% \VignetteIndexEntry{Bayesian Hierarchical Clustering}
\documentclass[a4paper]{article}
\title{Bayesian Hierarchical Clustering}
\author{Rich Savage}
\SweaveOpts{echo=true}
\usepackage{a4wide}
\begin{document}
\maketitle

This is a simple Sweave test of the Bayesian Hierarchical Clustering
method, as implemented in the R package \emph{BHC}.  It runs the
example code as given in the R help file and generates a resulting
dendrogram plot, to show the sort of results one can expect.

%%Code to generate the BHC dendrogram
<<reg>>=
require(BHC)
##BUILD SAMPLE DATA AND LABELS
data         <- matrix(0,15,10)
itemLabels   <- vector("character",15)
data[1:5,]   <- 1 ; itemLabels[1:5]   <- "a"
data[6:10,]  <- 2 ; itemLabels[6:10]  <- "b"
data[11:15,] <- 3 ; itemLabels[11:15] <- "c"
timePoints   <- 1:10 # for the time-course case

##DATA DIMENSIONS
nDataItems <- nrow(data)
nFeatures  <- ncol(data)

##RUN MULTINOMIAL CLUSTERING
hc1 <- bhc(data,itemLabels,verbose=TRUE)

##RUN TIME-COURSE CLUSTERING
hc2 <- bhc(data, itemLabels, 0, timePoints, "time-course",
          numReps=1, noiseMode=0, numThreads=1, verbose=TRUE)

##OUTPUT CLUSTER LABELS TO FILE
WriteOutClusterLabels(hc1, "labels.txt", verbose=TRUE)

##FOR THE MULTINOMIAL CASE, THE DATA CAN BE DISCRETISED
newData      <- data[] + rnorm(150, 0, 0.1);
percentiles  <- FindOptimalBinning(newData, itemLabels, transposeData=TRUE, verbose=TRUE)
discreteData <- DiscretiseData(t(newData), percentiles=percentiles)
discreteData <- t(discreteData)
hc3          <- bhc(discreteData, itemLabels, verbose=TRUE)

@ 
%% Embed the plot in the LaTex document
\begin{center}
<<fig=TRUE, echo=FALSE>>=
plot(hc1, axes=FALSE)
plot(hc2, axes=FALSE)
plot(hc3, axes=FALSE)
@ 
\end{center}


The plot shows a simple example dendrogram. (note that the structure
is quite distinctive; this may the be the result of discretising and
analysing a small data-set). 

\end{document}



