Streaming Clustering

Streaming Clustering is a computational framework within streaming algorithms that dynamically groups sequentially arriving data points into clusters under constraints of limited memory and single-pass processing, distinguishing it from batch clustering methods by its requirement for incremental and real-time structuring of evolving data streams.

283

Publications

16.1K

Citations

655

Authors

318

Institutions

Publications per year

2017–2026

86

Authors

655

  1. TS
    Thomas Seidl

    RWTH Aachen University, Germany

    9 publications · 576 citations

    9

    h-index

  2. JC
    James C. Bezdek

    University of West Florida, United States

    6 publications · 123 citations

    6

    h-index

  3. PA
    Plamen Angelov

    Lancaster University, United Kingdom

    6 publications · 393 citations

    6

    h-index

  4. JG
    João Gama

    Universidade do Porto, Portugal

    6 publications · 456 citations

    6

    h-index

  5. PK
    Philipp Kranen

    RWTH Aachen University, Germany

    6 publications · 448 citations

    6

    h-index

1–5 of 655

Venues

  1. Information Sciences

    11 publications · 968 citations

    11

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 8 publications · 1.7K citations

    8

    h-index

  3. Knowledge-Based Systems

    6 publications · 202 citations

    6

    h-index

  4. arXiv (Cornell University)

    Repository · 6 publications · 103 citations

    6

    h-index

  5. Knowledge and Information Systems

    6 publications · 372 citations

    6

    h-index

1–5