Semi-supervised Clustering

Semi-Supervised Clustering is a clustering method that incorporates limited supervision, typically in the form of pairwise constraints or labeled examples, to guide the formation of clusters in otherwise unsupervised data.

605

Publications

29.6K

Citations

2.1K

Authors

758

Institutions

Publications per year

2017–2026

283

Authors

2.1K

  1. JH
    Junhui Hou

    City University of Hong Kong, Hong Kong SAR China

    9 publications · 417 citations

    9

    h-index

  2. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    7 publications · 1.2K citations

    9

    h-index

  3. YJ
    Yuheng Jia

    City University of Hong Kong, Hong Kong SAR China

    9 publications · 417 citations

    9

    h-index

  4. LJ
    Licheng Jiao

    Xidian University, China

    8 publications · 304 citations

    8

    h-index

  5. SK
    Sam Kwong

    City University of Hong Kong, Hong Kong SAR China

    8 publications · 382 citations

    8

    h-index

1–5 of 2.1K

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 20 publications · 1.2K citations

    20

    h-index

  2. 14

    h-index

  3. Pattern Recognition

    12 publications · 822 citations

    13

    h-index

  4. IEEE Access

    14 publications · 479 citations

    13

    h-index

  5. IEEE Transactions on Cybernetics

    13 publications · 573 citations

    12

    h-index

1–5