Function reference • 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

Reference

All functions