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An efficient method for mining top-k multi-level high utility itemsets
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PRE
AI
DOI：10.1016/j.knosys.2025.113359.png)
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Abstract
En 中文
High utility itemset mining (HUIM) is employed for analysing user behaviour. It is an extended form of frequent itemset mining. Top-k HUIM provides a solution to the challenge of determining a suitable minimum utility threshold. Although HUIM algorithms have been improved and implemented with high performance, mining hierarchical data has not received considerable attention. The memory and runtime requirements of top-k HUIM algorithms on hierarchical databases remain high. This study proposes a method, called top-k multi-level high utility itemset (TK-MLHUI) mining, to make mining more efficient. The method uses several strategies, including sub-tree and local utility, to reduce the search space by applying stricter upper bounds. Any level of hierarchical data may be used with the upper bounds. It also suggests an approach for reducing the data required for scanning through various strategies, including combining items on lists at mining levels and updating promising items. Furthermore, a strategy to efficiently raise the threshold using the utility list of items is introduced. Experiments using various databases allow for the measurement of the method's performance. Compared with a previous method - mlTKO, TK-MLHUI decreases execution time by more than 3700 times while also using less memory.
Keywords:
Data mining
Hierarchical data
Multi-level high utility itemset mining
Top-k HUI mining
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