Event Sequence Mining

Event Sequence Mining is a temporal data mining task that identifies frequent and significant patterns of discrete events occurring in chronological order within an event sequence, distinguishing it from other temporal pattern discovery methods by focusing on the temporal ordering and co-occurrence of symbolic events rather than numerical time series or interval-based behaviors.

156

Publications

6.2K

Citations

444

Authors

193

Institutions

Publications per year

2017–2026

49

Authors

444

  1. SL
    Srivatsan Laxman

    Microsoft Research (India), India

    5 publications · 321 citations

    5

    h-index

  2. RG
    Robert Gwadera

    Purdue University West Lafayette, United States

    5 publications · 217 citations

    5

    h-index

  3. JW
    Jin Wang

    University of California, Los Angeles, United States

    4 publications · 133 citations

    4

    h-index

  4. HZ
    Hongyuan Zha

    Georgia Institute of Technology, United States

    4 publications · 178 citations

    4

    h-index

  5. QH
    Qing He

    Chinese Academy of Sciences, China

    4 publications · 128 citations

    4

    h-index

1–5 of 444

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 146 citations

    5

    h-index

  2. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 5 publications · 276 citations

    5

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 4 publications · 217 citations

    4

    h-index

  4. Expert Systems with Applications

    4 publications · 105 citations

    4

    h-index

  5. Knowledge-Based Systems

    3 publications · 80 citations

    3

    h-index

1–5