tdecomp

<p>a symmetric matrix of minimum format 2*2.</p>

<p>Computes transposed eigenvectors of matrix <code>m</code> times diagonal of
  square root of eigenvalues so that eigenvalues smaller than 1e-6 are
  set to 1e-6.</p>

array

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.

tdecomp: Root of singularity-corrected eigenvalue decomposition

Description

Usage

Arguments

Value

Details

Examples