Abstract
The aim of mining High Utility Sequential Patterns (HUSPs) is to discover sequential patterns having a high utility (e.g. high profit) based on a user-specified minimum utility threshold. The existing algorithms for mining HUSPs are capable of discovering the complete set of all HUSPs. However, they usually generate a large number of patterns which may be redundant in some cases. The periodic appearance of HUSPs can be regarded as an important criterion to consider the purchase behaviour of customers and measure the interestingness of HUSPs which is very common in real-life applications. In this paper, we focus on periodic high utility sequential patterns (PHUSPs) that are periodically bought by customers and generate a high profit. We proposed an algorithm named PHUSPM (Periodic High Utility Sequential Patterns Miner) to efficiently discover all PHUSPs. The experimental evaluation was performed on six large-scale datasets to evaluate the performance of PHUSPM in terms of execution time, memory usages and scalability. The experimental results show that the PHUSPM algorithm is very efficient by reducing the search space and discarding a considerable amount of non-PHUSPs.
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Acknowledgement
This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.05-2015.07.
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Dinh, T., Huynh, VN., Le, B. (2017). Mining Periodic High Utility Sequential Patterns. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_51
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DOI: https://doi.org/10.1007/978-3-319-54472-4_51
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