\HeaderA{MLIclust}{An interface to various unsupervised machine learning methods for ExpressionSets}{MLIclust}
\aliasA{agnesB}{MLIclust}{agnesB}
\aliasA{agnesB,ExpressionSet,numeric-method}{MLIclust}{agnesB,ExpressionSet,numeric.Rdash.method}
\aliasA{bclustB}{MLIclust}{bclustB}
\aliasA{bclustB,ExpressionSet,numeric-method}{MLIclust}{bclustB,ExpressionSet,numeric.Rdash.method}
\aliasA{claraB}{MLIclust}{claraB}
\aliasA{claraB,ExpressionSet,numeric-method}{MLIclust}{claraB,ExpressionSet,numeric.Rdash.method}
\aliasA{cmeansB}{MLIclust}{cmeansB}
\aliasA{cmeansB,ExpressionSet,numeric-method}{MLIclust}{cmeansB,ExpressionSet,numeric.Rdash.method}
\aliasA{cshellB}{MLIclust}{cshellB}
\aliasA{cshellB,ExpressionSet,numeric-method}{MLIclust}{cshellB,ExpressionSet,numeric.Rdash.method}
\aliasA{dianaB}{MLIclust}{dianaB}
\aliasA{dianaB,ExpressionSet,numeric-method}{MLIclust}{dianaB,ExpressionSet,numeric.Rdash.method}
\aliasA{distMat}{MLIclust}{distMat}
\aliasA{fannyB}{MLIclust}{fannyB}
\aliasA{fannyB,ExpressionSet,numeric-method}{MLIclust}{fannyB,ExpressionSet,numeric.Rdash.method}
\aliasA{hclustB}{MLIclust}{hclustB}
\aliasA{hclustB,ExpressionSet,missing,numeric-method}{MLIclust}{hclustB,ExpressionSet,missing,numeric.Rdash.method}
\aliasA{hclustB,ExpressionSet,numeric,ANY-method}{MLIclust}{hclustB,ExpressionSet,numeric,ANY.Rdash.method}
\aliasA{hclustB,ExpressionSet,numeric,missing-method}{MLIclust}{hclustB,ExpressionSet,numeric,missing.Rdash.method}
\aliasA{isoMDSB}{MLIclust}{isoMDSB}
\aliasA{kmeansB}{MLIclust}{kmeansB}
\aliasA{kmeansB,ExpressionSet,numeric-method}{MLIclust}{kmeansB,ExpressionSet,numeric.Rdash.method}
\aliasA{pamB}{MLIclust}{pamB}
\aliasA{pamB,ExpressionSet,numeric-method}{MLIclust}{pamB,ExpressionSet,numeric.Rdash.method}
\aliasA{silhouetteB}{MLIclust}{silhouetteB}
\aliasA{trainInds}{MLIclust}{trainInds}
\keyword{classif}{MLIclust}
\begin{Description}\relax
This document describes a family of wrappers to R functions
that perform clustering of multivariate datasets.
For example, the
\code{kmeansB} function is a wrapper for a call to \code{\LinkA{kmeans}{kmeans}} for objects
of class \code{ExpressionSet}. The interfaces of \code{MLInterfaces} provide a common calling
sequence and common return value for machine learning code.
\end{Description}
\begin{Usage}
\begin{verbatim}
kmeansB(exprObj, k , iter.max, metric = "euclidean") 
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{exprObj}] An instance of the \code{exprset} class. 
\item[\code{k}] The number of clusters
\item[\code{iter.max}] The maximum number of iterations to perform.
\item[\code{metric}] See \code{knn} for a complete description. 
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
See \code{\LinkA{kmeans}{kmeans}} for a complete description, and for other
functions named [f]B, see the documentation on \code{f}.
\end{Details}
\begin{Value}
An object of class \code{\LinkA{clustOutput-class}{clustOutput.Rdash.class}}.
\end{Value}
\begin{Author}\relax
Jess Mar, VJ Carey <stvjc@channing.harvard.edu>
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{ldaB}{ldaB}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
##get these from the source files
library(golubEsets)
data(Golub_Merge)
smallG <- Golub_Merge[1:60,]
set.seed(1234) # needed for nnet initialization
kmeansB( smallG, k=4 )
agnesB( smallG, k=5, height=0, FALSE )
dianaB( smallG, k=5, height=0, FALSE )
pamB( smallG, k=5, height=0, FALSE )
bclustB( smallG, k=5, height=0 )
cmeansB( smallG, k=5, height=0 )
cshellB( smallG, k=5, height=0 , iter.max=1)
#lcaB( BsmG, k=3 )
hclustB( smallG, k=4 )
hclustB( smallG, height=40000 )
\end{ExampleCode}
\end{Examples}

