Abstract
Though the research topic of high utility itemset (HUI) mining has received extensive attention in recent years, current algorithms suffer from the crucial problem that too many HUIs tend to be produced. This seriously degrades the performance of HUI mining in terms of execution and memory efficiency. Moreover, it is very hard for users to discover meaningful information in a huge number of HUIs. In this paper, we address this issue by proposing a promising framework with a novel algorithm named CHUI (Compact High Utility Itemset)-Mine to discover closed\(^{+}\) HUIs and maximal HUIs, which are compact representations of HUIs. The main merits of CHUI-Mine lie in two aspects: First, in terms of efficiency, unlike existing algorithms that tend to produce a large amount of candidates during the mining process, CHUI-Mine computes the utility of itemsets directly without generating candidates. Second, in terms of losslessness, unlike current algorithms that provide incomplete results, CHUI-Mine can discover the complete closed\(^{+}\) or maximal HUIs with no miss. A comprehensive investigation is also presented to compare the relative advantages of different compact representations in terms of computational cost and compactness. To our best knowledge, this is the first work addressing the issue of mining compact high utility itemsets in terms of closed\(^{+}\) and maximal HUIs without candidate generation. Experimental results show that CHUI-Mine achieves a massive reduction in the number of HUIs and is several orders of magnitude faster than benchmark algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 1–12 (2000)
Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-mine: fast and space-preserving frequent pattern mining in large databases. IIE Trans. 39(6), 593–605 (2007)
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)
Chan, R., Yang, Q., Shen, Y.: Mining high utility itemsets. In: Proceedings of IEEE International Conference on Data Mining, pp. 19–26 (2003)
Gan, W., Lin, J.C.W., Fournier-Viger, P., Chao, H.C., Tseng, V.S., Yu, P.: A survey of utility-oriented pattern mining (2018). arxiv:1805.10511
Li, H.F., Huang, H.Y., Chen, Y.C., Liu, Y.J., Lee, S.Y.: Fast and memory efficient mining of high utility itemsets in data streams. In: Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 881–886 (2008)
Liu, Y., Liao, W., Choudhary, A.: A fast high utility itemsets mining algorithm. In: Proceedings of the Utility-Based Data Mining Workshop, pp. 90–99 (2005)
Tseng, V.S., Shie, B.E., Wu, C.W., Yu, P.S.: Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Trans. Knowl. Data Eng. 25(8), 1772–1786 (2013)
Tseng, V.S., Wu, C.W., Fournier-Viger, P., Yu, P.S.: Efficient algorithms for mining top-k high utility itemsets. IEEE Trans. Knowl. Data Eng. 28(1), 54–67 (2016)
Tseng, V.S., Wu, C.W., Shie, B.E., Yu, P.S.: UP-growth: an efficient algorithm for high utility itemset mining. In: Proceedings of International Conference on ACM SIGKDD, pp. 253–262 (2010)
Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C.W., Tseng, V.S.: SPMF: a Java open-source pattern mining library. J. Mach. Learn. Res. 15, 3389–3393 (2014)
Tseng, V.S., Wu, C.W., Lin, J.H., Fournier-Viger, P.: UP-miner: a utility pattern mining toolbox. In: Proceedings of IEEE International Conference on Data Mining, pp. 1656–1659 (2015)
Li, Y.C., Yeh, J.S., Chang, C.C.: Isolated items discarding strategy for discovering high utility itemsets. Data Knowl. Eng. 64(1), 198–217 (2008)
Liu, M., Qu, J.: Mining high utility itemsets without candidate generation. In: Proceedings of ACM International Conference on Information and knowledge Management, pp. 55–64 (2012)
Shie, B.E., Tseng, V.S., Yu, P.S.: Online mining of temporal maximal utility itemsets from data streams. In: Proceedings of Annual ACM Symposium on Applied Computing, pp. 1622–1626 (2010)
Wu, C.W., Fournier-Viger, P., Gu, J.Y., Tseng, V.S.: Mining closed+ high utility itemsets without candidate generation. In: Proceedings of Conference on Technologies and Applications of Artificial Intelligence, pp. 187–194 (2015)
Boulicaut, J.-F., Bykowski, A., Rigotti, C.: Free-sets: a condensed representation of Boolean data for the approximation of frequency queries. Data Min. Knowl. Discov. 7(1), 5–22 (2003)
Calders, T., Goethals, B.: Mining all non-derivable frequent itemsets. In: Proceedings of European Conference on Principles of Data Mining and Knowledge Discovery, pp. 74–85 (2002)
Gouda, K., Zaki, M.J.: GenMax: an efficient algorithm for mining maximal frequent itemsets. Data Min. Knowl. Discov. 11(3), 223–242 (2005)
Lucchese, C., Orlando, S., Perego, R.: Fast and memory efficient mining of frequent closed itemsets. IEEE Trans. Knowl. Data Eng. 18(1), 21–36 (2006)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Efficient mining of association rules using closed itemset lattice. J. Inf. Syst. 24(1), 25–46 (1999)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Proceedings of International Conference on Database Theory, pp. 398–416 (1999)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Generating a condensed representation for association rules. J. Intell. Inf. Syst. 24(1), 29–60 (2005)
Wang, J., Han, J., Pei, J.: Closet+: searching for the best strategies for mining frequent closed itemsets. In: Proceedings of International Conference on ACM SIGKDD, pp. 236–245 (2003)
Zaki, M.J., Hsiao, C.J.: Efficient algorithms for mining closed itemsets and their lattice structure. IEEE Trans. Knowl. Data Eng. 17(4), 462–478 (2005)
Tseng, V.S., Wu, C.W., Fournier-Viger, P., Yu, P.S.: Efficient algorithms for mining the concise and lossless representation of high utility itemsets. IEEE Trans. Knowl. Data Eng. 27(3), 726–739 (2015)
Wu, C.W., Fournier-Viger, P., Yu, P.S., Tseng, V.S.: Efficient mining of a concise and lossless representation of high utility itemsets. In: Proceedings of IEEE International Conference on Data Mining, pp. 824–833 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Wu, CW., Fournier-Viger, P., Gu, JY., Tseng, V.S. (2019). Mining Compact High Utility Itemsets Without Candidate Generation. In: Fournier-Viger, P., Lin, JW., Nkambou, R., Vo, B., Tseng, V. (eds) High-Utility Pattern Mining. Studies in Big Data, vol 51. Springer, Cham. https://doi.org/10.1007/978-3-030-04921-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-04921-8_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04920-1
Online ISBN: 978-3-030-04921-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)