua, M., Wang, Y., Fournier-Viger, P., Lin, J.C.-W., Wu, J.M.-T.: TKC: mining top-k cross-level high utility itemsets. In: 3rd International Workshop on Utility-Driven Mining (UDML 2020) (2020)
Alias, S., Norwawi, N.M.: pSPADE: mining sequential pattern using personalized support threshold value. In: Proceedings - International Symposium on Information Technology 2008, ITSim, vol. 2, pp. 1–8 (2008)
Cong, S., Han, J., Padua, D.: Parallel mining of closed sequential patterns. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 562–567 (2005)
Zhu, T., Bai, S.: A parallel mining algorithm for closed sequential patterns. In: Proceedings - 21st International Conference on Advanced Information Networking and Applications Workshops/Symposia, AINAW 2007, vol. 2, pp. 392–395 (2007)
Nguyen, T.D.D., Nguyen, L.T.T., Vo, B.: A parallel algorithm for mining high utility itemsets. In: Świątek, J., Borzemski, L., Wilimowska, Z. (eds.) ISAT 2018. AISC, vol. 853, pp. 286–295. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-99996-8_26
Vo, B., Nguyen, L.T.T., Nguyen, T.D.D., Fournier-Viger, P., Yun, U.: A multi-core approach to efficiently mining high-utility itemsets in dynamic profit databases. IEEE Access 8, 85890–85899 (2020)
Nguyen, L.T.T., et al.: Efficient method for mining high-utility itemsets using high-average utility measure. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds.) ICCCI 2020. LNCS (LNAI), vol. 12496, pp. 305–315. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63007-2_24
Nguyen, N.T.: Using consensus methods for solving conflicts of data in distributed systems. In: Hlaváč, V., Jeffery, K.G., Wiedermann, J. (eds.) SOFSEM 2000. LNCS, vol. 1963, pp. 411–419. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-44411-4_30
Nguyen, N.T.: Consensus system for solving conflicts in distributed systems. Inf. Sci. 147(1), 91–122 (2002)
Chen, Y., An, A.: Approximate parallel high utility itemset mining. Big Data Res. 6, 26–42 (2016)
Sethi, K.K., Ramesh, D., Edla, D.R.: P-FHM+: parallel high utility itemset mining algorithm for big data processing. Procedia Comput. Sci. 132, 918–927 (2018)
Acknowledgements
This research is funded by Vietnam National University HoChiMinh City (VNU-HCM) under grant number C2020-28-04.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, T.D.D., Nguyen, L.T.T., Kozierkiewicz, A., Pham, T., Vo, B. (2021). An Efficient Approach for Mining High-Utility Itemsets from Multiple Abstraction Levels. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2021. Lecture Notes in Computer Science(), vol 12672. Springer, Cham. https://doi.org/10.1007/978-3-030-73280-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-73280-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73279-0
Online ISBN: 978-3-030-73280-6
eBook Packages: Computer ScienceComputer Science (R0)