  Utility-driven mining is an important task in data science and has many
applications in real life. High utility sequential pattern mining (HUSPM) is
one kind of utility-driven mining. HUSPM aims to discover all sequential
patterns with high utility. However, the existing algorithms of HUSPM can not
provide an accurate probability to deal with some scenarios for prediction or
recommendation. High-utility sequential rule mining (HUSRM) was proposed to
discover all sequential rules with high utility and high confidence. There is
only one algorithm proposed for HUSRM, which is not enough efficient. In this
paper, we propose a faster algorithm, called US-Rule, to efficiently mine
high-utility sequential rules. It utilizes rule estimated utility co-occurrence
pruning strategy (REUCP) to avoid meaningless computation. To improve the
efficiency on dense and long sequence datasets, four tighter upper bounds
(LEEU, REEU, LERSU, RERSU) and their corresponding pruning strategies (LEEUP,
REEUP, LERSUP, RERSUP) are proposed. Besides, US-Rule proposes rule estimated
utility recomputing pruning strategy (REURP) to deal with sparse datasets. At
last, a large number of experiments on different datasets compared to the
state-of-the-art algorithm demonstrate that US-Rule can achieve better
performance in terms of execution time, memory consumption and scalability.
