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 mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::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	1:9
modelNames	untyped	-
prior	untyped	-
control	untyped	mclust::emControl()
initialization	untyped	-
x	untyped	-

References

Scrucca, Luca, Fop, Michael, Murphy, Brendan T, Raftery, E A (2016). “mclust 5: clustering, classification and density estimation using Gaussian finite mixture models.” The R journal, 8(1), 289.

Fraley, Chris, Raftery, E A (2002). “Model-based clustering, discriminant analysis, and density estimation.” Journal of the American statistical Association, 97(458), 611–631.

See also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_clust.MBatchKMeans, mlr_learners_clust.SimpleKMeans, mlr_learners_clust.agnes, mlr_learners_clust.ap, mlr_learners_clust.bico, mlr_learners_clust.birch, mlr_learners_clust.cmeans, mlr_learners_clust.cobweb, mlr_learners_clust.dbscan, mlr_learners_clust.dbscan_fpc, mlr_learners_clust.diana, mlr_learners_clust.em, mlr_learners_clust.fanny, mlr_learners_clust.featureless, mlr_learners_clust.ff, mlr_learners_clust.hclust, mlr_learners_clust.hdbscan, mlr_learners_clust.kkmeans, mlr_learners_clust.kmeans, mlr_learners_clust.meanshift, mlr_learners_clust.optics, mlr_learners_clust.pam, mlr_learners_clust.xmeans

Super classes

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

Methods

Public methods

• LearnerClustMclust$new()

• LearnerClustMclust$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• 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"