research-article

ONP-Miner: One-off Negative Sequential Pattern Mining

Authors:

Xindong WuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 3

Article No.: 37, Pages 1 - 24

https://doi.org/10.1145/3549940

Published: 22 February 2023 Publication History

Abstract

Negative sequential pattern mining (SPM) is an important SPM research topic. Unlike positive SPM, negative SPM can discover events that should have occurred but have not occurred, and it can be used for financial risk management and fraud detection. However, existing methods generally ignore the repetitions of the pattern and do not consider gap constraints, which can lead to mining results containing a large number of patterns that users are not interested in. To solve this problem, this article discovers frequent one-off negative sequential patterns (ONPs). This problem has the following two characteristics. First, the support is calculated under the one-off condition, which means that any character in the sequence can only be used once at most. Second, the gap constraint can be given by the user. To efficiently mine patterns, this article proposes the ONP-Miner algorithm, which employs depth-first and backtracking strategies to calculate the support. Therefore, ONP-Miner can effectively avoid creating redundant nodes and parent-child relationships. Moreover, to effectively reduce the number of candidate patterns, ONP-Miner uses pattern join and pruning strategies to generate and further prune the candidate patterns, respectively. Experimental results show that ONP-Miner not only improves the mining efficiency but also has better mining performance than the state-of-the-art algorithms. More importantly, ONP mining can find more interesting patterns in traffic volume data to predict future traffic.

References

[1]

Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, and Philip S. Yu. 2019. A survey of parallel sequential pattern mining. ACM Transactions on Knowledge Discovery from Data 13, 3 (2019), 1–34.

[2]

Philippe Fournier-Viger, Jerry Chun-Wei Lin, Rage Uday Kiran, Yun Sing Koh, and Rincy Thomas. 2017. A survey of sequential pattern mining. Data Science and Pattern Recognition 1, 1 (2017), 54–77.

[3]

Xindong Wu, Xingquan Zhu, and Minghui Wu. 2022. The evolution of search: Three computing paradigms. ACM Transactions on Management Information Systems 13, 2 (2022), 20.

[4]

Pengfei Zhang, Tianrui Li, Zhong Yuan, Chuan Luo, Guoqiang Wang, Jia Liu, and Shengdong Du. 2022. A data-level fusion model for unsupervised attribute selection in multi-source homogeneous data. Information Fusion 80 (2022), 87–103.

[5]

Lizhen Wang, Yuan Fang, and Lihua Zhou. 2022. Preference-based Spatial Co-location Pattern Mining. Springer. DOI:

[6]

Youxi Wu, Yao Tong, Xingquan Zhu, and Xindong Wu. 2018. NOSEP: Nonoverlapping sequence pattern mining with gap constraints. IEEE Transactions on Cybernetics 48, 10 (2018), 2809–2822.

[7]

Youxi Wu, Meng Geng, Yan Li, Lei Guo, Zhao Li, Philippe Fournier-Viger, Xingquan Zhu, and Xindong Wu. 2021. HANP-Miner: High average utility nonoverlapping sequential pattern mining. Knowledge-Based Systems 229 (2021), 107361.

[8]

Tin Truong, Hai Duong, Bac Le, Philippe Fournier-Viger, Unil Yun, and Hamido Fujita. 2021. Efficient algorithms for mining frequent high utility sequences with constraints. Information Sciences 568 (2021), 239–264.

[9]

Wei Song, Lu Liu, and Chaomin Huang. 2021. Generalized maximal utility for mining high average-utility itemsets. Knowledge and Information Systems 63 (2021), 2947–2967.

[10]

Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, and Philip S. Yu. 2021. Beyond frequency: Utility mining with varied item-specific minimum utility. ACM Transactions on Internet Technology 21, 1 (2021), 3.

[11]

Lizhen Wang, Xuguang Bao, and Lihua Zhou. 2018. Redundancy reduction for prevalent co-location patterns. IEEE Transactions on Knowledge and Data Engineering 30, 1 (2018), 142–155.

[12]

Youxi Wu, Yuehua Wang, Yan Li, Xingquan Zhu, and Xindong Wu. 2021. Top-k self-adaptive contrast sequential pattern mining. IEEE Transactions on Cybernetics. DOI:

[13]

Ronghui Wu, Qing Li, and Xiangtao Chen. 2019. Mining contrast sequential pattern based on subsequence time distribution variation with discreteness constraints. Applied Intelligence 49, 12 (2019), 4348–4360.

