Explainable Data Mining

Explainable Data Mining is a data mining approach that belongs to the class of knowledge discovery methods, distinguished by its explicit emphasis on generating human-interpretable models and results to ensure transparency, accountability, and trust in the discovered patterns.

56

Publications

4K

Citations

196

Authors

91

Institutions

Publications per year

2017–2026

29

Authors

196

  1. AI
    Alexey Ignatiev

    Monash University, Australia

    2 publications · 50 citations

    2

    h-index

  2. WL
    William Lochet

    University of Bergen, Norway

    2 publications · 39 citations

    2

    h-index

  3. JM
    João Marques‐Silva

    Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento, Portugal

    2 publications · 50 citations

    2

    h-index

  4. NP
    Nidhi Purohit

    University of Bergen, Norway

    2 publications · 39 citations

    2

    h-index

  5. KS
    Kirill Simonov

    University of Bergen, Norway

    2 publications · 39 citations

    2

    h-index

1–5 of 196

Venues

  1. 2

    h-index

  2. Data Mining and Knowledge Discovery

    2 publications · 190 citations

    2

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 34 citations

    2

    h-index

  4. Applied Sciences

    1 publications · 17 citations

    1

    h-index

  5. Journal of Software Engineering and Applications

    JSEA · 1 publications · 11 citations

    1

    h-index

1–5