Association Rule Mining

Association Rule Mining is a data mining technique that identifies strong relationships or co-occurrence patterns among variables in large datasets by discovering rules that express associations between itemsets, characterized by measures such as support and confidence.

4K

Publications

268.1K

Citations

8.7K

Authors

2.5K

Institutions

Publications per year

2017–2026

1.1K

Authors

8.7K

  1. TH
    Tzung‐Pei Hong

    National University of Kaohsiung, Taiwan

    53 publications · 2.3K citations

    26

    h-index

  2. MJ
    Mohammed J. Zaki

    Rensselaer Polytechnic Institute, United States

    23 publications · 7.1K citations

    22

    h-index

  3. SV
    Sebastián Ventura

    University of Córdoba, Spain

    25 publications · 1.3K citations

    19

    h-index

  4. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    21 publications · 7.7K citations

    18

    h-index

  5. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    21 publications · 3.8K citations

    18

    h-index

1–5 of 8.7K

Venues

  1. Expert Systems with Applications

    104 publications · 6.3K citations

    48

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 56 publications · 11.9K citations

    41

    h-index

  3. Information Sciences

    45 publications · 2.5K citations

    30

    h-index

  4. Knowledge-Based Systems

    42 publications · 2.6K citations

    30

    h-index

  5. IEEE Access

    39 publications · 2K citations

    24

    h-index

1–5