Frequent Closed Pattern Mining

Frequent Closed Pattern Mining is a method in frequent pattern mining that identifies maximal frequent itemsets by retaining only those patterns for which no proper superset has the same support, thus reducing redundancy while preserving complete frequency information.

168

Publications

13.2K

Citations

377

Authors

187

Institutions

Publications per year

2017–2026

29

Authors

377

  1. BV
    Bay Vo

    Ho Chi Minh City University of Technology, Vietnam

    12 publications · 309 citations

    11

    h-index

  2. JW
    Jianyong Wang

    Tsinghua University, China

    10 publications · 1.4K citations

    10

    h-index

  3. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    10 publications · 3.1K citations

    10

    h-index

  4. BL
    Bac Le

    Ho Chi Minh City University of Science, Vietnam

    10 publications · 317 citations

    10

    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 377

Venues

  1. Expert Systems with Applications

    15 publications · 439 citations

    12

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 9 publications · 1.9K citations

    9

    h-index

  3. Information Sciences

    7 publications · 269 citations

    7

    h-index

  4. Applied Intelligence

    6 publications · 135 citations

    6

    h-index

  5. IEEE Access

    5 publications · 107 citations

    5

    h-index

1–5