Kernel K-Means Clustering Learner

A LearnerClust for kernel k-means clustering implemented in kernlab::kkmeans(). kernlab::kkmeans() doesn't have a default value for the number of clusters. Therefore, the centers parameter here is set to 2 by default. Kernel parameters have to be passed directly and not by using the kpar list in kkmeans. The predict method finds the nearest center in kernel distance to assign clusters for new data points.

Dictionary

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

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

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustKKMeans

Methods

Public methods

• LearnerClustKKMeans$new()

• LearnerClustKKMeans$clone()

Inherited methods

• 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

LearnerClustKKMeans$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustKKMeans$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

# available parameters:
learner$param_set$ids()
#> [1] "centers" "kernel"  "sigma"   "degree"  "scale"   "offset"  "order"  
#> [8] "alg"     "p"