Abstract
High-utility sequential pattern mining is an emerging topic in recent decades and most algorithms were designed to identify the complete set of high-utility sequential patterns under the single minimum utility threshold. In this paper, we first propose a novel framework called high-utility sequential pattern mining with multiple minimum utility thresholds to mine high-utility sequential patterns. A high-utility sequential pattern with multiple minimum utility thresholds algorithm, a lexicographic sequence (LS)-tree, and the utility-linked (UL)-list structure are respectively designed to efficiently mine the high-utility sequential patterns (HUSPs). Three pruning strategies are then introduced to lower the upper-bound values of the candidate sequences, and reduce the search space by early pruning the unpromising candidates. Substantial experiments on real-life datasets show that our proposed algorithms can effectively and efficiently mine the complete set of HUSPs with multiple minimum utility thresholds.
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Acknowledgment
This research was partially supported by the National Natural Science Foundation of China (NSFC) under grant No. 6150309, by the Research on the Technical Platform of Rural Cultural Tourism Planning Basing on Digital Media under grant 2017A020220011, and by the CCF-Tencent Project under grant No. IAGR20160115.
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Lin, J.CW., Zhang, J., Fournier-Viger, P. (2017). High-Utility Sequential Pattern Mining with Multiple Minimum Utility Thresholds. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_17
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DOI: https://doi.org/10.1007/978-3-319-63579-8_17
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