KMeans Cluster 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")

Super classes

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

Methods

Public methods

• LearnerClustKMeans$new()

• LearnerClustKMeans$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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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

learner = mlr3::lrn("clust.kmeans")
print(learner)
#> <LearnerClustKMeans:clust.kmeans>
#> * Model: -
#> * Parameters: centers=2
#> * Packages: stats, clue
#> * Predict Type: partition
#> * Feature types: logical, integer, numeric
#> * Properties: complete, exclusive, partitional

# available parameters:
learner$param_set$ids()
#> [1] "centers"   "iter.max"  "algorithm" "nstart"    "trace"