CRAN/E | tglkmeans

tglkmeans

Efficient Implementation of K-Means++ Algorithm

Installation

About

Efficient implementation of K-Means++ algorithm. For more information see (1) "kmeans++ the advantages of the k-means++ algorithm" by David Arthur and Sergei Vassilvitskii (2007), Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, pp. 1027-1035, and (2) "The Effectiveness of Lloyd-Type Methods for the k-Means Problem" by Rafail Ostrovsky, Yuval Rabani, Leonard J. Schulman and Chaitanya Swamy doi:10.1145/2395116.2395117.

tanaylab.github.io/tglkmeans/
github.com/tanaylab/tglkmeans
System requirements GNU make
Bug report File report

Key Metrics

Version 0.5.4
R ≥ 4.0.0
Published 2024-01-09 117 days ago
Needs compilation? yes
License MIT
License File
CRAN checks tglkmeans results
OS unix

Downloads

Yesterday 3 0%
Last 7 days 41 -5%
Last 30 days 167 -15%
Last 90 days 505 -96%
Last 365 days 13.261 +805%

Maintainer

Maintainer

Aviezer Lifshitz

aviezer.lifshitz@weizmann.ac.il

Authors

Aviezer Lifshitz

aut / cre

Amos Tanay

aut

Weizmann Institute of Science

cph

Material

README
NEWS
Reference manual
Package source

Vignettes

usage

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Old Sources

tglkmeans archive

Depends

R ≥ 4.0.0

Imports

cli
doFuture
dplyr ≥ 0.5.0
future
ggplot2 ≥ 2.2.0
magrittr
Matrix
methods
parallel ≥ 3.3.2
plyr ≥1.8.4
purrr ≥ 0.2.0
Rcpp ≥ 0.12.11
RcppParallel
tgstat ≥ 1.0.0
tibble ≥ 3.1.2

Suggests

covr
knitr
rlang
rmarkdown
testthat
withr

LinkingTo

Rcpp
RcppParallel