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TK-RNSP: Efficient Top-K Repetitive negative sequential pattern mining

INFORMATION PROCESSING & MANAGEMENT 2025年第3期62卷

作者： Lan, Dun Sun, Chuanhou Dong, Xiangjun Qiu, Ping Gong, Yongshun Liu, Xinwang Fournier-Viger, Philippe Zhang, Chengqi Qilu Univ Technol Shandong Comp Ctr Key Lab Comp Power Network & Informat Secur Natl Supercomp Ctr JinanShandong Acad SciMinist Jinan 250353 Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Comp Power Internet & Serv C Jinan 250101 Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210023 Peoples R China Shandong Univ Sch Software Jinan 250100 Peoples R China Natl Univ Def Technol Sch Comp Changsha 410003 Hunan Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Hong Kong Polytech Univ Dept Data Sci & Artificial Intelligence Hong Kong 100872 Peoples R China

Repetitive negative sequential patterns (RNSPs) can provide critical insights into the importance of sequences. However, most current RNSP mining methods require users to set an appropriate support threshold to obtain the expected number of patterns, which is a very difficult task for the users without prior experience. To address this issue, we propose a new algorithm, TK-RNSP, to mine the Top-K RNSPs with the highest support, without the need to set a support threshold. In detail, we achieve a significant breakthrough by proposing a series of definitions that enable RNSP mining to satisfy anti-monotonicity. Then, we propose a bitmapbased Depth-First Backtracking Search (DFBS) strategy to decrease the heavy computational burden by increasing the speed of support calculation. Finally, we propose the algorithm TKRNSP in an one-stage process, which can effectively reduce the generation of unnecessary patterns and improve computational efficiency comparing to those two-stage process algorithms. To the best of our knowledge, TK-RNSP is the first algorithm to mine Top-K RNSPs. Extensive experiments on eight datasets show that TK-RNSP has better flexibility and efficiency to mine Top-K RNSPs.

关键词： sequential pattern mining negative sequential pattern Top-K repetitive negative sequential patterns Nonoverlapping

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ONP-Miner: One-off negative sequential pattern Mining

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2022年第3期17卷 1-24页

作者： Wu, Youxi Chen, Mingjie Li, Yan Liu, Jing Li, Zhao Li, Jinyan Wu, Xindong Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Hebei Key Lab Big Data Comp Tianjin 300401 Peoples R China Hebei Univ Technol Sch Econ & Management Tianjin 300401 Peoples R China Zhejiang Univ Alibaba ZJU Joint Res Inst Frontier Technol Hangzhou 310000 Peoples R China Univ Technol Sydney Data Sci Inst Sydney NSW Australia Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ China Hefei 230009 Peoples R China

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 stateof-the-art algorithms. More importantly, ONP mining can find more interesting patterns in traffic volume data to predict future traffic.

关键词： sequential pattern mining negative sequential pattern one-off condition gap constraint

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Comparisons of Typical Algorithms in negative sequential pattern Mining

Comparisons of Typical Algorithms in Negative Sequential Pat...

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IEEE Workshop on Electronics, Computer and Applications (IWECA)

作者： Dong, Xiangjun Gong, Yangshun Zhao, Lulin Qilu Univ Technol Sch Informat Jinan 250353 Peoples R China

ISBN: (纸本)9781479945658

negative sequential pattern (NSP), which contains both non-occurring and occurring items, can play more important roles than positive sequential pattern (PSP) in many applications. NSP mining, however, has been just caught attention and very limited methods are available to mine NSP. Furthermore, there is not a unified definition about negative containment, i.e., how a data sequence contains a negative sequence. So in this paper, we select three typical existing methods, PNSP, Neg-GSP and e-NSP, and compare their definitions, runtime and the number of NSPs. Examples and experiments on same dataset clearly show their differences.

