Pattern Mining In Sequences

Pattern Mining in Sequences is a computational methodology within data mining that identifies recurring, significant, and semantically meaningful subsequences or structural regularities in ordered data sequences, distinguished by its focus on extracting temporal, positional, or sequential dependencies that characterize the inherent patterns in sequential datasets.

220

Publications

13K

Citations

533

Authors

284

Institutions

Publications per year

2017–2026

56

Authors

533

  1. PF
    Philippe Fournier‐Viger

    Harbin Institute of Technology, China

    16 publications · 1.2K citations

    16

    h-index

  2. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    15 publications · 1.2K citations

    13

    h-index

  3. XW
    Xindong Wu

    Hefei University of Technology, China

    11 publications · 474 citations

    11

    h-index

  4. WG
    Wensheng Gan

    Jinan University, China

    10 publications · 287 citations

    10

    h-index

  5. JC
    Jerry Chun‐Wei Lin

    Western Norway University of Applied Sciences, Norway

    8 publications · 305 citations

    8

    h-index

1–5 of 533

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 11 publications · 2.7K citations

    11

    h-index

  2. ACM Transactions on Knowledge Discovery from Data

    TKDD · 10 publications · 365 citations

    10

    h-index

  3. Information Sciences

    6 publications · 179 citations

    6

    h-index

  4. IEEE Access

    6 publications · 102 citations

    6

    h-index

  5. Knowledge and Information Systems

    6 publications · 194 citations

    6

    h-index

1–5