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Generalized maximal utility for mining high average-utility itemsets

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Abstract

Mining high average-utility itemsets (HAUIs) is a promising research topic in data mining because, in contrast to high utility itemsets, they are not biased toward long itemsets. Regardless of what upper bounds and pruning strategies are used, most existing HAUI mining algorithms are founded on the concept of maximal utility, namely the highest utility of a single item in each transaction. In this paper, we study this problem by generalizing the typical maximal utility and average-utility upper bound from a single item to an itemset, and propose an efficient HAIU mining algorithm based on generalized maximal utility (HAUIM-GMU). For this algorithm, we first propose the concepts of generalized maximal utility and the generalized average-utility upper bound, and discuss how the proposed upper bound can be made tighter to generate fewer candidates. A new pruning strategy is then proposed based on the concept of support, and this is shown to be effective for filtering out unpromising itemsets. The final algorithm is described in detail. Extensive experimental results show that the HAUIM-GMU algorithm outperforms existing state-of-the-art algorithms.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions, which helped to improve the quality of this paper. This work was partially supported by the National Natural Science Foundation of China (61977001) and the Great Wall Scholar Program (CIT & TCD20190305).

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Authors and Affiliations

  1. School of Information Science and Technology, North China University of Technology, Beijing, 100144, China

    Wei Song, Lu Liu & Chaomin Huang

  2. Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, Beijing, 100144, China

    Wei Song

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  1. Wei Song
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  2. Lu Liu
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  3. Chaomin Huang
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Correspondence to Wei Song.

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Song, W., Liu, L. & Huang, C. Generalized maximal utility for mining high average-utility itemsets. Knowl Inf Syst 63, 2947–2967 (2021). https://doi.org/10.1007/s10115-021-01614-z

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