Abstract
In this paper, we propose an efficient algorithm to discover HAUIs based on the compact average-utility list structure. A tighter upper-bound model is used to instead of the traditional auub model used in HAUIM to lower the upper-bound value. Three pruning strategies are also respectively developed to facilitate mining performance of HAUIM. Experiments show that the proposed algorithm outperforms the state-of-the-art HAUIM-MMAU algorithm in terms of runtime and memory usage.
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Acknowledgments
This research was partially supported by the National Natural Science Foundation of China (NSFC) under grant No. 61503092, by the Research on the Technical Platform of Rural Cultural Tourism Planning Basing on Digital Media under grant 2017A020220011, and by the Tencent Project under grant CCF-Tencent IAGR20160115.
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Wu, TY., Lin, J.CW., Ren, S. (2018). Efficient Mining of High Average-Utility Itemsets with Multiple Thresholds. In: Pan, JS., Tsai, PW., Watada, J., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2017. Smart Innovation, Systems and Technologies, vol 81. Springer, Cham. https://doi.org/10.1007/978-3-319-63856-0_25
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DOI: https://doi.org/10.1007/978-3-319-63856-0_25
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