mhtree {mclust} | R Documentation |
Determines a classification tree for agglomerative hierarchical clustering using criteria based on parameterizations of Gaussian mixture models that reflect the underlying geometry of the resulting clusters.
mhtree(data, modelid, partition, min.clusters = 1, verbose = F, ...) print.mhtree(x, ...)
data |
matrix of observations. |
modelid |
An integer specifying a parameterization of the MVN covariance matrix defined
by volume, shape and orientation charactertistics of the underlying clusters.
The allowed values or model and their interpretation are as follows:
"EI" : uniform spherical, "VI" : spherical, "EEE" : uniform variance,
"VVV" : unconstrained variance, "EFV" : fixed (user supplied)
uniform volume, "VFV" : fixed (user supplied) shape.
|
partition |
initial classification of the data. The default puts every observation in a singleton cluster. |
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. |
verbose |
A logical variable specifying printing of the model type when set to T .
|
... |
Allows users to specify the required shape parameter for the two fixed
shape models "EFV" and "VFV" , and to change default parameters
that are used in the algorithms underlying some models. In the
print.mhtree function this argument is used for extra parameters to the print function.
|
x |
An mhtree object. |
an object of class "mhclust"
, which consists of a classification tree with
attributes giving other information relating to the clustering process.
Only the six models illustrated in the example below are supported at present.
These correspond to the models discussed in the Banfield and Raftery
reference.
It may be desirable to transform the data in some way before attempting to
partition it into clusters.
Different permuations of the data may produce different classifications,
because mhclust
resolves ties in a way that is dependent on the order of the
observations, and because values of criterion that are close may change enough
to affect the choice of merge pairs in a given stage.
J. D. Banfield and A. E. Raftery, Model-based Gaussian and non-Gaussian Clustering, Biometrics, 49:803-821 (September 1993).
C. Fraley, Algorithms for Model-based Gaussian Hierarchical Clustering, Technical Report No. 311, Department of Statistics, University of Washington (October 1996), to appear in SIAM Journal on Scientific Computing.
mhclass
, loglik
, awe
, partuniq
data(iris) # Ellipsoidal, equal volume, shape and orientation mhtree(iris[,1:4], modelid = "EEE") # Spherical, equal volume, fixed shape, variable orientation shape <- c(1,1/2,1/3,1/4) mhtree(iris[,1:4], modelid = "EFV", shape=shape) # Spherical, equal volume (Ward's method). mhtree(iris[,1:4], modelid = "EI") # Ellipsoidal, equal volume, constant shape, variable orientation mhtree(iris[,1:4], modelid = "VFV", shape=shape) # Spherical, variable volume mhtree(iris[,1:4], modelid = "VI") # Unconstrained (default). mhtree(iris[,1:4], modelid = "VVV")