关键词： positive sequential pattern negative sequential pattern negative containment

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Research on typical algorithms in negative sequential pattern mining

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Open Automation and Control Systems Journal 2015年第1期7卷 934-941页

作者： Gong, Yongshun Liu, Chuanlu Dong, Xiangjun School of Information Qilu University of Technology JinanShandong250353 China

negative sequential pattern (NSP), which contains both non-occurring and occurring items, can discover much more interest roles than positive sequential pattern (PSP) in many applications. NSP mining, however, has been just caught attention and very limited methods are available to mine NSP. Furthermore, there is not a unified definition about negative containment, i.e., how a data sequence contains a negative sequence. The researchers who begin to study negative sequential pattern are often confused by these different definitions and are eager to know the differences of the existing methods. So in this paper, we select four typical existing methods, PNSP, Neg-GSP, e-NSP and NSPM, implement their algorithms by JAVA, and compare their definition, method, runtime and the number of NSPs. Examples and experiments on four dataset clearly show their differences. © Bu et al.

关键词： negative containment negative sequential pattern Positive sequential pattern

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Mining Repetitive negative sequential patterns with Gap Constraints

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ACM Transactions on Knowledge Discovery from Data 2025年第4期19卷

作者： Li, Yan Wang, Zhulin Liu, Jing Guo, Lei Fournier-Viger, Philippe Wu, Youxi Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin China School of Artificial Intelligence Hebei University of Technology Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Hebei Key Laboratory of Big Data Computing Tianjin China Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China

sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find the frequent missing items in the PSPGs. To tackle this issue, this article explores negative sequential patterns with gap constraints (NSPGs). We propose an efficient NSPG-Miner algorithm that can mine both frequent PSPGs and NSPGs simultaneously. To effectively reduce candidate patterns, we propose a pattern join strategy with negative patterns which can generate both positive and negative candidate patterns at the same time. To calculate the support (frequency of occurrence) of a pattern in each sequence, we explore a NegPair algorithm that employs a key-value pair array structure to deal with the gap constraints and the negative items simultaneously and can avoid redundant rescanning of the original sequence, thus improving the efficiency of the algorithm. To report the performance of NSPG-Miner, 11 competitive algorithms and 11 datasets are employed. The experimental results not only validate the effectiveness of the strategies adopted by NSPG-Miner but also verify that NSPG-Miner can discover more valuable information than the state-of-the-art algorithms. Copyright © 2025 held by the owner/author(s). Publication rights licensed to ACM.

关键词： gap constraint key-value pair negative sequential pattern sequential pattern mining

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Hybridized optimization oriented fast negative sequential patterns mining

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第4期81卷 5279-5303页

作者： Yadav, Poonam DAV Coll Engn & Technol Kanina 123027 Haryana India

Recently, negative sequential patterns (NSP) (like missing medical treatments) mining is important in data mining research since it includes negative correlations between item sets, which are overlooked by positive sequential pattern mining (PSP) (for instance, utilization of medical service). Yet, discovering the NSP is very complex than finding PSP because of the important problem complexity occurred by high computational cost, non-occurring elements, as well as huge search space in evaluating NSC, and most of the NSP based existing works are inefficient. Therefore, this paper intends to propose a fast NSP mining algorithm for the disease prediction model. This model includes Data normalization, Data separation based on labels, and pattern recognition phases. In the midst of data separation, the maximum occurring data is optimally selected using a new algorithm that hybridizes the FireFly (FF) algorithm and Grey Wolf Optimization (GWO). This proposed Firefly induced Grey Wolf optimization (F-GWO) algorithm automatically selects the maximum occurring information as per the PSP support. The proposed model is compared over other conventional methods with varied measures. Especially, the computation cost of our model is 46.87%, 6.27%, 9.37%, 2.76%, and 66.62% better than the existing GA, ABC, PSO, FF, and GWO models respectively.

关键词： Positive sequential pattern negative sequential pattern Optimization algorithm GWO FF

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On Mining Progressive Positive and negative sequential patterns Simultaneously

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2020年第1期36卷 145-169页

作者： Huang, Jen-Wei Wu, Yong-Bin Jaysawal, Bijay Prasad Natl Cheng Kung Univ Inst Comp & Commun Engn Tainan 701 Taiwan

Positive sequential pattern (PSP) mining focuses on appearing items, while negative sequential pattern (NSP) mining tends to find the relationship between occurring and nonoccurring items. There are few works involved in NSP mining, and the definitions of NSP are inconsistent in each work. The support threshold for PSP is always applied on NSP, which cannot bring out interesting patterns. In addition, PSP has been discovered on incremental databases and progressive databases, while NSP mining is only performed on static databases. Progressive sequential pattern mining finds the most up-to-date patterns, which can provide more valuable information. However, the previous progressive sequential pattern mining algorithm contains some redundant process. In this paper, we aim to find NSP on progressive databases. A new definition of NSP is given to discover more meaningful and interesting patterns. We propose an algorithm, Propone, for efficient mining process. We also propose a level-order traversal strategy and a pruning strategy to reduce the calculation time and the number of negative sequential candidates (NSC). By comparing Propone with some modified previous algorithms, the experimental results show that Propone outperforms comparative algorithms.

