research-article

RuleGrowth: mining sequential rules common to several sequences by pattern-growth

Authors:

Philippe Fournier-Viger,

Vincent Shin-Mu TsengAuthors Info & Claims

SAC '11: Proceedings of the 2011 ACM Symposium on Applied Computing

Pages 956 - 961

https://doi.org/10.1145/1982185.1982394

Published: 21 March 2011 Publication History

Abstract

Mining sequential rules from large databases is an important topic in data mining fields with wide applications. Most of the relevant studies focused on finding sequential rules appearing in a single sequence of events and the mining task dealing with multiple sequences were far less explored. In this paper, we present RuleGrowth, a novel algorithm for mining sequential rules common to several sequences. Unlike other algorithms, RuleGrowth uses a pattern-growth approach for discovering sequential rules such that it can be much more efficient and scalable. We present a comparison of RuleGrowth's performance with current algorithms for three public datasets. The experimental results show that RuleGrowth clearly outperforms current algorithms for all three datasets under low support and confidence threshold and has a much better scalability.

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Cited By

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
Chen LGan WChen C(2024)Toward Correlated Sequential RulesIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.34293065:10(5340-5351)Online publication date: Oct-2024
https://doi.org/10.1109/TAI.2024.3429306
Csalódi RBagyura ZAbonyi J(2024)Time-dependent sequential association rule-based survival analysis: A healthcare applicationMethodsX10.1016/j.mex.2023.10253512(102535)Online publication date: Jun-2024
https://doi.org/10.1016/j.mex.2023.102535
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Published In

cover image ACM Conferences

SAC '11: Proceedings of the 2011 ACM Symposium on Applied Computing

March 2011

1868 pages

ISBN:9781450301138

DOI:10.1145/1982185

Conference Chairs:
William Chu
Tunghai University, TaiChung, Taiwan
,
W. Eric Wong
University of Texas at Dallas, Richardson, Texas
,
Program Chairs:
Mathew J. Palakal
Indiana University Purdue University, Indianapolis
,
Chih-Cheng Hung
Southern Polytechnic State University, Marietta

Copyright © 2011 ACM.

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: 21 March 2011

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SAC'11: The 2011 ACM Symposium on Applied Computing

March 21 - 24, 2011

TaiChung, Taiwan

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

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
Chen LGan WChen C(2024)Toward Correlated Sequential RulesIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.34293065:10(5340-5351)Online publication date: Oct-2024
https://doi.org/10.1109/TAI.2024.3429306
Csalódi RBagyura ZAbonyi J(2024)Time-dependent sequential association rule-based survival analysis: A healthcare applicationMethodsX10.1016/j.mex.2023.10253512(102535)Online publication date: Jun-2024
https://doi.org/10.1016/j.mex.2023.102535
Rahman SAhmed SFariha TMohammad AHaque MChellappan SNoor J(2024)Unsupervised machine learning approach for tailoring educational content to individual student weaknessesHigh-Confidence Computing10.1016/j.hcc.2024.1002284:4(100228)Online publication date: Dec-2024
https://doi.org/10.1016/j.hcc.2024.100228
Drogkoula MKokkinos KSamaras N(2023)A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources ManagementApplied Sciences10.3390/app13221214713:22(12147)Online publication date: 8-Nov-2023
https://doi.org/10.3390/app132212147
Meng WQu RSilva SPaquete L(2023)Sequential Rule Mining for Automated Design of Meta-heuristicsProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596303(1727-1735)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596303
Oubelmouh YFargon FDe Runz CSoulet AVeillon C(2023)Identifying Survival-Changing Sequential Patterns for Employee Attrition Analysis2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302498(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/DSAA60987.2023.10302498
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
Da NHanh TThanh H(2022)Predicting Buying Behavior using CPT+: A Case Study of an E-commerce CompanyRecent Advances in Computer Science and Communications10.2174/266625581466620123011514815:8(1096-1102)Online publication date: Oct-2022
https://doi.org/10.2174/2666255814666201230115148
Yin ZGan WHuang GWu YFournier-Viger P(2022)Constraint-based Sequential Rule Mining2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA54385.2022.10032452(1-10)Online publication date: 13-Oct-2022
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