In the era of big data, targeted querying of interesting itemsets having concise expressions is promising for improving the efficiency and capability of data mining applications. As the full set of high-utility itemsets is no longer explored, mining becomes more efficient. Nonetheless, identifying whether the current itemset includes the target pattern and is closed remains a challenge for enhancing data analysis efficiency. At present, no single-phase algorithm reliably identifies the targeted closed high-utility itemsets. In this article, we propose an algorithm called TQCUI, Targeted Querying of Closed high-Utility Itemsets containing the target patterns in a transactional database. The algorithm employs a compact attribute-utility-list structure for maintaining the utility and attribute information of the itemsets. Additionally, TQCUI utilizes several efficient pruning strategies to filter out unpromising itemsets, substantially reducing the search space. Moreover, to quickly prune non-closed itemsets, TQCUI introduces forward-extension and backward-extension checking schemes for the closure checking of itemsets. Extensive experimentation on both real and synthetic datasets demonstrates the TQCUI algorithm has good performance in terms of runtime, memory consumption, and scalability.

A recently emerged branch of utility-based research, called high-utility quantitative itemset mining (HUQIM), has been widely applied in real-life, and it considers not only the utility factor but also the quantity with ranges of itemsets. However, most existing utility-mining algorithms assume that pat-terns always appear regardless of the period. For instance, some products may sell well at certain times of the year. Considering the rich information in the database, such as quantity and time, we propose an effective and efficient approach for discovering on-shelf high-utility quantitative itemsets (OHUQIs). To avoid scanning the database multiple times, we adopt a data structure to maintain some necessary information, and thus, OHUQI only accesses the database twice. Several pruning strategies are also designed to prune a large number of unpromising itemsets in advance to shrink the search space. Finally, the subsequent experimental results show that OHUQI performs well on several real-world datasets.

Traditional high-utility itemset mining (HUIM) aims to determine all high-utility itemsets (HUIs) that satisfy the minimum utility threshold in transaction databases. However, in most applications, not all HUIs are interesting because only specific parts are required. Thus, targeted mining based on user preferences is more important than traditional mining tasks. This paper is the first to propose a targeted HUIM problem and to provide a clear formulation of the targeted utility mining task in a quantitative transaction database. A tree-based algorithm known as Target-based high-Utility iteMset querying using (TargetUM) is proposed. The algorithm uses a lexicographic querying tree and three effective pruning strategies to improve the mining efficiency. We implemented experimental validation on several real and synthetic databases, and the results demonstrate that the performance of TargetUM is satisfactory, complete, and correct. Finally, owing to the lexicographic querying tree, the database no longer needs to be scanned repeatedly for multiple queries.