Multilevel Frequent Pattern Mining

Multilevel Frequent Pattern Mining is a data mining methodology that discovers frequent patterns across multiple levels of abstraction in a hierarchically organized dataset, distinguishing itself from flat-level frequent pattern mining by incorporating concept hierarchies to capture itemset relationships at varying degrees of generalization.

31

Publications

2.6K

Citations

87

Authors

51

Institutions

Publications per year

2017–2026

9

Authors

87

  1. RJ
    Ridhi Jain

    Technology Innovation Institute, United Arab Emirates

    2 publications · 44 citations

    2

    h-index

  2. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    2 publications · 1.7K citations

    2

    h-index

  3. LT
    Loan T. T. Nguyen

    Ho Chi Minh City University of Technology, Vietnam

    2 publications · 56 citations

    2

    h-index

  4. TD
    Trinh D. D. Nguyen

    Ho Chi Minh City International University, Vietnam

    2 publications · 56 citations

    2

    h-index

  5. ZY
    Zhenzhou Yuan

    Beijing Jiaotong University, China

    2 publications · 62 citations

    2

    h-index

1–5 of 87

Venues

  1. PLoS ONE

    1 publications · 14 citations

    1

    h-index

  2. Accident Analysis & Prevention

    1 publications · 32 citations

    1

    h-index

  3. Progress in Artificial Intelligence

    1 publications · 11 citations

    1

    h-index

  4. 1

    h-index

  5. arXiv (Cornell University)

    Repository · 1 publications · 11 citations

    1

    h-index

1–5