Maximal Sequential Pattern Mining
Maximal Sequential Pattern Mining is a method in sequence mining that identifies the longest sequential patterns without sub-patterns that are also frequent, distinguishing it from other sequential pattern mining approaches by focusing exclusively on maximal-length patterns to reduce redundancy and improve efficiency.
18
Publications
671
Citations
52
Authors
27
Institutions
Publications per year
2017–2026
3
Parents
18
OPP-Miner: Order-Preserving Sequential Pattern Mining for Time Series
Youxi Wu, Qian Hu, Yan Li et al. · IEEE Transactions on Cybernetics · 2022 · 36 citations
Computing, Data Science, Algorithms +10
1–5 of 18
52
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2
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2
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2
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2
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1–5 of 52
27
Wright State University
Dayton, United States
3 h-index
5 publications
149 citations

Hebei University of Technology
Tianjin, China
2 h-index
9 publications
309 citations

Hefei University of Technology
Hefei, China
2 h-index
2 publications
69 citations
University of California, Davis
Davis, United States
1 h-index
1 publications
122 citations

National Institute of Astrophysics, Optics and Electronics
Puebla City, Mexico
1 h-index
2 publications
58 citations
National University of Singapore
Singapore, Singapore
1 h-index
2 publications
86 citations
Florida Atlantic University
Boca Raton, United States
1 h-index
1 publications
36 citations
Jilin University
Changchun, China
1 h-index
4 publications
56 citations
1–8 of 27
TVCG · 1 publications · 122 citations
1
h-index
1 publications · 18 citations
1
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1 publications · 33 citations
1
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1 publications · 17 citations
1
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1 publications · 36 citations
1
h-index
1–5