An Efficient Method for Modeling Nonoccurring Behaviors by Negative Sequential Patterns With Loose Constraints

Qiu, Ping; Gong, Yongshun; Zhao, Yuhai; Cao, Longbing; Zhang, Chengqi; Xiang, Dong

doi:10.1109/tnnls.2021.3063162

Cited by 13 publications

(2 citation statements)

References 33 publications

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“…However, many scholars have discovered that missing data in a sequence may also contain important information (47). Therefore, negative SPM has also emerged, and many negative SPM algorithms have been proposed (48,49,50). For example, Cao et al ( 51) proposed e-NSP, which can mine negative patterns without rescanning the database to improve efficiency.…”

Section: Related Workmentioning

confidence: 99%

One-off Negative Sequential Pattern Mining

Wu¹,

Chen²,

Li³

et al. 2022

Preprint

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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 paper 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 paper 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.

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Section: Related Workmentioning

confidence: 99%

One-off Negative Sequential Pattern Mining

Wu¹,

Chen²,

Li³

et al. 2022

Preprint

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“…For instance, consider the cross-interaction among various factors influencing the risk of import and export enterprises in space to derive distribution patterns among these factors (Luo et al, 2023). In particular, SCP mining can be used to identify spatio-temporal crime patterns with heterogeneity (Li et al, 2022) and can also model frequently occurring events and behaviors to derive valuable information (Qiu et al, 2023), while optimal cluster-based feature extraction can be used to refine the intrusion detection systems (Maheswari et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Regional Co-Location Pattern Mining Based on Efficient Density Peak Clustering and Maximal Fuzzy Grid Cliques

Zhou,

Wang,

Wang

et al. 2024

JDSIS

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Due to the heterogeneity of data distribution in real life and the spatial autocorrelation among spatial instances, traditional spatial co-location pattern mining methods tend to ignore valuable information specific to local regions. To address the limitation, regional co-location pattern mining has been proposed to find patterns that may be hidden within local regions. In this paper, a fuzzy regional co-location pattern mining framework based on efficient density peak clustering and maximal fuzzy grid cliques is presented. By incorporating a grid-splitting method and fuzzy theory, an efficient density peak clustering algorithm is proposed to divide the global area into distinct local regions. Furthermore, we propose a method to materialize the neighbor relationships between instances based on the maximal fuzzy grid cliques, and parallelize the clustering process to improve the algorithm efficiency. Experimental results show that the proposed algorithm can not only reduce the time consumption by about 40%, but also mine meaningful patterns with tighter instance distributions.

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