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


[Up] [Top]

Documentation for package ‘ClusterR’ version 1.2.4

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