Shapelet Discovery

Shapelet Discovery is a temporal data mining technique that identifies discriminative subsequences of time series data whose shape characteristics enable effective classification by distinguishing between different classes of temporal patterns.

41

Publications

3.3K

Citations

131

Authors

72

Institutions

Publications per year

2017–2026

22

Authors

131

  1. EK
    Eamonn Keogh

    University of California, Riverside, United States

    5 publications · 867 citations

    5

    h-index

  2. MW
    Martin Wistuba

    University of Hildesheim, Germany

    4 publications · 640 citations

    4

    h-index

  3. LS
    Lars Schmidt-Thieme

    University of Hildesheim, Germany

    4 publications · 640 citations

    4

    h-index

  4. JG
    Josif Grabocka

    University of Hildesheim, Germany

    4 publications · 640 citations

    4

    h-index

  5. AM
    Abdullah Mueen

    University of New Mexico, United States

    4 publications · 475 citations

    4

    h-index

1–5 of 131

Venues

  1. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 301 citations

    3

    h-index

  2. Data Mining and Knowledge Discovery

    2 publications · 162 citations

    2

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 99 citations

    2

    h-index

  4. Pattern Recognition

    2 publications · 41 citations

    2

    h-index

  5. 1

    h-index

1–5