K-d Trees

K-D Trees is a spatial partitioning data structure that recursively divides a multi-dimensional space into axis-aligned half-spaces using alternating coordinate axes to enable efficient range and nearest-neighbor queries.

146

Publications

23.6K

Citations

349

Authors

178

Institutions

Publications per year

2017–2026

28

Variants

K-d TreeK-dimensional TreeK-dimensional Trees

Authors

349

  1. SD
    Sanjoy Dasgupta

    University of California San Diego, United States

    4 publications · 584 citations

    4

    h-index

  2. JB
    Jon Bentley

    Carnegie Mellon University, United States

    3 publications · 8.2K citations

    3

    h-index

  3. JT
    John T. Robinson

    IBM Research - Thomas J. Watson Research Center, United States

    3 publications · 1.2K citations

    3

    h-index

  4. KS
    Kalpathi Subramanian

    University of North Carolina at Charlotte, United States

    3 publications · 432 citations

    3

    h-index

  5. DM
    Dmitriy Morozov

    Lawrence Berkeley National Laboratory, United States

    3 publications · 209 citations

    3

    h-index

1–5 of 349

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 212 citations

    6

    h-index

  2. Information Processing Letters

    6 publications · 91 citations

    6

    h-index

  3. SIAM Journal on Computing

    3 publications · 550 citations

    3

    h-index

  4. IEEE Transactions on Software Engineering

    TSE · 2 publications · 439 citations

    2

    h-index

  5. ACM Transactions on Database Systems

    TODS · 2 publications · 295 citations

    2

    h-index

1–5