Discovering Patterns With Weak-Wildcard Gaps

Tan, Chaodong; Min, Fan; Wang, Min; Zhang, Heng‐Ru; Zhang, Zhiheng

doi:10.1109/access.2016.2593953

Cited by 23 publications

(9 citation statements)

References 33 publications

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“…The occurrence of STAP can be directly obtained by looking up the list. The time delays in STAP, also called periodic wildcard gaps [44]- [47] coincide with that of DTA when the lower bound equals 0.…”

Section: And Maximummentioning

confidence: 99%

“…i.e., ∅ matches the MTS at any time point, it is called a wildcard. For univariate time series, a wildcard [44], [45], [47] (also called a not cared position) matches any character which is time point in an MTS. To judge whether or not state s is frequent in S, we need to calculate its support, which is given by Sup(s, S) = t∈T m(S, t, s)…”

Section: A Data Modelmentioning

confidence: 99%

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Frequent State Transition Patterns of Multivariate Time Series

Zhang

Min

2019

IEEE Access

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Sequence pattern discovery is a key issue in multivariate time series analysis. Popular approaches first obtain the pattern of each single-variate time series and then obtain cross-variate associations. In this paper, we consider different variables at the same time during pattern construction, and propose a new type of pattern called State Transition pAttern with Periodic wildcard gaps (STAP). Compared to previous types, STAP reveals stronger cross associations among different variables and provides better interpretability for decision makers. We design an approach with two stages, namely frequent state discovery and pattern synthesis, to obtain frequent STAPs. We propose two pre-pruning and an Apriori-pruning techniques to speed up pattern discovery. We also propose two post-pruning techniques to simplify the output and a visualization way to support expert decision. Experimental results on four real-world datasets demonstrate 1) STAP captures the cross and temporal associations; 2) the five pruning and pattern synthesis techniques are quite effective; and 3) visualization technique greatly increases the readability of STAP.INDEX TERMS Cross-variate association, multivariate time series, sequence pattern.

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Section: And Maximummentioning

confidence: 99%

Section: A Data Modelmentioning

confidence: 99%

Frequent State Transition Patterns of Multivariate Time Series

Zhang

Min

2019

IEEE Access

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“…In sequential pattern mining, gap constraints are often the same , such as p m − 1 [ a , b ] p m or with g a p = [ a , b ]. This mining method is also called periodic sequential pattern mining [ 35 ] and has been applied in many applications, such as time series analysis [ 36 , 37 ] and feature selection [ 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

NetNMSP: Nonoverlapping maximal sequential pattern mining

Zhang

Guo

et al. 2022

Appl Intell

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Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms focused on finding all frequent patterns and found lots of redundant short patterns. However, it not only reduces the mining efficiency, but also increases the difficulty in obtaining the demand information. To reduce the frequent patterns and retain its expression ability, this paper focuses on the Nonoverlapping Maximal Sequential Pattern (NMSP) mining which refers to finding frequent patterns whose super-patterns are infrequent. In this paper, we propose an effective mining algorithm, Nettree for NMSP mining (NetNMSP), which has three key steps: calculating the support, generating the candidate patterns, and determining NMSPs. To efficiently calculate the support, NetNMSP employs the backtracking strategy to obtain a nonoverlapping occurrence from the leftmost leaf to its root with the leftmost parent node method in a Nettree. To reduce the candidate patterns, NetNMSP generates candidate patterns by the pattern join strategy. Furthermore, to determine NMSPs, NetNMSP adopts the screening method. Experiments on biological sequence datasets verify that not only does NetNMSP outperform the state-of-the-arts algorithms, but also NMSP mining has better compression performance than closed pattern mining. On sales datasets, we validate that our algorithm guarantees the best scalability on large scale datasets. Moreover, we mine NMSPs and frequent patterns in SARS-CoV-1, SARS-CoV-2 and MERS-CoV. The results show that the three viruses are similar in the short patterns but different in the long patterns. More importantly, NMSP mining is easier to find the differences between the virus sequences.

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“…It has been widely applied in various fields, such as big data mining [4,5], big data intelligence [6], e-commerce shopping analysis [7], biological sequence analysis [8], and event analysis [9]. To handle some specific issues, many methods have been proposed, such as negative SPM [10,11], maximal frequent pattern mining [12,13], three-way pattern mining [14,15], closed SPM [16,17], gap constraint SPM [18,19].…”

Section: Introductionmentioning

confidence: 99%

NetNCSP: Nonoverlapping closed sequential pattern mining

Zhu

et al. 2020

Knowledge-Based Systems

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Discovering Patterns With Weak-Wildcard Gaps

Cited by 23 publications

References 33 publications

Frequent State Transition Patterns of Multivariate Time Series

Frequent State Transition Patterns of Multivariate Time Series

NetNMSP: Nonoverlapping maximal sequential pattern mining

NetNCSP: Nonoverlapping closed sequential pattern mining

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