Towards Target Sequential Rules

In many real-world applications, sequential rule mining (SRM) can offer prediction and recommendation functions for a variety of services. It is an important technique of pattern mining to discover all valuable rules that can reveal the temporal relationship between objects. Although several algorithms of SRM are proposed to solve various practical problems, there are no studies on the problem of targeted mining. Targeted sequential rule mining aims to obtain those interesting sequential rules that users focus on, thus avoiding the generation of other invalid and unnecessary rules. It can further improve the efficiency of users in analyzing rules and reduce the consumption of computing resources. In this paper, we first present the relevant definitions of target sequential rules and formulate the problem of targeted sequential rule mining. Then, we propose an efficient algorithm called 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 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 baseline algorithm.