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

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Documentation for package ‘ClusterR’ version 1.3.3

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

AP_affinity_propagation	Affinity propagation clustering
AP_preferenceRange	Affinity propagation preference range
center_scale	Function to scale and/or center the data
Clara_Medoids	Clustering large applications
Cluster_Medoids	Partitioning around medoids
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)
distance_matrix	Distance matrix calculation
external_validation	external clustering validation
GMM	Gaussian Mixture Model clustering
KMeans_arma	k-means using the Armadillo library
KMeans_rcpp	k-means using RcppArmadillo
MiniBatchKmeans	Mini-batch-k-means using RcppArmadillo
mushroom	The mushroom data
Optimal_Clusters_GMM	Optimal number of Clusters for the gaussian mixture models
Optimal_Clusters_KMeans	Optimal number of Clusters for Kmeans or Mini-Batch-Kmeans
Optimal_Clusters_Medoids	Optimal number of Clusters for the partitioning around Medoids functions
plot_2d	2-dimensional plots
predict.GMMCluster	Prediction function for a Gaussian Mixture Model object
predict.KMeansCluster	Prediction function for the k-means
predict.MBatchKMeans	Prediction function for Mini-Batch-k-means
predict.MedoidsCluster	Predictions for the Medoid functions
predict_GMM	Prediction function for a Gaussian Mixture Model object
predict_KMeans	Prediction function for the k-means
predict_MBatchKMeans	Prediction function for Mini-Batch-k-means
predict_Medoids	Predictions for the Medoid functions
Silhouette_Dissimilarity_Plot	Plot of silhouette widths or dissimilarities
silhouette_of_clusters	Silhouette width based on pre-computed clusters
soybean	The soybean (large) data set from the UCI repository