research-article

PSP-AMS: Progressive Mining of Sequential Patterns Across Multiple Streams

Authors:

Bijay Prasad Jaysawal,

Jen-Wei HuangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 13, Issue 1

Article No.: 5, Pages 1 - 23

https://doi.org/10.1145/3281632

Published: 19 December 2018 Publication History

Abstract

Sequential pattern mining is used to find frequent data sequences over time. When sequential patterns are generated, the newly arriving patterns may not be identified as frequent sequential patterns due to the existence of old data and sequences. Progressive sequential pattern mining aims to find the most up-to-date sequential patterns given that obsolete items will be deleted from the sequences. When sequences come with multiple data streams, it is difficult to maintain and update the current sequential patterns. Even worse, when we consider the sequences across multiple streams, previous methods cannot efficiently compute the frequent sequential patterns. In this work, we propose an efficient algorithm PSP-AMS to address this problem. PSP-AMS uses a novel data structure PSP-MS-tree to insert new items, update current items, and delete obsolete items. By maintaining a PSP-MS-tree, PSP-AMS efficiently finds the frequent sequential patterns across multiple streams. The experimental results show that PSP-AMS significantly outperforms previous algorithms for mining of progressive sequential patterns across multiple streams on synthetic data as well as real data.

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Cited By

Duong HTran A(2024)MFS-SubSC: an efficient algorithm for mining frequent sequences with sub-sequence constraintKnowledge and Information Systems10.1007/s10115-024-02148-w66:10(6151-6186)Online publication date: 11-Jun-2024
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Wang KLu JLiu AZhang G(2023)An Augmented Learning Approach for Multiple Data Streams Under Concept DriftAI 2023: Advances in Artificial Intelligence10.1007/978-981-99-8388-9_32(391-402)Online publication date: 28-Nov-2023
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Index Terms

PSP-AMS: Progressive Mining of Sequential Patterns Across Multiple Streams
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Data stream mining

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 13, Issue 1

February 2019

340 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3301280

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
University of Louisiana at Lafayette, USA

Issue’s Table of Contents

Copyright © 2018 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 December 2018

Accepted: 01 September 2018

Revised: 01 July 2018

Received: 01 February 2018

Published in TKDD Volume 13, Issue 1

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Cited By

Duong HTran A(2024)MFS-SubSC: an efficient algorithm for mining frequent sequences with sub-sequence constraintKnowledge and Information Systems10.1007/s10115-024-02148-w66:10(6151-6186)Online publication date: 11-Jun-2024
https://doi.org/10.1007/s10115-024-02148-w
Wang KLu JLiu AZhang G(2023)An Augmented Learning Approach for Multiple Data Streams Under Concept DriftAI 2023: Advances in Artificial Intelligence10.1007/978-981-99-8388-9_32(391-402)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1007/978-981-99-8388-9_32
Zhang BXie J(2022)Design and Implementation of an Online Learning Behavior Evaluation System Based on Data MiningAdvances in Multimedia10.1155/2022/42599132022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4259913
Huang GGan WHuang SChen J(2022)Negative pattern discovery with individual supportKnowledge-Based Systems10.1016/j.knosys.2022.109194251(109194)Online publication date: Sep-2022
https://doi.org/10.1016/j.knosys.2022.109194
Song WYe WFournier-Viger P(2022)Mining sequential patterns with flexible constraints from MOOC dataApplied Intelligence10.1007/s10489-021-03122-752:14(16458-16474)Online publication date: 23-Mar-2022
https://doi.org/10.1007/s10489-021-03122-7
Guyet TZhang WBifet A(2022)Incremental Mining of Frequent Serial Episodes Considering Multiple OccurrencesComputational Science – ICCS 202210.1007/978-3-031-08751-6_33(460-472)Online publication date: 21-Jun-2022
https://dl.acm.org/doi/10.1007/978-3-031-08751-6_33
Cheng YCheng MPang TLiu S(2021)Using Clustering Analysis and Association Rule Technology in Cross-MarketingComplexity10.1155/2021/99798742021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/9979874
Wu YLuo LLi YGuo LFournier-Viger PZhu XWu X(2021)NTP-Miner: Nonoverlapping Three-Way Sequential Pattern MiningACM Transactions on Knowledge Discovery from Data10.1145/348024516:3(1-21)Online publication date: 22-Oct-2021
https://dl.acm.org/doi/10.1145/3480245
Li L(2021)An Big Data Analysis Approach Based on Frequent Change Structure Mining2021 6th International Conference on Smart Grid and Electrical Automation (ICSGEA)10.1109/ICSGEA53208.2021.00109(455-459)Online publication date: May-2021
https://doi.org/10.1109/ICSGEA53208.2021.00109

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