GMMClustering: Model-based clustering

In sharp: Stability-enHanced Approaches using Resampling Procedures

Model-based clustering

Description

Runs clustering with Gaussian Mixture Models (GMM) using implementation from Mclust. This function is not using stability.

Usage

GMMClustering(xdata, nc = NULL, ...)

Arguments

xdata	data matrix with observations as rows and variables as columns.
nc	matrix of parameters controlling the number of clusters in the underlying algorithm specified in implementation. If nc is not provided, it is set to seq(1, tau*nrow(xdata)).
...	additional parameters passed to Mclust.

Value

A list with:

comembership	an array of binary and symmetric co-membership matrices.
weights	a matrix of median weights by feature.

See Also

Other clustering algorithms: DBSCANClustering(), HierarchicalClustering(), KMeansClustering(), PAMClustering()

Examples

# Data simulation
set.seed(1)
simul <- SimulateClustering(n = c(10, 10), pk = 50)

# Clustering using Gaussian Mixture Models
mygmm <- GMMClustering(xdata = simul$data, nc = seq_len(30))

sharp documentation built on April 11, 2025, 5:44 p.m.