Abstract
Several approaches have been introduced to solve the problem of high utility pattern mining (HUPM). However, the proposed algorithms require a minimum utility threshold before execution. This task is impractical for end users as they do not know utility distributions in the transaction datasets. The output will contain too many patterns if this value is too low. In contrast, if the threshold is set too high, the result would be empty or insufficient for analysis. Recently, HUPM was extended to work with hierarchical transaction datasets. With the search space of the mining task expanded, selecting a proper threshold is far more challenging. To address this issue, we propose a top-\(k\) high utility pattern mining method from multi-level transactions databases. The users only need to specify a \(k\) value, denotes the desired number of patterns of interest. To the best of our knowledge, the method proposed in our work is the first to address this mining topic. Experiments on both real and synthetic hierarchical datasets were extensively conducted to evaluate the performance of the proposed algorithm.
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This research is funded by Vietnam National University HoChiMinh City (VNU-HCM) under grant number B2023-28-02.
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Le, T.M., Nguyen, T.D.D., Nguyen, L.T.T., Kozierkiewicz, A., Tung, N.T. (2023). Extracting Top-k High Utility Patterns from Multi-level Transaction Databases. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2023. Lecture Notes in Computer Science(), vol 13995. Springer, Singapore. https://doi.org/10.1007/978-981-99-5834-4_24
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