research-article

US-Rule: Discovering Utility-driven Sequential Rules

Authors:

Philip S. YuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 1

Article No.: 10, Pages 1 - 22

https://doi.org/10.1145/3532613

Published: 20 February 2023 Publication History

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.

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Cited By

Wu YWang ZLi YGuo YJiang HZhu XWu X(2024)Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time SeriesACM Transactions on Management Information Systems10.1145/365845015:2(1-27)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3658450
Zhang CLyu MGan WYu P(2024)Totally-ordered Sequential Rules for Utility MaximizationACM Transactions on Knowledge Discovery from Data10.1145/362845018:4(1-23)Online publication date: 12-Feb-2024
https://dl.acm.org/doi/10.1145/3628450
Huang SGan WMiao J(2023)Targeted Querying of Closed High-Utility Itemsets2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386927(5967-5976)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386927
Show More Cited By

Index Terms

US-Rule: Discovering Utility-driven Sequential Rules
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 1

January 2023

375 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3572846

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 February 2023

Online AM: 09 August 2022

Accepted: 16 April 2022

Received: 19 November 2021

Published in TKDD Volume 17, Issue 1

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National Natural Science Foundation of China
Natural Science Foundation of Guangdong Province of China
Guangzhou Basic and Applied Basic Research Foundation

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Cited By

Wu YWang ZLi YGuo YJiang HZhu XWu X(2024)Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time SeriesACM Transactions on Management Information Systems10.1145/365845015:2(1-27)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3658450
Zhang CLyu MGan WYu P(2024)Totally-ordered Sequential Rules for Utility MaximizationACM Transactions on Knowledge Discovery from Data10.1145/362845018:4(1-23)Online publication date: 12-Feb-2024
https://dl.acm.org/doi/10.1145/3628450
Huang SGan WMiao J(2023)Targeted Querying of Closed High-Utility Itemsets2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386927(5967-5976)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386927
Lin HGan WHuang GYu P(2023)USER: Towards High-Utility Sequential Rules with Repetitive Items2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386473(5977-5986)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386473

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