ridgeline

alpha

Sigma1

Sigma2

<p>Computes (alpha*Sigma1^-1+(1-alpha)*Sigma2^-1)^-1* 
  alpha*(Sigma1^-1*mu1)+(1-alpha)*(Sigma2^-1*mu2)) as required for the
  computation of the ridgeline (Ray and Lindsay, 2005) to find
  all density extrema of a two-component Gaussian mixture with
  mean vectors mu1 and mu2 and covariance matrices Sigma1, Sigma2.</p>

multivariate

cluster

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.

ridgeline: Ridgeline computation

Description

Usage

Arguments

Value

References

Examples