  In many real-world applications, sequential rule mining (SRM) can provide
prediction and recommendation functions for a variety of services. It is an
important technique of pattern mining to discover all valuable rules that
belong to high-frequency and high-confidence sequential rules. Although several
algorithms of SRM are proposed to solve various practical problems, there are
no studies on target sequential rules. Targeted sequential rule mining aims at
mining the interesting sequential rules that users focus on, thus avoiding the
generation of other invalid and unnecessary rules. This approach can further
improve the efficiency of users in analyzing rules and reduce the consumption
of data resources. In this paper, we provide the relevant definitions of target
sequential rule and formulate the problem of targeted sequential rule mining.
Furthermore, we propose an efficient algorithm, called targeted sequential rule
mining (TaSRM). Several pruning strategies and an optimization are introduced
to improve the efficiency of TaSRM. Finally, a large number of experiments are
conducted on different benchmarks, and we analyze the results in terms of their
running time, memory consumption, and scalability, as well as query cases with
different query rules. It is shown that the novel algorithm TaSRM and its
variants can achieve better experimental performance compared to the existing
baseline algorithm.
