Frequent Pattern Mining

Frequent Pattern Mining is a data mining task that systematically discovers all patterns (such as itemsets, subsequences, or substructures) in a dataset whose occurrence frequency (support) exceeds a user-specified minimum threshold.

5.1K

Publications

358.9K

Citations

8.7K

Authors

2.3K

Institutions

Publications per year

2017–2026

1.1K

Authors

8.7K

  1. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    69 publications · 24.5K citations

    49

    h-index

  2. PF
    Philippe Fournier‐Viger

    Harbin Institute of Technology, China

    101 publications · 6.1K citations

    46

    h-index

  3. JC
    Jerry Chun‐Wei Lin

    Western Norway University of Applied Sciences, Norway

    108 publications · 5.3K citations

    43

    h-index

  4. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    72 publications · 9.8K citations

    42

    h-index

  5. UY
    Unil Yun

    Sejong University, South Korea

    91 publications · 4.1K citations

    41

    h-index

1–5 of 8.7K

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 131 publications · 18.3K citations

    68

    h-index

  2. Expert Systems with Applications

    180 publications · 9.6K citations

    56

    h-index

  3. Information Sciences

    126 publications · 5.7K citations

    46

    h-index

  4. Knowledge-Based Systems

    99 publications · 5.1K citations

    45

    h-index

  5. Knowledge and Information Systems

    102 publications · 4.6K citations

    39

    h-index

1–5