Abstract
In this paper, we address a novel and important topic in the area of HUI mining, named Trending High Utility Itemset (TrendHUI) mining, with the promise of expanding the applications of HUI mining with the power of trend analytics. We introduce formal definitions for TrendHUI mining and highlighted the importance of the TrendHUI output. Moreover, we develop two algorithms, Two-Phase Trending High Utility Itemset (TP-THUI) miner and Two-Phase Trending High Utility Itemset Guided (TP-THUI-Guided) miner. Both are two-phase algorithms that mine a complete set of TrendHUI. TP-THUI-Guided miner utilizes a remainder utility to calculate the temporal trend of a given itemset to reduce the search space effectively, such that the execution efficiency can be enhanced substantially. Through a series of experiments, using three different datasets, the proposed algorithms prove to be excellent for validity and efficiency. To the best of our knowledge, this is the first work addressing the promising topic on Trending High Utility Itemset mining, which is expected to facilitate numerous applications in data mining fields.
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This research was partially supported by Ministry of Science and Technology, Taiwan, under grant no. MOST 106-3114-E-009-008 and MOST 104-2221-E-009-128-MY3.
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Hackman, A., Huang, Y., Tseng, V.S. (2018). Mining Trending High Utility Itemsets from Temporal Transaction Databases. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11030. Springer, Cham. https://doi.org/10.1007/978-3-319-98812-2_42
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DOI: https://doi.org/10.1007/978-3-319-98812-2_42
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