positive integer. Number of points.

positive integer. Number of independent variables.

Generates tuning constants <code>ca</code>
 for <code><a rd-options="" href="/link/fixreg?package=fpc&version=2.2-4" data-mini-rdoc="fpc::fixreg">fixreg</a></code> dependent on
 the number of points and variables of the dataset. 
Only thought for use in <code><a rd-options="" href="/link/fixreg?package=fpc&version=2.2-4" data-mini-rdoc="fpc::fixreg">fixreg</a></code>.

arith

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.

Christian Hennig

Flexible Procedures for Clustering

can function

Generates tuning constants <code>ca</code>
 for <code><a rd-options='' href='fixreg'>fixreg</a></code> dependent on
 the number of points and variables of the dataset. 
Only thought for use in <code><a rd-options='' href='fixreg'>fixreg</a></code>.

can: Generation of the tuning constant for regression fixed point clusters

Description

Usage

Arguments

Value

Details

References

See Also

Examples