mhtree.EEE {mclust} | R Documentation |
Computes a classification tree for agglomerative hierarchical clustering using a Gaussian model in which all clusters have the same volume, shape, and orientation.
mhtree.EEE(data, partition, min.clusters = 1)
data |
matrix of observations. |
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. The default value is 1. |
an object of class "mhclust"
, which consists of a classification tree with
the following attributes:
call |
a copy of the call to mhclust.EEE .
|
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. |
dimensions |
the data dimensions. |
initial.partition |
the partition at which agglomerative hierarchical clustering is initiated. |
trace |
the trace of the sum of sample crossproduct matrices produced during the course of hierarchical clustering. |
The constant variance option is one of the slowest options for mhclust
,
because the model does not admit a fast hierarchical clustering algorithm.
At the same time, it is one of the more space-sefficient options.
J. D. Banfield and A. E. Raftery, Model-based Gaussian and non-Gaussian Clustering, Biometrics, 49:803-821 (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.
H. P. Friedman and J. Rubin, On some invariant criteria for grouping data, Journal of the American Statistical Association, 62:1159-1178 (1967).
mhtree
, mhclass
, awe
, partuniq
data(iris) mhtree.EEE(iris[,1:4])