Flexible Procedures for Clustering
Mahalanobis fixed point clusters initial configuration
Clustering by merging Gaussian mixture components
New parameters from merging two Gaussian mixture components
Asymmetric discriminant coordinates
Asymmetric neighborhood based discriminant coordinates
Asymmetric weighted discriminant coordinates
Minimum size of regression fixed point cluster
Bhattacharyya discriminant projection
Bhattacharyya distance between Gaussian distributions
Matrix of pairwise Bhattacharyya distances
Calinski-Harabasz index
Generation of the tuning constant for regression fixed point clusters
Recode nominal variables to binary variables
CDbw-index for cluster validation
Standardise cluster validation statistics by random clustering results
Classification of unclustered points
Sets of colours and symbols for cluster plotting
Jaccard similarity between logical vectors
Expected value of the number of times a fixed point cluster is found
Compute and format cluster validation statistics
Run many clustering methods on many numbers of clusters
Cluster validation statistics
Variablewise statistics for clusters
Run and validate many clusterings
Clusterwise cluster stability assessment by resampling
Generation of tuning constant for Mahalanobis fixed point clusters.
Connectivity components of an undirected graph
Misclassification probabilities in mixtures
Weighted Covariance Matrices (Maximum Likelihood)
Cluster validation statistics (version for use with clusterbenchstats
Cluster validation based on nearest neighbours
Weight function for AWC
DBSCAN density reachability and connectivity clustering
Density of multivariate Gaussian mixture, mclust parameterisation
Simulates p-value for dip test
Diptest for discriminant coordinate projection
Discriminant coordinates/canonical variates
Recodes mixed variables dataset
Linear dimension reduction for classification
Factor for dissimilarity of mixed type data
Distance based validity criteria for large data sets
Similarity of within-cluster distributions to normal and uniform
Density along the ridgeline
Duda-Hart test for splitting
Extract parameters for certain components from mclust
Finding representatives for cluster border
Mahalanobis Fixed Point Clusters
Linear Regression Fixed Point Clusters
Fitting mixed Gaussian/multinomial mixtures with flexmix
fpc package overview
Extracting clusters from fixed point cluster objects
Number of regression fixed point cluster iterations
Jitter variables in a data matrix
Interface functions for clustering methods
k-means with estimating k and initialisations
flexmix method for mixed Gaussian/multinomial mixtures
Local shape matrix
Mahalanobis for AWC
Mahalanobis distances from center of indexed points
Prediction strength of merged Gaussian mixture
Mean/variance differences discriminant coordinates
Neighborhood based discriminant coordinates
Neg-entropy normality index for cluster validation
Selection of the number of clusters via bootstrap
Partitioning around medoids with estimation of number of clusters
Ridgeline Pi-function
Extrema of two-component Gaussian mixture
Simulation-standardised plot and print of cluster validation statistic...
Discriminant projection plot.
Prediction strength for estimating number of clusters
Random partition matrix
Generate a sample indicator vector
Simulation of validity indexes based on random clusterings
Mixture Model ML for Clusterwise Linear Regression
"Face-shaped" clustered benchmark datasets
Ridgeline plots, ratios and unimodality
Ridgeline computation
Extracting intersections between clusters from fpc-object
Inversion of (possibly singular) symmetric matrices
Position in a similarity vector
Stupid average dissimilarity random clustering
Stupid k-centroids random clustering
Stupid farthest neighbour random clustering
Stupid nearest neighbour random clustering
Root of singularity-corrected eigenvalue decomposition
Tone perception data
Is a fitted denisity unimodal or not?
Cluster validation statistics - object
Ordered posterior plots
Weight function (for Mahalabobis distances)
Partition crosstable with empty clusters
Matrix of misclassification probabilities between mixture components
Various methods for clustering and cluster validation. Fixed point clustering. Linear regression clustering. Clustering by merging Gaussian mixture components. Symmetric and asymmetric discriminant projections for visualisation of the separation of groupings. Cluster validation statistics for distance based clustering including corrected Rand index. Standardisation of cluster validation statistics by random clusterings and comparison between many clustering methods and numbers of clusters based on this. Cluster-wise cluster stability assessment. Methods for estimation of the number of clusters: Calinski-Harabasz, Tibshirani and Walther's prediction strength, Fang and Wang's bootstrap stability. Gaussian/multinomial mixture fitting for mixed continuous/categorical variables. Variable-wise statistics for cluster interpretation. DBSCAN clustering. Interface functions for many clustering methods implemented in R, including estimating the number of clusters with kmeans, pam and clara. Modality diagnosis for Gaussian mixtures. For an overview see package?fpc.