Sequential Pattern Discovery

Sequential Pattern Discovery is a data mining technique that identifies statistically significant patterns of ordered events or items within sequential datasets, distinguishing itself from other pattern discovery methods by its focus on the temporal or sequential relationships among elements.

332

Publications

16.1K

Citations

727

Authors

363

Institutions

Publications per year

2017–2026

79

Authors

727

  1. PF
    Philippe Fournier‐Viger

    Harbin Institute of Technology, China

    14 publications · 689 citations

    13

    h-index

  2. TH
    Tzung‐Pei Hong

    National University of Kaohsiung, Taiwan

    11 publications · 351 citations

    11

    h-index

  3. XW
    Xindong Wu

    Hefei University of Technology, China

    9 publications · 341 citations

    9

    h-index

  4. TC
    Tony Cheng-Kui Huang

    National Chung Cheng University, Taiwan

    8 publications · 262 citations

    8

    h-index

  5. MT
    Maguelonne Teisseire

    Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, France

    8 publications · 236 citations

    8

    h-index

1–5 of 727

Venues

  1. Expert Systems with Applications

    13 publications · 651 citations

    12

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 11 publications · 708 citations

    11

    h-index

  3. Applied Intelligence

    12 publications · 356 citations

    11

    h-index

  4. Knowledge-Based Systems

    9 publications · 545 citations

    9

    h-index

  5. Information Sciences

    9 publications · 233 citations

    9

    h-index

1–5