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.
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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.
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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
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DOI: https://doi.org/10.1007/978-3-030-19063-7_71
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