research-article

Efficient Mining of Outlying Sequence Patterns for Analyzing Outlierness of Sequence Data

Authors:

Zhifeng BaoAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 5

Article No.: 62, Pages 1 - 26

https://doi.org/10.1145/3399671

Published: 05 August 2020 Publication History

Abstract

Recently, a lot of research work has been proposed in different domains to detect outliers and analyze the outlierness of outliers for relational data. However, while sequence data is ubiquitous in real life, analyzing the outlierness for sequence data has not received enough attention. In this article, we study the problem of mining outlying sequence patterns in sequence data addressing the question: given a query sequence s in a sequence dataset D, the objective is to discover sequence patterns that will indicate the most unusualness (i.e., outlierness) of s compared against other sequences. Technically, we use the rank defined by the average probabilistic strength (aps) of a sequence pattern in a sequence to measure the outlierness of the sequence. Then a minimal sequence pattern where the query sequence is ranked the highest is defined as an outlying sequence pattern. To address the above problem, we present OSPMiner, a heuristic method that computes aps by incorporating several pruning techniques. Our empirical study using both real and synthetic data demonstrates that OSPMiner is effective and efficient.

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Index Terms

Efficient Mining of Outlying Sequence Patterns for Analyzing Outlierness of Sequence Data
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 14, Issue 5

Special Issue on KDD 2018, Regular Papers and Survey Paper

October 2020

376 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3407672

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

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Copyright © 2020 ACM.

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Publication History

Published: 05 August 2020

Accepted: 01 May 2020

Revised: 01 January 2020

Received: 01 May 2019

Published in TKDD Volume 14, Issue 5

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Cited By

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https://doi.org/10.1016/j.ins.2023.119791
Wu YMeng YLi YGuo LZhu XFournier-Viger PWu X(2023)COPP-Miner: Top-k Contrast Order-Preserving Pattern Mining for Time Series ClassificationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332174936:6(2372-2387)Online publication date: 19-Oct-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3321749
Li YZhang CLi JSong WQi ZWu YWu X(2023)MCoR-Miner: Maximal Co-Occurrence Nonoverlapping Sequential Rule MiningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.324121335:9(9531-9546)Online publication date: 1-Sep-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: 1-Nov-2023
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Wu YHu QLi YGuo LZhu XWu X(2023)OPP-Miner: Order-Preserving Sequential Pattern Mining for Time SeriesIEEE Transactions on Cybernetics10.1109/TCYB.2022.316932753:5(3288-3300)Online publication date: May-2023
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Wu YChen MLi YLiu JLi ZLi JWu X(2022)ONP-Miner: One-off Negative Sequential Pattern MiningACM Transactions on Knowledge Discovery from Data10.1145/3549940Online publication date: 4-Aug-2022
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