[14]

Qingzhe Li, Liang Zhao, Yi-Ching Lee, and Jessica Lin. 2020. Contrast pattern mining in paired multivariate time series of a controlled driving behavior experiment. ACM Transactions on Spatial Algorithms and Systems 6, 4 (2020), 1–28.

[15]

David Savage, Xiuzhen Zhang, Pauline Chou, Xinghuo Yu, and Qingmai Wang. 2017. Distributed mining of contrast patterns. IEEE Transactions on Parallel and Distributed Systems 28, 7 (2017), 1881–1890.

[16]

Youxi Wu, Qian Hu, Yan Li, Lei Guo, Xingquan Zhu, and Xindong Wu. 2022. OPP-Miner: Order-preserving sequential pattern mining for time series. IEEE Transactions on Cybernetics. DOI:

[17]

Jerry Chun-Wei Lin, Youcef Djenouri, and Gautam Srivastava. 2021. Efficient closed high-utility pattern fusion model in large-scale databases. Information Fusion 76 (2021), 122–132.

[18]

Bac Le, Hai Duong, Tin Truong, and Philippe Fournier-Viger. 2017. FGenSM: Two efficient algorithms for mining frequent closed and generator sequences using the local pruning strategy. Knowledge and Information Systems 53 (2017), 71–107.

[19]

Youxi Wu, Changrui Zhu, Yan Li, Lei Guo, and Xindong Wu. 2020. NetNCSP: Nonoverlapping closed sequential pattern mining. Knowledge-based Systems 196 (2020), 105812.

[20]

Zengyou He, Simeng Zhang, and Jun Wu. 2019. Significance-based discriminative sequential pattern mining. Expert Systems with Applications 122 (2019), 54–64.

[21]

Tingting Wang, Lei Duan, Guozhu Dong, and Zhifeng Bao. 2020. Efficient mining of outlying sequence patterns for analyzing outlierness of sequence data. ACM Transactions on Knowledge Discovery from Data 14, 5 (2020), 1–26.

[22]

Wei Wang and Longbing Cao. 2021. VM-NSP: Vertical negative sequential pattern mining with loose negative element constraints. ACM Transactions on Information Systems 39, 2 (2021), 1–27.

[23]

Wei Wang and Longbing Cao. 2019. Negative sequence analysis: A review. ACM Computing Surveys 52, 2 (2019), 32.

[24]

Tiantian Xu, Tongxuan Li, and Xiangjun Dong. 2018. Efficient high utility negative sequential patterns mining in smart campus. IEEE Access 6, 1 (2018) 23839–23847.

[25]

Tiantian Xu, Xiangjun Dong, Jianliang Xu, and Yongshun Gong. 2017. E-msNSP: Efficient negative sequential patterns mining based on multiple minimum supports. International Journal of Pattern Recognition and Artificial Intelligence 31, 2 (2017), 1–17.

[26]

Jen-Wei Huang, Yongbin Wu, and Bijay Prasad Jaysawal. 2020. On mining progressive positive and negative sequential patterns simultaneously. Journal of Information Science and Engineering 36, 1 (2020), 145–169.

[27]

Philippe Fournier-Viger, Wensheng Gan, Youxi Wu, Mourad Nouioua, Wei Song, Tin Truong, and Hai Duong. 2022. Pattern mining: Current challenges and opportunities. In Proceedings of the 1st Workshop on Pattern Mining and Machine Learning in Big Complex Databases. 1–16.

[28]

Chunkai Zhang, Zilin Du, Wensheng Gan, and Philip S. Yu. 2021. TKUS: Mining top-k high utility sequential patterns. Information Sciences 570 (2021), 342–359.

[29]

Jen-Wei Huang, Bijay Prasad Jaysawal, Kuanying Chen, and Yongbin Wu. 2019. Mining frequent and top-k high utility time interval-based events with duration patterns. Knowledge and Information Systems 61, 3 (2019), 1331–1359.

[30]

Fabio Fumarola, Pasqua Fabiana Lanotte, Michelangelo Ceci, and Donato Malerba. 2016. CloFAST: Closed sequential pattern mining using sparse and vertical id-lists. Knowledge and Information Systems 48, 2 (2016), 429–463.

[31]

Md. Rezaul Karim, Michael Cochez, Oya Deniz Beyan, Chowdhury Farhan Ahmed, and Stefan Decker. 2018. Mining maximal frequent patterns in transactional databases and dynamic data streams: A spark-based approach. Information Sciences 432 (2018), 278–300.

