Matrix Profile Clustering

Matrix Profile Clustering is a time series clustering methodology that utilizes the matrix profile structure to identify conserved patterns and anomalies by measuring the similarity of subsequences within and across time series, distinguishing itself through its reliance on indexed, distance-based motifs derived from the collective pairwise z-normalized Euclidean distances.

7

Publications

348

Citations

33

Authors

10

Institutions

Publications per year

2017–2026

7

Authors

33

  1. EK
    Eamonn Keogh

    University of California, Riverside, United States

    4 publications · 289 citations

    4

    h-index

  2. YZ
    Yan Zhu

    University of California, Riverside, United States

    3 publications · 227 citations

    3

    h-index

  3. ZZ
    Zachary Zimmerman

    University of California, Riverside, United States

    3 publications · 227 citations

    3

    h-index

  4. HL
    Hailin Li

    Huaqiao University, China

    2 publications · 30 citations

    2

    h-index

  5. HA
    Hoang Anh Dau

    University of California, Riverside, United States

    2 publications · 165 citations

    2

    h-index

1–5 of 33

Venues

  1. Data Mining and Knowledge Discovery

    3 publications · 227 citations

    3

    h-index

  2. 1

    h-index

  3. Advanced Engineering Informatics

    1 publications · 19 citations

    1

    h-index

  4. Journal of Retailing and Consumer Services

    1 publications · 11 citations

    1

    h-index

  5. Measurement

    1 publications · 15 citations

1–5