Expectation-maximization Clustering

Expectation-Maximization Clustering is a probabilistic clustering method that iteratively refines cluster assignments by alternating between estimating the probability of data points belonging to clusters (Expectation step) and updating the cluster parameters to maximize the likelihood of the observed data (Maximization step).

133

Publications

9.6K

Citations

389

Authors

202

Institutions

Publications per year

2017–2026

29

Variants

Em Clustering

Authors

389

  1. EO
    Edward Omiecinski

    Georgia Institute of Technology, United States

    3 publications · 114 citations

    3

    h-index

  2. CO
    Carlos Ordońẽz

    University of Houston, United States

    3 publications · 114 citations

    3

    h-index

  3. CR
    Cory Reina

    Microsoft (United States), United States

    3 publications · 382 citations

    3

    h-index

  4. UM
    Usama M. Fayyad

    Jet Propulsion Laboratory, United States

    3 publications · 382 citations

    3

    h-index

  5. AT
    A. Topchy

    Michigan State University, United States

    2 publications · 733 citations

    2

    h-index

1–5 of 389

Venues

  1. Bioinformatics

    5 publications · 1.6K citations

    5

    h-index

  2. arXiv (Cornell University)

    Repository · 5 publications · 86 citations

    5

    h-index

  3. 3

    h-index

  4. Pattern Recognition

    2 publications · 469 citations

    2

    h-index

  5. Neural Computation

    2 publications · 124 citations

    2

    h-index

1–5