The ClusterR package consists of Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering algorithms with the option to plot, validate, predict (new data) and find the optimal number of clusters. The package takes advantage of ‘RcppArmadillo’ to speed up the computationally intensive parts of the functions. More details on the functionality of ClusterR can be found in the blog-post, Vignette and in the package Documentation.

UPDATE 16-08-2018

As of version 1.1.4 the ClusterR package allows R package maintainers to perform linking between packages at a C++ code (Rcpp) level. This means that the Rcpp functions of the ClusterR package can be called in the C++ files of another package. In the next lines I’ll give detailed explanations on how this can be done:

Assumming that an R package (‘PackageA’) calls one of the ClusterR Rcpp functions. Then the maintainer of ‘PackageA’ has to :


or download the latest version from Github using the devtools package,


LinkingTo: ClusterR

# include <RcppArmadillo.h>
# include <ClusterRHeader.h>
# include <affinity_propagation.h>
// [[Rcpp::depends("RcppArmadillo")]]
// [[Rcpp::depends(ClusterR)]]
// [[Rcpp::plugins(cpp11)]]

The available functions can be found in the following files: inst/include/ClusterRHeader.h and inst/include/affinity_propagation.h

A complete minimal example would be :

# include <RcppArmadillo.h>
# include <ClusterRHeader.h>
# include <affinity_propagation.h>
// [[Rcpp::depends("RcppArmadillo")]]
// [[Rcpp::depends(ClusterR)]]
// [[Rcpp::plugins(cpp11)]]

using namespace clustR;

// [[Rcpp::export]]
Rcpp::List mini_batch_kmeans(arma::mat& data, int clusters, int batch_size, int max_iters, int num_init = 1, 

                            double init_fraction = 1.0, std::string initializer = "kmeans++",
                            int early_stop_iter = 10, bool verbose = false, 
                            Rcpp::Nullable<Rcpp::NumericMatrix> CENTROIDS = R_NilValue, 
                            double tol = 1e-4, double tol_optimal_init = 0.5, int seed = 1) {

  ClustHeader clust_header;

  return clust_header.mini_batch_kmeans(data, clusters, batch_size, max_iters, num_init, init_fraction, 
                                        initializer, early_stop_iter, verbose, CENTROIDS, tol, 
                                        tol_optimal_init, seed);

Then, by opening an R file a user can call the mini_batch_kmeans function using,

Rcpp::sourceCpp('example.cpp')              # assuming that the previous Rcpp code is included in 'example.cpp' 
dat = matrix(runif(100000), nrow = 1000, ncol = 100)

mbkm = mini_batch_kmeans(dat, clusters = 3, batch_size = 50, max_iters = 100, num_init = 2, 

                         init_fraction = 1.0, initializer = "kmeans++", early_stop_iter = 10, 
                         verbose = T, CENTROIDS = NULL, tol = 1e-4, tol_optimal_init = 0.5, seed = 1)

Use the following link to report bugs/issues,