Online Clustering

Online Clustering is a dynamic document clustering approach that iteratively assigns incoming documents to clusters in a single pass, without prior knowledge of the total dataset, thereby distinguishing itself from batch clustering methods by its sequential and incremental processing of textual data.

258

Publications

11.5K

Citations

698

Authors

305

Institutions

Publications per year

2017–2026

84

Variants

Incremental Clustering

Authors

698

  1. PA
    Plamen Angelov

    Lancaster University, United Kingdom

    9 publications · 424 citations

    9

    h-index

  2. YB
    Yevgeniy Bodyanskiy

    Kharkiv National University of Radio Electronics, Ukraine

    7 publications · 157 citations

    7

    h-index

  3. OK
    Oleksii K. Tyshchenko

    Kharkiv National University of Radio Electronics, Ukraine

    7 publications · 157 citations

    7

    h-index

  4. JC
    James C. Bezdek

    University of West Florida, United States

    6 publications · 229 citations

    6

    h-index

  5. ZH
    Zhengbing Hu

    Central China Normal University, China

    5 publications · 113 citations

    5

    h-index

1–5 of 698

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 245 citations

    7

    h-index

  2. IEEE Transactions on Fuzzy Systems

    TFS · 7 publications · 441 citations

    7

    h-index

  3. Neurocomputing

    5 publications · 163 citations

    5

    h-index

  4. Data & Knowledge Engineering

    DKE · 4 publications · 382 citations

    4

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 4 publications · 470 citations

    4

    h-index

1–5