US-Rule: Discovering Utility-driven Sequential Rules

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. It aims at discovering all sequential patterns with high utility. However, the existing algorithms of HUSPM can not provide a relatively accurate probability to deal with some scenarios for prediction or recommendation. High-utility sequential rule mining (HUSRM) is proposed to discover all sequential rules with high utility and high confidence. There is only one algorithm proposed for HUSRM, which is not efficient enough. In this article, we propose a faster algorithm called US-Rule, to efficiently mine high-utility sequential rules. It utilizes the rule estimated utility co-occurrence pruning strategy (REUCP) to avoid meaningless computations. Moreover, to improve its efficiency on dense and long sequence datasets, four tighter upper bounds (LEEU, REEU, LERSU, and RERSU) and corresponding pruning strategies (LEEUP, REEUP, LERSUP, and RERSUP) are designed. US-Rule also proposes the rule estimated utility recomputing pruning strategy (REURP) to deal with sparse datasets. Finally, 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.

Automated summary

In this article, a faster algorithm called US-Rule is proposed for efficiently mining high-utility sequential rules. It utilizes the rule estimated utility co-occurrence pruning strategy to avoid meaningless computations. Moreover, four tighter upper bounds and corresponding pruning strategies are designed to improve efficiency on dense and long sequence datasets. US-Rule also proposes the rule estimated utility recomputing pruning strategy to deal with sparse datasets. Experimental results demonstrate that US-Rule outperforms existing algorithms in terms of execution time, memory consumption, and scalability.