Finding high-importance patterns in data is an emerging data mining task known as High-utility itemset mining (HUIM). Given a minimum utility threshold, a HUIM algorithm extracts all the high-utility itemsets (HUIs) whose utility values are not less than the threshold. This can reveal a wealth of useful information, but the precise needs of users are not well taken into account. In particular, users often want to focus on patterns that have some specific items rather than find all patterns. To overcome that difficulty, targeted mining has emerged, focusing on user preferences, but only preliminary work has been conducted. For example, the targeted high-utility itemset querying algorithm (TargetUM) was proposed, which uses a lexicographic tree to query itemsets containing a target pattern. However, selecting the minimum utility threshold is difficult when the user is not familiar with the processed database. As a solution, this paper formulates the task of targeted mining of the top-k high-utility itemsets and proposes an efficient algorithm called TMKU based on the TargetUM algorithm to discover the top-k target high-utility itemsets (top-k THUIs). At the same time, several pruning strategies are used to reduce memory consumption and execution time. Extensive experiments show that the proposed TMKU algorithm has good performance on real and synthetic datasets.

翻译：发现数据中的高重要性模式是一项新兴的数据挖掘任务，称为高效用项集挖掘（HUIM）。给定最小效用阈值，HUIM算法会提取所有效用值不低于该阈值的高效用项集（HUI）。这虽然能揭示大量有用信息，但未能充分考虑用户的精准需求。尤其用户往往希望聚焦于包含特定项的特定模式，而非挖掘所有模式。为解决这一难题，目标挖掘技术应运而生，其专注于用户偏好，但目前仅有初步研究工作。例如，已有研究者提出目标高效用项集查询算法（TargetUM），该算法利用字典树查询包含目标模式的项集。然而，当用户对处理数据库不熟悉时，最小效用阈值的选取较为困难。为此，本文定义了面向Top-K高效用项集的目标挖掘任务，并基于TargetUM算法提出一种名为TMKU的高效算法，用于发现Top-K目标高效用项集（Top-K THUI）。同时，采用多种剪枝策略以减少内存消耗和执行时间。大量实验表明，所提出的TMKU算法在真实数据集和合成数据集上均具有良好的性能。