Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans and K-Medoids Clustering

Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions.

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 blog-post and in the package Vignette. ClusterR can be installed, currently, in the following OS's: Linux, Mac and Windows.

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')

Use the following link to report bugs/issues,

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

News

ClusterR 1.0.3

I updated the dissimilarity functions to accept data with missing values.
I added an error exception in the predict_GMM() function in case that the determinant is equal to zero. The latter is possible if the data includes highly correlated variables or variables with low variance.
I replaced all unsigned int's in the rcpp files with int data types