Probabilistic Clustering

Probabilistic Clustering is a clustering methodology that models cell-to-cluster assignments through probability distributions, distinguishing itself by quantifying uncertainty in cluster membership via statistical inference rather than enforcing hard or deterministic partitions.

350

Publications

22K

Citations

1.2K

Authors

475

Institutions

Publications per year

2017–2026

84

Authors

1.2K

  1. PS
    Padhraic Smyth

    University of California, Irvine, United States

    7 publications · 812 citations

    7

    h-index

  2. JC
    James C. Bezdek

    University of West Florida, United States

    5 publications · 541 citations

    5

    h-index

  3. SG
    Scott Gaffney

    University of California, Irvine, United States

    4 publications · 533 citations

    4

    h-index

  4. PL
    Pedro Larrañaga

    Universidad Politécnica de Madrid, Spain

    4 publications · 149 citations

    4

    h-index

  5. BK
    Ben Kao

    University of Hong Kong, Hong Kong SAR China

    3 publications · 377 citations

    3

    h-index

1–5 of 1.2K

Venues

  1. Expert Systems with Applications

    8 publications · 336 citations

    8

    h-index

  2. IEEE Transactions on Fuzzy Systems

    TFS · 8 publications · 563 citations

    8

    h-index

  3. Pattern Recognition Letters

    7 publications · 429 citations

    7

    h-index

  4. Pattern Recognition

    7 publications · 358 citations

    7

    h-index

  5. 7

    h-index

1–5