  For applied intelligence, utility-driven pattern discovery algorithms can
identify insightful and useful patterns in databases. However, in these
techniques for pattern discovery, the number of patterns can be huge, and the
user is often only interested in a few of those patterns. Hence, targeted
high-utility itemset mining has emerged as a key research topic, where the aim
is to find a subset of patterns that meet a targeted pattern constraint instead
of all patterns. This is a challenging task because efficiently finding
tailored patterns in a very large search space requires a targeted mining
algorithm. A first algorithm called TargetUM has been proposed, which adopts an
approach similar to post-processing using a tree structure, but the running
time and memory consumption are unsatisfactory in many situations. In this
paper, we address this issue by proposing a novel list-based algorithm with
pattern matching mechanism, named THUIM (Targeted High-Utility Itemset Mining),
which can quickly match high-utility itemsets during the mining process to
select the targeted patterns. Extensive experiments were conducted on different
datasets to compare the performance of the proposed algorithm with
state-of-the-art algorithms. Results show that THUIM performs very well in
terms of runtime and memory consumption, and has good scalability compared to
TargetUM.
