Toward Better Structure and Constraint to Mine Negative Sequential Patterns

Xinming, Gao; Gong, Yongshun; Xu, Tiantian; Lü, Jinhu; Zhao, Yuhai; Xiang, Dong

doi:10.1109/tnnls.2020.3041732

Cited by 19 publications

(7 citation statements)

References 49 publications

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“…NegPSpan uses the PrefixSpan depth-first method to extract NSPs with a maximum gap constraint enabled [31]. sc-NSP is an efficient NSP mining algorithm with an improved technique [24]. The algorithm utilizes the improved PrefixSpan algorithm of a bitmap storage structure to mine PSPs, loosens the frequent constraint, exploits the NSC generation method of PNSP, and uses the most efficient bitwise-based operation to calculate the support of the NSCs.…”

Section: Related Work a Nsp Miningmentioning

confidence: 99%

“…This study investigates item-based NSP mining. Therefore, we modified the NSC generation method of sc-NSP [24] by combining the NSC generation method of NegI-NSP [21]. Thus, this influence can be used to mine interesting itembased NSPs.…”

Section: B Nsc Generation Of Modified Sc-nspmentioning

confidence: 99%

“…The analysis of non-occurring behavior can typically obtain more comprehensive information and can play an irreplaceable role in some aspects, such as in analyzing the association between treatment services and disease [21]. In addition, some efficient NSP mining algorithms have been proposed, such as Neg-GSP [22], e-NSP [23], NegI-NSP [21], and sc-NSP [24].…”

Section: Introductionmentioning

confidence: 99%

“…First, we modified the traditional NSP mining algorithm sc-NSP [24] such that the influence measure can be used to mine the NSPs of non-occurring items. This is because the candidate sequence generation method of the sc-NSP algorithm has a high matching degree with the candidate sequence generation method of the influence measure.…”

Section: Introductionmentioning

confidence: 99%

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Mining Interesting Negative Sequential Patterns Based on Influence

2023

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Negative sequential pattern (NSP) mining can capture frequently occurring and nonoccurring behavior information and can play an irreplaceable role in many applications. Most traditional NSP mining algorithms adopt a support measure to discover interesting patterns. However, the support measure does not truly reflect the interestingness of patterns in some cases. In particular, it ignores the effect of the support of every element and the order characteristics among these elements. Hence, an influence measure was proposed to truly reflect the interestingness of patterns. However, the current influence measure is used only in positive sequential pattern (PSP) mining and does not involve NSPs. To address these problems, this study proposes an algorithm, InfI-NSP, to mine interesting NSPs based on influence. First, we modify an existing NSP mining algorithm to efficiently mine NSPs. Second, we modify the influence measure and apply it to NSP mining to mine interesting NSPs. To the best of our knowledge, InfI-NSP is the first algorithm to mine interesting NSPs based on influence. Experiments on real-life and synthetic datasets show that InfI-NSP is effective.

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Section: Related Work a Nsp Miningmentioning

confidence: 99%

Section: B Nsc Generation Of Modified Sc-nspmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mining Interesting Negative Sequential Patterns Based on Influence

2023

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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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