Hierarchical Clustering

Hierarchical Clustering is a clustering method that belongs to unsupervised learning techniques, distinguished by its construction of a hierarchy of clusters either through successive merging of smaller clusters (agglomerative) or recursive splitting of larger clusters (divisive), thereby producing a dendrogram that represents nested cluster structures at multiple granularity levels.

5K

Publications

386.9K

Citations

19.2K

Authors

4.7K

Institutions

Publications per year

2017–2026

1.8K

Authors

19.2K

  1. LJ
    Lawrence J. Hubert

    University of Illinois Urbana-Champaign, United States

    16 publications · 2.1K citations

    16

    h-index

  2. AS
    Adam Smoliński

    Central Mining Institute, Poland

    11 publications · 312 citations

    11

    h-index

  3. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    11 publications · 967 citations

    11

    h-index

  4. SM
    Sadaaki Miyamoto

    University of Tsukuba, Japan

    10 publications · 219 citations

    10

    h-index

  5. GK
    George Karypis

    University of Minnesota, United States

    11 publications · 6K citations

    10

    h-index

1–5 of 19.2K

Venues

  1. Bioinformatics

    72 publications · 21K citations

    43

    h-index

  2. PLoS ONE

    118 publications · 6.4K citations

    41

    h-index

  3. Monthly Notices of the Royal Astronomical Society

    64 publications · 10.7K citations

    39

    h-index

  4. The Astrophysical Journal

    ApJ · 60 publications · 7.6K citations

    35

    h-index

  5. BMC Bioinformatics

    60 publications · 4.5K citations

    33

    h-index

1–5