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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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.