Abstract
Regular pattern mining has been emerged as one of the important sub-domains of data mining with its numerous applications. Although patterns that occur at a regular interval throughout the whole database can lead to interesting knowledge, examining the utility values of these patterns can unveil more interesting useful information. In a sequence database, the task of mining regular high utility patterns can be more challenging. In this paper, we first propose a new algorithm for mining regular high utility sequential patterns from static databases. As handling of the incremental nature of big data brings useful results in many applications in the recent era of big data, we then extend our algorithm to mine regular high utility sequential patterns from dynamic databases. Evaluation results on several real-life datasets show the effectiveness of our two algorithms.
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
Ahmed, C.F., Tanbeer, S.K., Jeong, B.S.: A novel approach for mining high-utility sequential patterns in sequence databases. ETRI J. 32(5), 676–686 (2010)
Ahmed, C.F., Tanbeer, S.K., Jeong, B.S., Lee, Y.K.: Efficient tree structures for high utility pattern mining in incremental databases. IEEE TKDE 21(12), 1708–1721 (2009)
Alkan, O.K., Karagoz, P.: CRoM and HuspExt: improving efficiency of high utility sequential pattern extraction. IEEE TKDE 27(10), 2645–2657 (2015)
Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential PAttern mining using a bitmap representation. In: ACM KDD 2002, pp. 429–435 (2002)
Cheng, H., Yan, X., Han, J.: IncSpan: incremental mining of sequential patterns in large database. In: ACM KDD 2004, pp. 527–532 (2004)
Choi, P., Hwang, B.: Dynamic weighted sequential pattern mining for USN system. In: ACM IMCOM 2017, pp. 19:1–19:6 (2017)
Dinh, D.T., Le, B., Fournier-Viger, P., Huynh, V.N.: An efficient algorithm for mining periodic high-utility sequential patterns. Appl. Intell. 48(12), 4694–4714 (2018)
Fournier-Viger, P., Lin, J.C.W., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z., Lam, H.T.: The SPMF open-source data mining library version 2. In: PKDD 2016, Part III. pp. 36–40 (2016)
Gunawan, D., Mambo, M.: Set-valued data anonymization maintaining data utility and data property. In: IMCOM 2018, pp. 88:1–88:8 (2018)
Hsu, K.: Effectively mining time-constrained sequential patterns of smartphone application usage. In: ACM IMCOM 2017, pp. 39:1–39:8 (2017)
Lin, J.C.W., Hong, T.P., Gan, W., Chen, H.Y., Li, S.T.: Incrementally updating the discovered sequential patterns based on pre-large concept. Intell. Data Anal. 19(5), 1071–1089 (2015)
Nguyen, S.N., Sun, X., Orlowska, M.E.: Improvements of IncSpan: incremental mining of sequential patterns in large database. In: PAKDD 2005, pp. 442–451 (2005)
Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: Mining sequential patterns by pattern-growth: the PrefixSpan approach. IEEE TKDE 16(11), 1424–1440 (2004)
Rahman, M.M., Ahmed, C.F., Leung, C.K., Pazdor, A.G.M.: Frequent sequence mining with weight constraints in uncertain databases. In: IMCOM 2018, pp. 48:1–48:8 (2018)
Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: EDBT 1996, pp. 3–17 (1996)
Tanbeer, S.K., Ahmed, C.F., Jeong, B.S., Lee, Y.K.: Mining regular patterns in transactional databases. IEICE Trans. Inf. Syst. E91.D(11), 2568–2577 (2008)
Tanbeer, S.K., Ahmed, C.F., Jeong, B.S., Lee, Y.K.: Discovering periodic-frequent patterns in transactional databases. In: PAKDD 2009, pp. 242–253 (2009)
Tseng, V.S., Wu, C.W., Shie, B.E., Yu, P.S.: UP-Growth: an efficient algorithm for high utility itemset mining. In: ACM KDD 2010, pp. 253–262 (2010)
Yao, H., Hamilton, H.J., Butz, C.J.: A foundational approach to mining itemset utilities from databases. In: SIAM SDM 2004, pp. 482–486 (2004)
Yeh, J.S., Li, Y.C., Chang, C.C.: Two-phase algorithms for a novel utility-frequent mining model. In: PAKDD 2007, pp. 433–444 (2007)
Yin, J., Zheng, Z., Cao, L.: USpan: An efficient algorithm for mining high utility sequential patterns. In: ACM KDD 2012, pp. 660–668 (2012)
Zaki, M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1–2), 31–60 (2001)
Acknowledgments
This project is partially supported by NSERC (Canada) and University of Manitoba. In addition, the project is also supported by High-Profile ICT Scholar Fellowship (2017–2018) funded by the Information and Communication Technology (ICT) Division, Ministry of Posts, Telecommunications and Information Technology, Government of the People’s Republic of Bangladesh.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ishita, S.Z., Ahmed, C.F., Leung, C.K., Hoi, C.H.S. (2019). Mining Regular High Utility Sequential Patterns in Static and Dynamic Databases. In: Lee, S., Ismail, R., Choo, H. (eds) Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019. IMCOM 2019. Advances in Intelligent Systems and Computing, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-19063-7_71
Download citation
DOI: https://doi.org/10.1007/978-3-030-19063-7_71
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19062-0
Online ISBN: 978-3-030-19063-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)