Constraint-based Sequence Mining

Constraint-Based Sequence Mining is a methodological framework within sequence mining that incorporates user-defined constraints to guide the discovery of sequential patterns, distinguishing itself by pruning the search space through syntactic, semantic, or domain-specific conditions imposed on the patterns of interest.

38

Publications

1.9K

Citations

99

Authors

66

Institutions

Publications per year

2017–2026

10

Authors

99

  1. JB
    James Bailey

    The University of Melbourne, Australia

    2 publications · 242 citations

    2

    h-index

  2. QL
    Qing Li

    City University of Hong Kong, Hong Kong SAR China

    2 publications · 59 citations

    2

    h-index

  3. SD
    Sebastian Deorowicz

    Silesian University of Technology, Poland

    2 publications · 35 citations

    2

    h-index

  4. XJ
    Xiaonan Ji

    The University of Melbourne, Australia

    2 publications · 242 citations

    2

    h-index

  5. GD
    Guozhu Dong

    Wright State University, United States

    2 publications · 242 citations

    2

    h-index

1–5 of 99

Venues

  1. Applied Intelligence

    4 publications · 76 citations

    4

    h-index

  2. Constraints

    2 publications · 42 citations

    2

    h-index

  3. Information Processing Letters

    2 publications · 176 citations

    2

    h-index

  4. Knowledge and Information Systems

    2 publications · 140 citations

    2

    h-index

  5. arXiv (Cornell University)

    Repository · 2 publications · 29 citations

    2

    h-index

1–5