Package 'fpc' - open source solution for an Internet free, intelligent ...

4 fpc-package
Cluster validity indexes and estimation of the number of clusters
cluster.stats This computes several cluster validity statistics from a clustering and a dissimilarity
matrix including the Calinski-Harabasz index, the adjusted Rand index and other statistics explained
in Gordon (1999) as well as several characterising measures such as average between
cluster and within cluster dissimilarity and separation. See also calinhara, dudahart2 for
specific indexes.
prediction.strength Estimates the number of clusters by computing the prediction strength of a
clustering of a dataset into different numbers of components for various clustering methods,
see Tibshirani and Walther (2005). In fact, this is more flexible than what is in the original
paper, because it can use point classification schemes that work better with clustering methods
other than k-means.
nselectboot Estimates the number of clusters by bootstrap stability selection, see Fang and Wang
(2012). This is quite flexible regarding clustering methods and point classification schemes
and also allows for dissimilarity data.
Cluster visualisation and validation
clucols Sets of colours and symbols useful for cluster plotting.
clusterboot Cluster-wise stability assessment of a clustering. Clusterings are performed on resampled
data to see for every cluster of the original dataset how well this is reproduced. See
Hennig (2007) for details.
cluster.varstats Extracts variable-wise information for every cluster in order to help with cluster
interpretation.
plotcluster Visualisation of a clustering or grouping in data by various linear projection methods
that optimise the separation between clusters, or between a single cluster and the rest of the
data according to Hennig (2004) including classical methods such as discriminant coordinates.
This calls the function discrproj, which is a bit more flexible but doesn’t produce a plot itself.
ridgeline.diagnosis Plots and diagnostics for assessing modality of Gaussian mixtures, see Ray
and Lindsay (2005).
weightplots Plots to diagnose component separation in Gaussian mixtures, see Hennig (2010).
localshape Local shape matrix, can be used for finding clusters in connection with function ics in
package ICS, see Hennig’s discussion and rejoinder of Tyler et al. (2009).
Useful wrapper functions for clustering methods
kmeansCBI This and other "CBI"-functions (see the kmeansCBI-help page) are unified wrappers
for various clustering methods in R that may be useful because they do in one step for what
you normally may need to do a bit more in R (for example fitting a Gaussian mixture with
noise component in package mclust).
kmeansruns This calls kmeans for the k-means clustering method and includes estimation of the
number of clusters and finding an optimal solution from several starting points.
pamk This calls pam and clara for the partitioning around medoids clustering method (Kaufman
and Rouseeuw, 1990) and includes two different ways of estimating the number of clusters.