K-Means Clustering Learner

A LearnerClust for k-means clustering implemented in stats::kmeans(). stats::kmeans() doesn't have a default value for the number of clusters. Therefore, the centers parameter here is set to 2 by default. The predict method uses clue::cl_predict() 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.kmeans")
lrn("clust.kmeans")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

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

• Required Packages: mlr3, mlr3cluster, 'stats', clue

Parameters

Id	Type	Default	Levels	Range
centers	untyped	2		-
iter.max	integer	10		\([1, \infty)\)
algorithm	character	Hartigan-Wong	Hartigan-Wong, Lloyd, Forgy, MacQueen	-
nstart	integer	1		\([1, \infty)\)
trace	integer	0		\([0, \infty)\)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustKMeans

Methods

Public methods

• LearnerClustKMeans$new()

• LearnerClustKMeans$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

LearnerClustKMeans$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustKMeans$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("stats") && requireNamespace("clue")) {
  learner = mlr3::lrn("clust.kmeans")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerClustKMeans:clust.kmeans>: K-Means
#> * Model: -
#> * Parameters: centers=2
#> * Packages: mlr3, mlr3cluster, stats, clue
#> * Predict Types:  [partition]
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, exclusive, partitional
#> [1] "centers"   "iter.max"  "algorithm" "nstart"    "trace"