Package index • ClusterR

All functions

AP_affinity_propagation(): Affinity propagation clustering

AP_preferenceRange(): Affinity propagation preference range

Clara_Medoids(): Clustering large applications

Cluster_Medoids(): Partitioning around medoids

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

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

Silhouette_Dissimilarity_Plot(): Plot of silhouette widths or dissimilarities

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)

distance_matrix(): Distance matrix calculation

external_validation(): external clustering validation

mushroom: The mushroom data

plot_2d(): 2-dimensional plots

predict_GMM() predict(<GMMCluster>): Prediction function for a Gaussian Mixture Model object

predict_KMeans() predict(<KMeansCluster>): Prediction function for the k-means

predict_MBatchKMeans() predict(<MBatchKMeans>): Prediction function for Mini-Batch-k-means

predict_Medoids() predict(<MedoidsCluster>): Predictions for the Medoid functions

silhouette_of_clusters(): Silhouette width based on pre-computed clusters

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