research-article

OWSP-Miner: Self-adaptive One-off Weak-gap Strong Pattern Mining

Authors:

Xindong WuAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 13, Issue 3

Article No.: 25, Pages 1 - 23

https://doi.org/10.1145/3476247

Published: 04 February 2022 Publication History

Abstract

Gap constraint sequential pattern mining (SPM), as a kind of repetitive SPM, can avoid mining too many useless patterns. However, this method is difficult for users to set a suitable gap without prior knowledge and each character is considered to have the same effects. To tackle these issues, this article addresses a self-adaptive One-off Weak-gap Strong Pattern (OWSP) mining, which has three characteristics. First, it determines the gap constraint adaptively according to the sequence. Second, all characters are divided into two groups: strong and weak characters, and the pattern is composed of strong characters, while weak characters are allowed in the gaps. Third, each character can be used at most once in the process of support (the frequency of pattern) calculation. To handle this problem, this article presents OWSP-Miner, which equips with two key steps: support calculation and candidate pattern generation. A reverse-order filling strategy is employed to calculate the support of a candidate pattern, which reduces the time complexity. OWSP-Miner generates candidate patterns using pattern join strategy, which effectively reduces the candidate patterns. For clarification, time series is employed in the experiments and the results show that OWSP-Miner is not only more efficient but also is easier to mine valuable patterns. In the experiment of stock application, we also employ OWSP-Miner to mine OWSPs and the results show that OWSPs mining is more meaningful in real life. The algorithms and data can be downloaded at https://github.com/wuc567/Pattern-Mining/tree/master/OWSP-Miner.

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Ouyang WDong RTao RLiu XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate PredictionProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657941(2416-2420)Online publication date: 10-Jul-2024
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Index Terms

OWSP-Miner: Self-adaptive One-off Weak-gap Strong Pattern Mining
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 13, Issue 3

September 2022

312 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3512349

Editor:
Daniel Zeng
Chinese Academy of Sciences, China

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Publication History

Published: 04 February 2022

Accepted: 01 July 2021

Revised: 01 May 2021

Received: 01 November 2020

Published in TMIS Volume 13, Issue 3

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National Natural Science Foundation of China
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Cited By

Ouyang WDong RTao RLiu XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate PredictionProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657941(2416-2420)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657941
Li YMa CGao RWu YLi JWang WWu X(2024)OPF-Miner: Order-Preserving Pattern Mining With Forgetting Mechanism for Time SeriesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.343827436:12(8981-8995)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3438274
He GDai LYu ZChen C(2024)GAN-Based Temporal Association Rule Mining on Multivariate Time Series DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.333504936:10(5168-5180)Online publication date: Oct-2024
https://doi.org/10.1109/TKDE.2023.3335049
Geng MWu YLi YLiu JFournier-Viger PZhu XWu X(2024)RNP-Miner: Repetitive Nonoverlapping Sequential Pattern MiningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.333430036:9(4874-4889)Online publication date: Sep-2024
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Sun CRen XDong XQiu PWu XZhao LGuo YGong YZhang C(2024)Mining actionable repetitive positive and negative sequential patternsKnowledge-Based Systems10.1016/j.knosys.2024.112398(112398)Online publication date: Aug-2024
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Sun CGong YGuo YZhao LGuan HLiu XDong X(2024)SN-RNSPKnowledge-Based Systems10.1016/j.knosys.2024.111449287:COnline publication date: 16-May-2024
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Wu YChen MLi YLiu JLi ZLi JWu X(2023)ONP-Miner: One-off Negative Sequential Pattern MiningACM Transactions on Knowledge Discovery from Data10.1145/354994017:3(1-24)Online publication date: 22-Feb-2023
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Wu YZhao XLi YGuo LZhu XFournier-Viger PWu X(2023)OPR-Miner: Order-Preserving Rule Mining for Time SeriesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322496335:11(11722-11735)Online publication date: 6-Jan-2023
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