Closed Sequential Pattern Mining

Closed Sequential Pattern Mining is a sequential pattern mining task that identifies maximal sequences which are not strictly contained in any other sequence with the same support, thereby eliminating redundant patterns while preserving the complete frequent pattern information.

48

Publications

2.6K

Citations

129

Authors

67

Institutions

Publications per year

2017–2026

9

Authors

129

  1. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    5 publications · 905 citations

    5

    h-index

  2. DL
    David Lo

    Singapore Management University, Singapore

    4 publications · 205 citations

    4

    h-index

  3. SK
    Siau‐Cheng Khoo

    National University of Singapore, Singapore

    3 publications · 188 citations

    3

    h-index

  4. XY
    Xifeng Yan

    University of California, Santa Barbara, United States

    3 publications · 710 citations

    3

    h-index

  5. JW
    Jianyong Wang

    Tsinghua University, China

    3 publications · 138 citations

    3

    h-index

1–5 of 129

Venues

  1. Knowledge and Information Systems

    3 publications · 235 citations

    3

    h-index

  2. IEEE Access

    2 publications · 36 citations

    2

    h-index

  3. Data & Knowledge Engineering

    DKE · 2 publications · 89 citations

    2

    h-index

  4. Knowledge-Based Systems

    2 publications · 148 citations

    2

    h-index

  5. arXiv (Cornell University)

    Repository · 2 publications · 53 citations

    2

    h-index

1–5