关键词： progressive mining negative sequential pattern frequency ratio of interest sequential pattern mining data mining

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Mining negative sequential Rules from negative sequential patterns 27th

Mining Negative Sequential Rules from Negative Sequential Pa...

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27th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Sun, Chuanhou Jiang, Xiaoqi Dong, Xiangjun Xu, Tiantian Zhao, Long Li, Zhao Zhao, Yuhai Qilu Univ Technol Shandong Acad Sci Dept Comp Sci & Technol Jinan Peoples R China Northeastern Univ Shenyang Peoples R China

ISBN: (纸本)9783031001239;9783031001222

As an important tool for behavior informatics, negative sequential rules (NSRs, e.g., double right arrow inverted left perpendicular) are sometimes much more informative than positive sequential rules (PSRs, e.g., double right arrow ), as they can provide valuable decision-making information from both negative and positive sides. Very limited NSR mining algorithms are available now and most of them discover NSRs only from positive sequential patterns (PSPs, e.g., ) rather than from negative sequential patterns (NSPs, e.g., ), which may result in a loss of important information. However, discovering NSRs (e.g., double right arrow) from NSPs is much more difficult than mining NSRs from PSPs because NSPs do not satisfy the downward closure property. In addition, it is very difficult to find which kind of NSRs should be generated. This paper proposes a novel algorithm named nspRule to address all these difficulties. The experiment results on real-life and synthetic datasets show that nspRule can mine NSRs correctly and efficiently w.r.t. several aspects including rule number, runtime, memory usage and scalabilit.

关键词： negative sequential rule negative sequential pattern Positive sequential rule Positive sequential pattern

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An Efficient GA-Based Algorithm for Mining negative sequential patterns

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14th Pacific-Asia Conference on Knowledge Discovery and Data Mining

作者： Zheng, Zhigang Zhao, Yanchang Zuo, Ziye Cao, Longbing Univ Technol Sydney Fac Engn & IT Ctr Quantum Computat & Intelligent Syst Data Sci & Knowledge Discovery Res Lab Sydney NSW 2007 Australia

ISBN: (纸本)9783642136566

negative sequential pattern mining has attracted increasing concerns in recent data mining research because it considers negative relationships between itemsets, which are ignored by positive sequential pattern mining. However, the search space for mining negative patterns is much bigger than that for positive ones. When the support threshold is low, in particular, there will be huge amounts of negative candidates. This paper proposes a Genetic Algorithm (GA) based algorithm to find negative sequential patterns with novel crossover and mutation operations, which are efficient at passing good genes on to next generations without generating candidates. An effective dynamic fitness function and a pruning method are also provided to improve performance. The results of extensive experiments show that the proposed method can find negative patterns efficiently and has remarkable performance compared with some other algorithms of negative pattern mining.

关键词： negative sequential pattern Genetic Algorithm Sequence Mining Data Mining

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negative-GSP: an efficient method for mining negative sequential patterns 09

Negative-GSP: an efficient method for mining negative sequen...

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Proceedings of the Eighth Australasian Data Mining Conference - Volume 101

作者： Zhigang Zheng Yanchang Zhao Ziye Zuo Longbing Cao University of Technology Sydney Australia

ISBN: (纸本)9781920682828

Different from traditional positive sequential pattern mining, negative sequential pattern mining considers both positive and negative relationships between items. negative sequential pattern mining doesn't necessarily follow the Apriori principle, and the searching space is much larger than positive pattern mining. Giving definitions and some constraints of negative sequential patterns, this paper proposes a new method for mining negative sequential patterns, called negative-GSP. negative-GSP can find negative sequential patterns effectively and efficiently by joining and pruning, and extensive experimental results show the efficiency of the method.

关键词： data mining sequence mining negative sequential pattern

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