% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/varclust.R
\docType{package}
\name{varclust}
\alias{varclust}
\alias{varclust-package}
\title{Variable Clustering with Multiple Latent Components Clustering algorithm}
\description{
Package varclust performs clustering of variables, according
to a probabilistic model, which assumes that each clusters lies in a low
dimensional subspace. Segmentation of variables, number of clusters and their
dimensions are selected based on the appropriate implementation of the 
Bayesian Information Criterion.
}
\details{
The best candidate models are identified by the specific 
implementation of K-means algorithm, in which cluster centers are represented
by some number of orthogonal factors and distance between a given variable
and a cluster center depends on residuals from a linear model fit. Based on
the Bayesian Information Criterion (BIC),  sums of squares of residuals are 
appropriately scaled, which allows to avoid an over-excessive attraction by
clusters with larger dimensions. To reduce the chance that the local minimum
of BIC is obtained instead of the global one, for every fixed number of 
clusters in a given range K-means algorithm is run large number of times,
with different random initializations of cluster centers.

The main function of package \pkg{varclust} is
\code{\link{mlcc.bic}} which allows clustering variables in a data 
with unknown number of clusters. Variable partition is computed
with k-means based algorithm. Number of clusters and their dimensions
are computed using BIC criterion.
If the number of clusters is known one might use function \code{\link{mlcc.reps}},
which takes number of clusters as a parameter. For \code{\link{mlcc.reps}} one might 
specify as well some initial segmentation for k-means algorithm. This can be useful if
user has some apriori knowledge about clustering.

We also provide function \code{\link{misclassification}} that computes misclassification
rate between two partitions. This performance measure is 
extensively used in image segmentation.

Version: 0.9.4
}
\examples{
\donttest{
sim.data <- data.simulation(n = 50, SNR = 1, K = 3, numb.vars = 50, max.dim = 3)
mlcc.bic(sim.data$X, numb.clusters = 1:5, numb.runs = 20, verbose = TRUE)
mlcc.reps(sim.data$X, numb.clusters = 3, numb.runs = 20)}
}
\author{
Piotr Sobczyk,
        Stanislaw Wilczynski,
        Julie Josse,
        Malgorzata Bogdan

Maintainer: Piotr Sobczyk \email{Piotr.Sobczyk@pwr.edu.pl}
}