varSelEM: A variable selection algorithm for clustering

In MBCbook: Companion Package for the Book "Model-Based Clustering and Classification for Data Science" by Bouveyron et al. (2019, ISBN:9781108644181).

Description

A variable selection algorithm for clustering which implements the method described in Law et al. (2004) <doi:10.1109/TPAMI.2004.71>.

Usage

varSelEM(X,G,maxit=100,eps=1e-6)

Arguments

X	a data frame containing the observations to cluster.
G	the expected number of groups (integer).
maxit	the maximum number of iterations (integer). The default value is 100.
eps	the convergence threshold. The default value is 1e-6.

Value

A list is returned with the following elements:

mu	the group means for relevant variables.
sigma	the group variances for relevant variables.
lambda	the group means for irrelevant variables
alpha	the group variances for irrelevant variables.
rho	the feature saliency.
P	the group posterior probabilities.
cls	the group memberships.
ll	the log-likelihood value.

Author(s)

C. Bouveyron

References

Law, M. H., Figueiredo, M. A. T., and Jain, A. K., Simultaneous feature selection and clustering using mixture models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, pp. 1154–1166, 2004 <doi:10.1109/TPAMI.2004.71>.

Examples

data(wine27)
X = scale(wine27[,1:27]) 
cls = wine27$Type

# Clustering and variable selection with VarSelEM
res = varSelEM(X,G=3)

# Clustering table
table(cls,res$cls)

MBCbook documentation built on July 2, 2019, 9:08 a.m.