mclust (version 1.1-7)

mhtree.EI: Classification tree for hierarchical clustering for Gaussian models with uniform diagonal variance.

Description

Computes a classification tree for agglomerative hierarchical clustering using a Gaussian model in which clusters are spherical and of equal volume (Wards' method).

Usage

mhtree.EI(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 "mhtree", which consists of a classification tree with the following attributes:
• call
  a copy of the call to mhtree.EI.
• change
  value of the optimal change in likelihood at each stage.
• dimensions
  the data dimensions.
• initial.partition
  the partition at which agglomerative hierarchical clustering is initiated.

References

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.

J. H. Ward, Hierarchical groupings to optimize and objective function, Journal of the American Statistical Association,58:234-244 (1963).

See Also

mhtree, mhclass, awe, partuniq

Examples

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