Gaussian Mixture Models-Based Clustering Learner

A LearnerClust for model-based clustering implemented in mclust::Mclust(). The predict method uses mclust::predict.Mclust() to compute the cluster memberships for new data.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("clust.mclust")
lrn("clust.mclust")

Meta Information

• Task type: “clust”

• Predict Types: “partition”, “prob”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3cluster, mclust

Parameters

Id	Type	Default
G	untyped	c , 1:9
modelNames	untyped	-
prior	untyped	-
control	untyped	mclust::emControl
initialization	untyped	-
x	untyped	-

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustMclust

Methods

Public methods

• LearnerClustMclust$new()

• LearnerClustMclust$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$train()
• mlr3cluster::LearnerClust$reset()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClustMclust$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustMclust$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("mclust")) {
  learner = mlr3::lrn("clust.mclust")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerClustMclust:clust.mclust>: Gaussian Mixture Models Clustering
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3cluster, mclust
#> * Predict Types:  [partition], prob
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, fuzzy, partitional
#> [1] "G"              "modelNames"     "prior"          "control"       
#> [5] "initialization" "x"