gm_clust: Gaussian Mixture Models (GMM)

In tidyclust: A Common API to Clustering

Gaussian Mixture Models (GMM)

Description

gm_clust defines a model that fits clusters based on fitting a specified number of multivariate Gaussian distributions (MVG) to the data.

There are multiple implementations for this model, and the implementation is chosen by setting the model engine. The engine-specific pages for this model are listed below.

• mclust

Usage

gm_clust(
  mode = "partition",
  engine = "mclust",
  num_clusters = NULL,
  circular = TRUE,
  shared_size = TRUE,
  zero_covariance = TRUE,
  shared_orientation = TRUE,
  shared_shape = TRUE
)

Arguments

mode	A single character string for the type of model. The only possible value for this model is "partition".
engine	A single character string specifying what computational engine to use for fitting. The engine for this model is "mclust".
num_clusters	Positive integer, number of clusters in model (required).
circular	Boolean, whether or not to fit circular MVG distributions for each cluster. Default TRUE.
shared_size	Boolean, whether each cluster MVG should have the same size/volume. Default TRUE.
zero_covariance	Boolean, whether or not to assign covariances of 0 for each MVG. Default TRUE.
shared_orientation	Boolean, whether each cluster MVG should have the same orientation. Default TRUE.
shared_shape	Boolean, whether each cluster MVG should have the same shape. Default TRUE.

Details

What does it mean to predict?

To predict the cluster assignment for a new observation, we determine which cluster a point has the highest probability of belonging to.

Value

A gm_clust cluster specification.

Examples

# Show all engines
modelenv::get_from_env("gm_clust")

gm_clust()

tidyclust documentation built on June 20, 2026, 9:08 a.m.