Distributed Frequent Pattern Mining

Distributed Frequent Pattern Mining is a computational framework within frequent pattern mining that identifies recurring itemsets across multiple distributed datasets by leveraging parallel and distributed processing architectures to handle large-scale data.

146

Publications

9.5K

Citations

354

Authors

185

Institutions

Publications per year

2017–2026

36

Authors

354

  1. AS
    Assaf Schuster

    Technion – Israel Institute of Technology, Israel

    7 publications · 349 citations

    7

    h-index

  2. RW
    Ran Wolff

    Technion – Israel Institute of Technology, Israel

    7 publications · 349 citations

    7

    h-index

  3. SO
    Salvatore Orlando

    Ca' Foscari University of Venice, Italy

    5 publications · 155 citations

    5

    h-index

  4. KW
    Kawuu W. Lin

    National Kaohsiung University of Applied Sciences, Taiwan

    4 publications · 79 citations

    4

    h-index

  5. SP
    Srinivasan Parthasarathy

    The Ohio State University, United States

    3 publications · 108 citations

    3

    h-index

1–5 of 354

Venues

  1. Information Sciences

    5 publications · 224 citations

    5

    h-index

  2. Knowledge and Information Systems

    4 publications · 151 citations

    4

    h-index

  3. Data Mining and Knowledge Discovery

    4 publications · 1.4K citations

    4

    h-index

  4. Expert Systems with Applications

    4 publications · 93 citations

    4

    h-index

  5. Cluster Computing

    3 publications · 149 citations

    3

    h-index

1–5