mclust (version 1.1-7)

mhtree.EEE: Classification tree for hierarchical clustering for Gaussian models with constant variance.

Description

Computes a classification tree for agglomerative hierarchical clustering using a Gaussian model in which all clusters have the same volume, shape, and orientation.

Usage

mhtree.EEE(data, partition, min.clusters = 1)

Arguments

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.

Value

• 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.

NOTES

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.

References

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).

See Also

mhtree, mhclass, awe, partuniq

Examples

data(iris)
mhtree.EEE(iris[,1:4])