Algorithms Based on Dynamic Minimum Probabilistic Support and Utility Thresholds for Mining Top-K High-Utility Itemsets From Uncertain Databases

Mining high-utility itemsets (HUIs) from large and uncertain databases is challenging due to the vast data volume and extensive search space. Determining the minimum utility and probabilistic support for all items is also a time-consuming task. Setting these thresholds too high results in too few itemsets, whereas setting them too low leads to an excessive number of itemsets and high computational costs. To address these issues, we developed solutions for mining Top-K high-utility itemsets from uncertain databases. First, we formally define the problem of mining Top-K high-utility itemsets from uncertain databases. Based on this formulation, we propose four algorithms: FTKHUUIM+, ITUHUFP, TUHUFP, and TKUU, which effectively meet the problem requirements. These algorithms adopt automated threshold-raising strategies and specialized storage structures to ensure optimal performance. Experimental results on public benchmark datasets demonstrate the scalability and robust performance of the proposed algorithms across both dense and sparse datasets in terms of runtime and efficiency. All of them share the common feature of pruning candidate itemsets based on dynamic minimum probabilistic support and utility thresholds. Although their pruning strategies differ, all algorithms follow a recursive process to select Top-K HUIs by pairing items from the database and filtering patterns according to the thresholds. Among the proposed algorithms, the ITUHUFP algorithm shows the best performance and stability on various benchmark datasets.