Cluster Evaluation Metrics

Cluster Evaluation Metrics is a quantitative method used to assess the quality and validity of clustering results by measuring the degree of cluster cohesion, separation, and alignment with ground-truth labels or intrinsic data structures.

582

Publications

75.3K

Citations

1.3K

Authors

651

Institutions

Publications per year

2017–2026

199

Variants

Clustering Validation Metrics

Authors

1.3K

  1. JC
    James C. Bezdek

    University of West Florida, United States

    11 publications · 4.5K citations

    10

    h-index

  2. SB
    Sanghamitra Bandyopadhyay

    Indian Statistical Institute, India

    8 publications · 2.7K citations

    8

    h-index

  3. RJ
    Ricardo J. G. B. Campello

    Universidade de São Paulo, Brazil

    7 publications · 1K citations

    7

    h-index

  4. HX
    Hui Xiong

    Rutgers, The State University of New Jersey, United States

    7 publications · 2K citations

    7

    h-index

  5. SS
    Sriparna Saha

    Indian Institute of Technology Patna, India

    6 publications · 283 citations

    6

    h-index

1–5 of 1.3K

Venues

  1. Pattern Recognition

    23 publications · 4.5K citations

    21

    h-index

  2. Pattern Recognition Letters

    14 publications · 2.2K citations

    13

    h-index

  3. Information Sciences

    13 publications · 1.5K citations

    13

    h-index

  4. IEEE Access

    16 publications · 424 citations

    13

    h-index

  5. Expert Systems with Applications

    10 publications · 492 citations

    10

    h-index

1–5