Abstract
In this paper, an efficient algorithm with three pruning strategies are presented to provide tighter upper-bound average-utility of the itemsets, thus reducing the search space for mining the set of high average-utility itemsets (HAUIs). The first strategy finds the relationships of the 2-itemsets, thus reducing the search space of k-itemsets (k ≥ 3). The second and the third pruning strategies set lower upper-bounds of the itemsets to early reduce the unpromising candidates. Substantial experiments show that the proposed algorithm can efficiently and effectively reduce the search space compared to the state-of-the-art algorithms in terms of runtime and number of candidates.
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Lin, J.CW., Ren, S., Fournier-Viger, P., Su, JH., Vo, B. (2017). More Efficient Algorithm to Mine High Average-Utility Patterns. In: Pan, JS., Tsai, PW., Huang, HC. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 64. Springer, Cham. https://doi.org/10.1007/978-3-319-50212-0_13
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DOI: https://doi.org/10.1007/978-3-319-50212-0_13
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