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ClusterR

The ClusterR package consists of Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids 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 package Vignette.

To install the package from CRAN use,


install.packages("ClusterR")

and to download the latest version from Github use the install_github function of the devtools package, 


devtools::install_github('mlampros/ClusterR')

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Version

Install

install.packages('ClusterR')

Monthly Downloads

3,933

Version

1.0.1

License

MIT + file LICENSE

Maintainer

Lampros Mouselimis

Last Published

September 9th, 2016

Functions in ClusterR (1.0.1)

distance_matrix

Distance matrix calculation
center_scale

Function to scale and/or center the data
KMeans_arma

k-means using the Armadillo library
GMM

Gaussian Mixture Model clustering
function_interactive

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

external clustering validation
entropy_formula

entropy formula (used in external_validation function)
Clara_Medoids

Clustering large applications
Cluster_Medoids

Partitioning around medoids
dietary_survey_IBS

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

Prediction function for the k-means
KMeans_rcpp

k-means using RcppArmadillo
predict_GMM

Prediction function for a Gaussian Mixture Model object
mushroom

The mushroom data
predict_MBatchKMeans

Prediction function for Mini-Batch-k-means
Optimal_Clusters_KMeans

Optimal number of Clusters for k-means
Optimal_Clusters_GMM

Optimal number of Clusters for the gaussian mixture models
MiniBatchKmeans

Mini-batch-k-means using RcppArmadillo
plot_2d

2-dimensional plots
Optimal_Clusters_Medoids

Optimal number of Clusters for the partitioning around Medoids functions
Silhouette_Dissimilarity_Plot

Plot of silhouette widths or dissimilarities
tryCatch_GMM

tryCatch function to prevent armadillo errors
soybean

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

Predictions for the Medoid functions