Maximal Sequential Pattern Mining

Maximal Sequential Pattern Mining is a method in sequence mining that identifies the longest sequential patterns without sub-patterns that are also frequent, distinguishing it from other sequential pattern mining approaches by focusing exclusively on maximal-length patterns to reduce redundancy and improve efficiency.

18

Publications

671

Citations

52

Authors

27

Institutions

Publications per year

2017–2026

3

Authors

52

  1. SM
    Soon M. Chung

    Wright State University, United States

    3 publications · 80 citations

    3

    h-index

  2. JL
    Jinyan Li

    University of Technology Sydney, Australia

    2 publications · 77 citations

    2

    h-index

  3. CL
    Congnan Luo

    Wright State University, United States

    2 publications · 69 citations

    2

    h-index

  4. XW
    Xindong Wu

    Hefei University of Technology, China

    2 publications · 69 citations

    2

    h-index

  5. LG
    Lei Guo

    Hebei University of Technology, China

    2 publications · 69 citations

    2

    h-index

1–5 of 52

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 1 publications · 122 citations

    1

    h-index

  2. Data Mining and Knowledge Discovery

    1 publications · 18 citations

    1

    h-index

  3. Applied Intelligence

    1 publications · 33 citations

    1

    h-index

  4. 1

    h-index

  5. IEEE Transactions on Cybernetics

    1 publications · 36 citations

    1

    h-index

1–5