Affiliations: Department of Computer Science and Engineering, North Minzu University, NingXia, China
Correspondence:
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Corresponding author. Meng Han, Department of Computer Science and Engineering, North Minzu University, NingXia, China. E-mail: [email protected]; [email protected]
Abstract: High utility itemset mining (HUIM) with negative utility is an emerging data mining task. However, the setting of the minimum utility threshold is always a challenge when mining high utility itemsets (HUIs) with negative items. Although the top-k HUIM method is very common, this method can only mine itemsets with positive items, and the problem of missing itemsets occurs when mining itemsets with negative items. To solve this problem, we first propose an effective algorithm called THN (Top-k High Utility Itemset Mining with Negative Utility). It proposes a strategy for automatically increasing the minimum utility threshold. In order to solve the problem of multiple scans of the database, it uses transaction merging and dataset projection technology. It uses a redefined sub-tree utility value and a redefined local utility value to prune the search space. Experimental results on real datasets show that THN is efficient in terms of runtime and memory usage, and has excellent scalability. Moreover, experiments show that THN performs particularly well on dense datasets.
Keywords: Utility mining, high utility itemsets mining, top-k high utility itemsets, negative utility
