.BG
.FN mhtreeEEE
.TL
Classification tree for hierarchical clustering for Gaussian models with 
constant variance.
.SH DESCRIPTION
Computes a classification tree for agglomerative hierarchical clustering using
a Gaussian model in which all clusters have the same volume, shape, and 
orientation.
.CS
mhtreeEEE(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.EEE'.
.RC determinant
value related to the determinant of the sum of sample crossproduct matrices,
which is the relevant criterion for the model, computed during the course of 
hierarchical clustering.
.RC dimensions
the data dimensions.
.RC initial.partition
the partition at which agglomerative hierarchical clustering is initiated.
.RC trace
the trace of the sum of sample crossproduct matrices produced during the
course of hierarchical clustering.
.SH NOTES
The constant variance option is one of the slowest options for `mhtree',
because the model does not admit a fast hierarchical clustering algorithm.
At the same time, it is one of the more space-sefficient options.
.SH REFERENCES
J. D. Banfield and A. E. Raftery, Model-based Gaussian and non-Gaussian
Clustering, \fIBiometrics, \fR49:803-821 (1993).

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

H. P. Friedman and J. Rubin, On some invariant criteria for grouping data,
\fIJournal of the American Statistical Association, \fR62:1159-1178 (1967).
.SA
`mhtree', `mhclass', `awe', `partuniq'
.EX
> data <- matrix(aperm(iris, c(1,3,2)), 150, 4)
> mhtreeEEE(data)  

.KW clustering
.WR

