K-means Quantization

K-Means Quantization is a vector quantization method that partitions continuous or high-precision data points into a finite set of representative clusters by minimizing the within-cluster variance through iterative application of the k-means clustering algorithm.

83

Publications

6.8K

Citations

213

Authors

102

Institutions

Publications per year

2017–2026

17

Variants

K-means Vector Quantization

Authors

213

  1. ME
    M. Emre Celebi

    Louisiana State University in Shreveport, United States

    10 publications · 429 citations

    10

    h-index

  2. DB
    Dibyendu Barman

    Jadavpur University, India

    2 publications · 23 citations

    2

    h-index

  3. LB
    Léon Bottou

    AT&T (United States), United States

    2 publications · 412 citations

    2

    h-index

  4. JV
    Juha Vesanto

    University of Helsinki, Finland

    2 publications · 973 citations

    2

    h-index

  5. PS
    Paul Scheunders

    University of Antwerp, Belgium

    2 publications · 345 citations

    2

    h-index

1–5 of 213

Venues

  1. The Imaging Science Journal

    3 publications · 85 citations

    3

    h-index

  2. IEEE Signal Processing Letters

    2 publications · 28 citations

    2

    h-index

  3. Zenodo (CERN European Organization for Nuclear Research)

    Repository · 2 publications · 32 citations

    2

    h-index

  4. Symmetry

    2 publications · 30 citations

    2

    h-index

  5. arXiv (Cornell University)

    Repository · 2 publications · 1.2K citations

    2

    h-index

1–5