Abstract
High utility sequential pattern mining is to mine sequences with high utility (e.g. profits) but probably with low frequency. In some applications such as marketing analysis, high utility sequential patterns are usually more useful than sequential patterns with high frequency. In this paper, we devise two pruning strategies RSU and PDU, and propose HUS-Span algorithm based on these two pruning strategies to efficiently identify high utility sequential patterns. Experimental results show that HUS-Span algorithm outperforms prior algorithms by pruning more low utility sequences.
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Notes
- 1.
Due to the page limit, readers can refer to [2] for more details about LQS-Tree.
- 2.
The details of PDU will be described in Sect. 7.3.3.
- 3.
The details of RSU will be described in Sect. 7.3.2.
References
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© 2014 Springer Science+Business Media Dordrecht
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Wang, JZ., Yang, ZH., Huang, JL. (2014). An Efficient Algorithm for High Utility Sequential Pattern Mining. In: Park, J., Zomaya, A., Jeong, HY., Obaidat, M. (eds) Frontier and Innovation in Future Computing and Communications. Lecture Notes in Electrical Engineering, vol 301. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8798-7_7
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DOI: https://doi.org/10.1007/978-94-017-8798-7_7
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