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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},
}

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Install

install.packages('ClusterR')

Monthly Downloads

5,273

Version

1.3.3

License

GPL-3

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Last Published

June 18th, 2024

Functions in ClusterR (1.3.3)

Silhouette_Dissimilarity_Plot

Plot of silhouette widths or dissimilarities
function_interactive

Interactive function for consecutive plots ( using dissimilarities or the silhouette widths of the observations )
predict_Medoids

Predictions for the Medoid functions
silhouette_of_clusters

Silhouette width based on pre-computed clusters
distance_matrix

Distance matrix calculation
tryCatch_GMM

tryCatch function to prevent armadillo errors
mushroom

The mushroom data
soybean

The soybean (large) data set from the UCI repository
plot_2d

2-dimensional plots
predict_GMM

Prediction function for a Gaussian Mixture Model object
tryCatch_KMEANS_arma

tryCatch function to prevent armadillo errors in KMEANS_arma
tryCatch_optimal_clust_GMM

tryCatch function to prevent armadillo errors in GMM_arma_AIC_BIC
predict_MBatchKMeans

Prediction function for Mini-Batch-k-means
predict_KMeans

Prediction function for the k-means
AP_preferenceRange

Affinity propagation preference range
Clara_Medoids

Clustering large applications
Cluster_Medoids

Partitioning around medoids
Optimal_Clusters_GMM

Optimal number of Clusters for the gaussian mixture models
KMeans_arma

k-means using the Armadillo library
Optimal_Clusters_KMeans

Optimal number of Clusters for Kmeans or Mini-Batch-Kmeans
AP_affinity_propagation

Affinity propagation clustering
GMM

Gaussian Mixture Model clustering
KMeans_rcpp

k-means using RcppArmadillo
MiniBatchKmeans

Mini-batch-k-means using RcppArmadillo
center_scale

Function to scale and/or center the data
cost_clusters_from_dissim_medoids

Compute the cost and clusters based on an input dissimilarity matrix and medoids
dietary_survey_IBS

Synthetic data using a dietary survey of patients with irritable bowel syndrome (IBS)
Optimal_Clusters_Medoids

Optimal number of Clusters for the partitioning around Medoids functions
entropy_formula

entropy formula (used in external_validation function)
external_validation

external clustering validation