.BG
.FN mhtreeEI
.TL
Classification tree for hierarchical clustering for Gaussian models with 
uniform diagonal variance.
.SH DESCRIPTION
Computes a classification tree for agglomerative hierarchical clustering using
a Gaussian model in which clusters are spherical and of equal volume (Wards' 
method).
.CS
mhtreeEI(data, partition, min.clusters = 1)
.PP
.RA
.AG data
matrix of observations.
.OA
.AG partition
initial classification of the data. The default puts every observation in a
singleton cluster.
.AG min.clusters
minimum number of clusters desired. The default is to carry out agglomerative
hierarchical clustering until termination, that is, until all observations
belong to a single group. The default value is 1.
.RT
an object of class `"mhtree"', which consists of a classification tree with
the following attributes:
.RC call
a copy of the call to `mhtree.EI'.
.RC change
value of the optimal change in likelihood at each stage.
.RC dimensions
the data dimensions.
.RC initial.partition
the partition at which agglomerative hierarchical clustering is initiated.
.SH REFERENCES
J. D. Banfield and A. E. Raftery, Model-based Gaussian and non-Gaussian
Clustering, \fIBiometrics, \fR49:803-821 (September 1993).

C. Fraley, Algorithms for Model-based Gaussian Hierarchical Clustering,
\fISIAM Journal on Scientific Computing, \fR20:270-281 (1998).

J. H. Ward, Hierarchical groupings to optimize and objective function,
\fIJournal of the American Statistical Association, \fR58:234-244 (1963).
.SA
`mhtree', `mhclass', `awe', `partuniq'
.EX
> data <- matrix(aperm(iris, c(1,3,2)), 150, 4)
> mhtreeEI(data)

.KW clustering
.WR

