Pattern Quality Assessment

Pattern Quality Assessment is a systematic evaluation framework that quantifies the reliability, validity, and usefulness of discovered patterns in data by measuring their structural coherence, statistical significance, and interpretability relative to domain-specific criteria.

62

Publications

2K

Citations

240

Authors

93

Institutions

Publications per year

2017–2026

14

Authors

240

  1. JV
    Jilles Vreeken

    Max Planck Institute for Informatics, Germany

    3 publications · 131 citations

    3

    h-index

  2. XK
    Xrysovalantis Kavousianos

    University of Ioannina, Greece

    2 publications · 22 citations

    2

    h-index

  3. GH
    Gary H. Bernstein

    University of Notre Dame, United States

    2 publications · 250 citations

    2

    h-index

  4. TC
    Taylor Cassidy

    DEVCOM Army Research Laboratory, United States

    2 publications · 89 citations

    2

    h-index

  5. TH
    Timothy Hanratty

    DEVCOM Army Research Laboratory, United States

    2 publications · 89 citations

    2

    h-index

1–5 of 240

Venues

  1. 4

    h-index

  2. Microscopy and Microanalysis

    3 publications · 86 citations

    3

    h-index

  3. 2

    h-index

  4. Journal of Microscopy

    2 publications · 41 citations

    2

    h-index

  5. Optics Express

    2 publications · 65 citations

    2

    h-index

1–5