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.

19

Publications

839

Citations

49

Authors

20

Institutions

Publications per year

2017–2026

8

Authors

49

  1. JL
    Jessica Lin

    George Mason University, United States

    9 publications · 307 citations

    9

    h-index

  2. YG
    Yifeng Gao

    George Mason University, United States

    5 publications · 105 citations

    5

    h-index

  3. PN
    Pawan Nunthanid

    Chulalongkorn University, Thailand

    2 publications · 67 citations

    2

    h-index

  4. CA
    Chotirat Ann Ratanamahatana

    Chulalongkorn University, Thailand

    2 publications · 67 citations

    2

    h-index

  5. TO
    Tim Oates

    University of Maryland, Baltimore County, United States

    2 publications · 140 citations

    2

    h-index

1–5 of 49

Venues

  1. Data Mining and Knowledge Discovery

    2 publications · 86 citations

    2

    h-index

  2. Computer Methods and Programs in Biomedicine

    1 publications · 16 citations

    1

    h-index

  3. Knowledge and Information Systems

    1 publications · 23 citations

    1

    h-index

  4. Nucleic Acids Research

    1 publications · 25 citations

    1

    h-index

  5. Knowledge-Based Systems

    1 publications · 66 citations

    1

    h-index

1–5