Package index • fuzzyclara

Clustering functions

Functions to perform the actual cluster analysis

fuzzyclara(): Perform clustering

clustering_clara(): Perform CLARA clustering algorithm

clustering_clarans(): Perform CLARANS clustering

clustering_sample(): Perform clustering algorithm on a data sample

clustering_local(): Perform a local iteration of CLARANS clustering

Helper functions for cluster analysis

Functions to support the computation of the cluster analysis

assign_cluster(): Assign a cluster to each observation of the entire dataset

calculate_memb_score(): Calculate membership score of one observation for each medoid

compute_distance_matrix(): Compute the dissimilarity matrix for a data sample

perform_sample_clustering(): Perform pam or vegclust clustering on a data sample

print_logMessage(): Print diagnostic messages to console or log file

evaluate_cluster_numbers(): Visualization of the selection criterion for different cluster numbers

plot_cluster_numbers(): Function to provide graphical visualization for selecting the optimal number of clusters. The function provides graphical visualization showing the minimal (weighted) average distance for every cluster number.

Visualization functions for clustering results

Function to visualize clustering solutions in different ways

clara_boxplot(): Plot function boxplot

clara_barplot(): Plot function barplot

clara_pca(): Plot function PCA

clara_silhouette(): Plot function silhouette

clara_scatterplot(): Plot function scatterplot

clara_wordcloud(): Plot function wordcloud

clara_parallel(): Plot function parallel coordinate plot

plot(<fuzzyclara>): Visualization of clustering solution by variables

Analysis function of clustering results

Further functions to analyze obtained clustering solutions

print(<fuzzyclara>): Print output of "fuzzyclara" object

predict(<fuzzyclara>): Prediction of cluster assignments

Datasets

Internal datasets to showcase the package functionalities.

travel: Data from the German Reiseanalyse survey