clusexpect

positive integer. Total number of points.

positive integer. Number of independent variables.

positive integer smaller or equal to <code>n</code>.
 Size of the FPC.

positive integer. Number of fixed point iterations.

A rough approximation of the expectation of the number of times a well
 separated fixed point
 cluster (FPC) of size <code>n</code> is found in <code>ir</code> fixed point
 iterations of <code><a rd-options="" href="/link/fixreg?package=fpc&version=2.1-10" data-mini-rdoc="fpc::fixreg">fixreg</a></code>.

cluster

univar

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.
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.

Christian Hennig

Flexible Procedures for Clustering

clusexpect function

A rough approximation of the expectation of the number of times a well
 separated fixed point
 cluster (FPC) of size <code>n</code> is found in <code>ir</code> fixed point
 iterations of <code><a rd-options='' href='fixreg'>fixreg</a></code>.

clusexpect: Expected value of the number of times a fixed point cluster is found

Description

Usage

Arguments

Value

Details

References

See Also

Examples