Subgroup Discovery Evaluation

Subgroup Discovery Evaluation is a systematic methodological framework within pattern evaluation that assesses the quality, significance, and interpretability of discovered subgroups by combining statistical measures, domain-specific relevance criteria, and comprehensibility indicators to distinguish meaningful patterns from background data.

98

Publications

4.8K

Citations

261

Authors

148

Institutions

Publications per year

2017–2026

33

Authors

261

  1. NL
    Nada Lavrač

    Jožef Stefan Institute, Slovenia

    13 publications · 698 citations

    13

    h-index

  2. AK
    Arno Knobbe

    Leiden University, Netherlands

    12 publications · 506 citations

    11

    h-index

  3. MJ
    María José del Jesús

    Universidad de Jaén, Spain

    10 publications · 723 citations

    10

    h-index

  4. PG
    Pedro González

    Universidad de Jaén, Spain

    10 publications · 479 citations

    10

    h-index

  5. FH
    Francisco Herrera

    Universidad de Granada, Spain

    8 publications · 625 citations

    8

    h-index

1–5 of 261

Venues

  1. Data Mining and Knowledge Discovery

    7 publications · 362 citations

    7

    h-index

  2. Knowledge-Based Systems

    4 publications · 99 citations

    4

    h-index

  3. Expert Systems with Applications

    4 publications · 158 citations

    4

    h-index

  4. Machine Learning

    4 publications · 246 citations

    4

    h-index

  5. Journal of Biomedical Informatics

    3 publications · 103 citations

    3

    h-index

1–5