Closed Frequent Itemset Mining

Closed Frequent Itemset Mining is a pattern mining methodology that identifies maximal frequent itemsets under set inclusion, distinguishing itself from general frequent itemset mining by excluding all non-maximal itemsets that have the same support as their supersets.

178

Publications

14.3K

Citations

376

Authors

188

Institutions

Publications per year

2017–2026

35

Authors

376

  1. BL
    Bac Le

    Ho Chi Minh City University of Science, Vietnam

    11 publications · 330 citations

    11

    h-index

  2. BV
    Bay Vo

    Ho Chi Minh City University of Technology, Vietnam

    11 publications · 296 citations

    11

    h-index

  3. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    10 publications · 2.8K citations

    10

    h-index

  4. JW
    Jianyong Wang

    Tsinghua University, China

    9 publications · 1.3K citations

    9

    h-index

  5. CL
    Claudio Lucchese

    Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo", Italy

    6 publications · 439 citations

    6

    h-index

1–5 of 376

Venues

  1. Expert Systems with Applications

    15 publications · 443 citations

    14

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 9 publications · 1.9K citations

    9

    h-index

  3. Applied Intelligence

    7 publications · 152 citations

    7

    h-index

  4. Information Sciences

    6 publications · 177 citations

    6

    h-index

  5. Proceedings

    ICDM · 6 publications · 314 citations

    6

    h-index

1–5