Variable-length Motif Discovery
Variable-Length Motif Discovery is a computational method in time series analysis that identifies repeated patterns of varying durations within sequential data, distinguishing itself from fixed-length approaches by allowing the length of the motifs to adapt dynamically based on underlying data structures.
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839
Citations
49
Authors
20
Institutions
Publications per year
2017–2026
8
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19
Isidore Rigoutsos, Tien Huynh, Kevin C. Miranda et al. · Proceedings of the National Academy of Sciences · 2006 · 132 citations · Full text
Visualizing Variable-Length Time Series Motifs
Yuan Li, Jessica Lin, Tim Oates · 2012 · 83 citations · Full text
Characterization of genome-wide STR variation in 6487 human genomes
Yirong Shi, Yiwei Niu, Peng Zhang et al. · Nature Communications · 2023 · 68 citations · Full text
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George Mason University
Fairfax, United States
8 h-index
17 publications
463 citations
Chulalongkorn University
Bangkok, Thailand
2 h-index
6 publications
201 citations

City University of Hong Kong
Hong Kong, Hong Kong
1 h-index
2 publications
132 citations
Technion – Israel Institute of Technology
Haifa, Israel
1 h-index
2 publications
50 citations
Princeton University
Princeton, United States
1 h-index
5 publications
660 citations
U.S. Army Engineer Research and Development Center
Vicksburg, United States
1 h-index
3 publications
171 citations
University of Southern California
Los Angeles, United States
1 h-index
2 publications
24 citations
Los Alamos National Laboratory
Los Alamos, United States
1 h-index
1 publications
57 citations
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2 publications · 86 citations
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1 publications · 16 citations
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1 publications · 23 citations
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1 publications · 25 citations
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1 publications · 66 citations
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