High-utility pattern mining is a research hotspot in the field of pattern mining, and one of its main research topics is how to improve the efficiency of the mining algorithm. Based on the study on the state-of-the-art high-utility pattern mining algorithms, this paper proposes an improved strategy that removes noncandidate items from the global header table and local header table as early as possible, thus reducing search space and improving efficiency of the algorithm. The proposed strategy is applied to the algorithm EFIM (EFficient high-utility Itemset Mining). Experimental verification was carried out on nine typical datasets (including two large datasets); results show that our strategy can effectively improve temporal efficiency for mining high-utility patterns.

中文翻译：

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高实用性模式挖掘算法的改进策略

高效模式挖掘是模式挖掘领域的研究热点，其主要研究课题之一是如何提高挖掘算法的效率。在对最新的高实用性模式挖掘算法进行研究的基础上，本文提出了一种改进的策略，该策略可以尽早从全局头表和局部头表中删除非候选项，从而减少搜索空间并改进算法的效率。所提出的策略被应用于算法EFIM（高效的高实用项集挖掘）。对9个典型数据集（包括2个大型数据集）进行了实验验证；结果表明，我们的策略可以有效地提高高效率模式挖掘的时间效率。