US-Rule: Discovering Utility-driven Sequential Rules

Author:

Huang Gengsen1ORCID,Gan Wensheng1ORCID,Weng Jian1ORCID,Yu Philip S.2ORCID

Affiliation:

1. Jinan University, Guangzhou, China

2. University of Illinois at Chicago, Chicago, IL

Abstract

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.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province of China

Guangzhou Basic and Applied Basic Research Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

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