[32]

Yan Li, Shuai Zhang, Lei Guo, Jing Liu, Youxi Wu, and Xindong Wu. 2022. NetNMSP: Nonoverlapping maximal sequential pattern mining. Applied Intelligence 52, 9 (2022), 9861–9884.

[33]

Fan Min, Zhiheng Zhang, Wenjie Zhai, and Rongping Shen. 2020. Frequent pattern discovery with tri-partition alphabets. Information Sciences 507 (2020), 715–732.

[34]

Youxi Wu, Lanfang Luo, Yan Li, Lei Guo, Philippe Fournier-Viger, Xingquan Zhu, and Xindong Wu. 2022. NTP-Miner: Nonoverlapping three-way sequential pattern mining. ACM Transactions on Knowledge Discovery from Data 16, 3 (2022), 1–21.

[35]

Youxi Wu, Xiaohui Wang, Yan Li, Lei Guo, Zhao Li, Ji Zhang, and Xindong Wu. 2022. OWSP-Miner: Self-adaptive one-off weak-gap strong pattern mining. ACM Transactions on Management Information Systems 13, 3 (2022), 25.

[36]

Youxi Wu, Zhu Yuan, Yan Li, Lei Guo, Philippe Fournier-Viger, and Xindong Wu. 2022. NWP-Miner: Nonoverlapping weak-gap sequential pattern mining. Information Sciences 588 (2022), 124–141.

[37]

Yoonji Baek, Unil Yun, Heonho Kim, Jongseong Kim, Bay Vo, Tin C. Truong, and Zhihong Deng. 2021. Approximate high utility itemset mining in noisy environments. Knowledge-based Systems 212 (2021), 106596.

[38]

Yoonji Baek, Unil Yun, Heonho Kim, Hyoju Nam, Hyunsoo Kim, Jerry Chun-Wei Lin, Bay Vo, and Witold Pedrycz. 2021. RHUPS: Mining recent high utility patterns with sliding window-based arrival time control over data streams. ACM Transactions on Intelligent Systems and Technology 12, 2 (2021), 1–27.

[39]

Youxi Wu, Rong Lei, Yan Li, Lei Guo, and Xindong Wu. 2021. HAOP-Miner: Self-adaptive high-average utility one-off sequential pattern mining. Expert Systems With Applications 184 (2021), 115449.

[40]

Unil Yun, Hyoju Nam, Jongseong Kim, Heonho Kim, Yoonji Baek, Judae Lee, Eunchul Yoon, Tin C. Truong, Bay Vo, and Witold Pedrycz. 2020. Efficient transaction deleting approach of pre-large based high utility pattern mining in dynamic databases. Future Generation Computer Systems 103 (2020), 58–78.

[41]

Jinane Harmouche and Sriram Narasimhan. 2020. Long-term monitoring for leaks in water distribution networks using association rules mining. IEEE Transactions on Industrial Informatics 16, 1 (2020), 258–266.

[42]

Sumalatha Saleti and R. B. V. Subramanyam. 2020. Distributed mining of high utility time interval sequential patterns using mapreduce approach. Expert Systems with Applications 141 (2020), 112967.

[43]

Youxi Wu, Lingling Wang, Jiadong Ren, Wei Ding, and Xindong Wu. 2014. Mining sequential patterns with periodic wildcard gaps. Applied Intelligence 41, 1 (2014), 99–116.

[44]

Yuehua Wang, Youxi Wu, Yan Li, Fang Yao, Philippe Fournier-Viger, and Xindong Wu. 2022. Self-adaptive nonoverlapping sequential pattern mining. Applied Intelligence 52, 6 (2022), 6646–6661.

[45]

Fei Xie, Xindong Wu, and Xingquan Zhu. 2017. Efficient sequential pattern mining with wildcards for keyphrase extraction. Knowledge-based Systems 115 (2017), 27–39.

[46]

Yu-Hao Ke, Jen-Wei Huang, Wei-Chen Lin, and Bijay Prasad Jaysawal. 2020. Finding possible promoter binding sites in DNA sequences by sequential patterns mining with specific numbers of gaps. IEEE/ACM Transactions on Computational Biology and Bioinformatics 18, 6 (2020), 2459–2470.

[47]

Gengsen Huang, Wensheng Gan, Shan Huang, and Jiahui Chen. 2022. Negative pattern discovery with individual support. Knowledge-based Systems 251 (2022), 109194.

