Initial CRAN release.
Provides seven fast k-means clustering algorithms behind a single, uniform interface, wrapping high-performance C++ implementations via ‘Rcpp’ and ‘RcppEigen’:
geo_kmeans() — Geometric-k-means, the
bound-free method of Sharma et al.
ball_kmeans() — Ball k-means++.lloyd_kmeans(), elkan_kmeans(),
hamerly_kmeans(), annulus_kmeans(), and
exponion_kmeans().kmeans_dc() — dispatcher to select any of the above by
name.centers accepts either the number of clusters or a
matrix of initial centroids (mirroring stats::kmeans());
initialisation can be "random" or
"sequential".
Returns a geokmeans object with the final centroids,
per-point cluster assignments, iteration count, and number of distance
computations, along with a print() method.
Random initialisation uses R’s random number generator and is
reproducible via set.seed() or the optional
seed argument (default NULL).
Safeguards for degenerate input: an informative error when more
clusters are requested than there are distinct observations, and
optional removal of empty clusters via drop_empty.
Ships two example datasets in inst/extdata
(Breastcancer.csv and CreditRisk.csv) and a
“Getting started with geokmeans” vignette.