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tic codecov.io CRAN_Status_Badge Downloads Buy Me A Coffee Dependencies

ClusterR


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-posts (first and second), Vignette and in the package Documentation ( scroll down for information on how to use the docker image )

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 :


  • 1st. install the ClusterR package to take advantage of the new functionality either from CRAN using,

install.packages("ClusterR")

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


remotes::install_github('mlampros/ClusterR', upgrade = 'always', dependencies = TRUE, repos = 'https://cloud.r-project.org/')

  • 2nd. update the DESCRIPTION file of 'PackageA' and especially the LinkingTo field by adding the ClusterR package (besides any other packages),

LinkingTo: ClusterR

  • 3rd. open a new C++ file (for instance in Rstudio) and at the top of the file add the following 'headers', 'depends' and 'plugins',

# 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' 
             
set.seed(1)
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)
                         
str(mbkm)

Use the following link to report bugs/issues,

https://github.com/mlampros/ClusterR/issues


UPDATE 28-11-2019


Docker images of the ClusterR package are available to download from my dockerhub account. The images come with Rstudio and the R-development version (latest) installed. The whole process was tested on Ubuntu 18.04. To pull & run the image do the following,


docker pull mlampros/clusterr:rstudiodev

docker run -d --name rstudio_dev -e USER=rstudio -e PASSWORD=give_here_your_password --rm -p 8787:8787 mlampros/clusterr:rstudiodev

The user can also bind a home directory / folder to the image to use its files by specifying the -v command,


docker run -d --name rstudio_dev -e USER=rstudio -e PASSWORD=give_here_your_password --rm -p 8787:8787 -v /home/YOUR_DIR:/home/rstudio/YOUR_DIR mlampros/clusterr:rstudiodev

In the latter case you might have first give permission privileges for write access to YOUR_DIR directory (not necessarily) using,


chmod -R 777 /home/YOUR_DIR

The USER defaults to rstudio but you have to give your PASSWORD of preference (see https://rocker-project.org/ for more information).


Open your web-browser and depending where the docker image was build / run give,


1st. Option on your personal computer,


http://0.0.0.0:8787

2nd. Option on a cloud instance,


http://Public DNS:8787

to access the Rstudio console in order to give your username and password.


Citation:

If you use the code of this repository in your paper or research please cite both ClusterR and the original articles / software https://CRAN.R-project.org/package=ClusterR:


@Manual{,
  title = {{ClusterR}: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering},
  author = {Lampros Mouselimis},
  year = {2024},
  note = {R package version 1.3.3},
  url = {https://CRAN.R-project.org/package=ClusterR},
}