  Traditional high-utility itemset mining (HUIM) aims to determine all
high-utility itemsets (HUIs) that satisfy the minimum utility threshold
(\textit{minUtil}) 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 target-based 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
\textbf{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.