[48]

Xiangjun Dong, Ping Qiu, Jinhu Lv, Longbing Cao, and Tiantian Xu. 2019. Mining top-k useful negative sequential patterns via learning. IEEE Transactions on Neural Networks and Learning Systems 30, 9 (2019), 2764–2778.

[49]

Xinming Gao, Yongshun Gong, Tiantian Xu, Jinhu Lu, Yuanhai Zhao, and Xiangjun Dong. 2020. Towards to better structure and looser constraint to mine negative sequential patterns. IEEE Transactions on Neural Networks and Learning Systems. DOI:

[50]

Ping Qiu, Yongshun Gong, Yuhai Zhao, Longbing Cao, Chengqi Zhang, and Xiangjun Dong. 2021. An efficient method for modeling nonoccurring behaviors by negative sequential patterns with loose constraints. IEEE Transactions on Neural Networks and Learning Systems. DOI:

[51]

Longbing Cao, Xiangjun Dong, and Zhigang Zheng. 2016. e-NSP: Efficient negative sequential pattern mining. Artificial Intelligence 235 (2016), 156–182.

[52]

Xiangjun Dong, Yongshun Gong, and Longbing Cao. 2020. e-RNSP: An efficient method for mining repetition negative sequential patterns. IEEE Transactions on Cybernetics 50, 5 (2020), 2084–2096.

[53]

Yan Li, Lei Yu, Jing Liu, Lei Guo, Youxi Wu, and Xindong Wu. 2021. NetDPO: (delta, gamma)-approximate pattern matching with gap constraints under one-off condition. Applied Intelligence. DOI:

Digital Library

[54]

Youxi Wu, Cong Shen, He Jiang, and Xindong Wu. 2017. Strict pattern matching under non-overlapping condition. Science China Information Sciences 60, 1 (2017), 012101.

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
Wu YMeng YLi YGuo LZhu XFournier-Viger PWu X(2024)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: Jul-2024
https://doi.org/10.1109/TKDE.2023.3321749
Csalódi RBagyura ZVathy-Fogarassy ÁAbonyi J(2024)Time-dependent frequent sequence mining-based survival analysisKnowledge-Based Systems10.1016/j.knosys.2024.111885296(111885)Online publication date: Jul-2024
https://doi.org/10.1016/j.knosys.2024.111885
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Index Terms

ONP-Miner: One-off Negative Sequential Pattern Mining
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Theory of computation
  1. Design and analysis of algorithms

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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 ...
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Abstract
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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 3

April 2023

379 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3583064

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

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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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Publication History

Published: 22 February 2023

Online AM: 04 August 2022

Accepted: 13 July 2022

Revised: 08 June 2022

Received: 30 December 2021

Published in TKDD Volume 17, Issue 3

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National Natural Science Foundation of China
National Key Research and Development Program of China
Natural Science Foundation of Hebei Province, China

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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
Wu YMeng YLi YGuo LZhu XFournier-Viger PWu X(2024)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: Jul-2024
https://doi.org/10.1109/TKDE.2023.3321749
Csalódi RBagyura ZVathy-Fogarassy ÁAbonyi J(2024)Time-dependent frequent sequence mining-based survival analysisKnowledge-Based Systems10.1016/j.knosys.2024.111885296(111885)Online publication date: Jul-2024
https://doi.org/10.1016/j.knosys.2024.111885
Sun CGong YGuo YZhao LGuan HLiu XDong X(2024)SN-RNSPKnowledge-Based Systems10.1016/j.knosys.2024.111449287:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111449
Bechini ABondielli ADell'Oglio PMarcelloni F(2023)From basic approaches to novel challenges and applications in Sequential Pattern MiningElectronic Research Archive10.3934/aci.20230043:1(44-78)Online publication date: 2023
https://doi.org/10.3934/aci.2023004
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
https://dl.acm.org/doi/10.1109/TKDE.2023.3241213
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
https://dl.acm.org/doi/10.1109/TKDE.2022.3224963
Li YLiu JGuo YLiu JWu Y(2022)AOP-Miner: Approximate Order-Preserving Pattern Mining for Time Series2022 IEEE International Conference on Knowledge Graph (ICKG)10.1109/ICKG55886.2022.00026(149-156)Online publication date: Dec-2022
https://doi.org/10.1109/ICKG55886.2022.00026

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