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

## Documentation for package ‘ClusterR’ version 1.2.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## 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