Gap-constrained Pattern Mining

Gap-Constrained Pattern Mining is a sequence mining methodology that restricts the distance between consecutive elements in a pattern to a predefined maximum gap threshold, thereby ensuring temporal or positional coherence in discovered sequential patterns.

41

Publications

1.6K

Citations

109

Authors

67

Institutions

Publications per year

2017–2026

9

Authors

109

  1. XW
    Xindong Wu

    Hefei University of Technology, China

    15 publications · 525 citations

    13

    h-index

  2. YW
    Youxi Wu

    Hebei University of Technology, China

    11 publications · 438 citations

    11

    h-index

  3. YL
    Yan Li

    Hebei University of Technology, China

    5 publications · 192 citations

    5

    h-index

  4. XZ
    Xingquan Zhu

    Florida Atlantic University, United States

    5 publications · 251 citations

    5

    h-index

  5. LG
    Lei Guo

    Hebei University of Technology, China

    4 publications · 115 citations

    4

    h-index

1–5 of 109

Venues

  1. Applied Intelligence

    5 publications · 156 citations

    5

    h-index

  2. ACM Transactions on Knowledge Discovery from Data

    TKDD · 4 publications · 249 citations

    4

    h-index

  3. Knowledge and Information Systems

    2 publications · 108 citations

    2

    h-index

  4. IEEE Transactions on Cybernetics

    2 publications · 156 citations

    2

    h-index

  5. International Journal of Foundations of Computer Science

    IJFCS · 1 publications · 27 citations

    1

    h-index

1–5