ClustQual: Cluster Quality

In MGMM: Missingness-Aware Gaussian Mixture Models

Cluster Quality

Description

Evaluates cluster quality. Returns the following metrics:

• BIC: Bayesian Information Criterion; lower is better.

• CHI: Calinski-Harabasz index; higher is better.

• DBI: Davies-Bouldin index; lower is better.

• SIL: Mean silhouette width; higher is better.

Usage

ClustQual(fit)

Arguments

fit	Object of class mix.

Value

List containing the cluster quality metrics.

See Also

See ChooseK for using quality metrics to choose the cluster number.

Examples

set.seed(100)

# Data generation
mean_list = list(
c(2, 2, 2),
c(-2, 2, 2),
c(2, -2, 2),
c(2, 2, -2)
)

data <- rGMM(n = 500, d = 3, k = 4, means = mean_list)
fit <- FitGMM(data, k = 4)

# Clustering quality
cluster_qual <- ClustQual(fit)

MGMM documentation built on Feb. 27, 2026, 1:07 a.m.