A LearnerClust for Simple K Means clustering implemented in RWeka::SimpleKMeans(). The predict method uses RWeka::predict.Weka_clusterer() 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.SimpleKMeans")
lrn("clust.SimpleKMeans")

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustSimpleKMeans

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClustSimpleKMeans$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustSimpleKMeans$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("clust.SimpleKMeans") print(learner)
#> <LearnerClustSimpleKMeans:clust.SimpleKMeans> #> * Model: - #> * Parameters: list() #> * Packages: RWeka #> * Predict Type: partition #> * Feature types: logical, integer, numeric #> * Properties: complete, exclusive, partitional
# available parameters: learner$param_set$ids()
#> [1] "A" "C" "fast" #> [4] "I" "init" "M" #> [7] "max_candidates" "min_density" "N" #> [10] "num_slots" "O" "periodic_pruning" #> [13] "S" "t2" "t1" #> [16] "V" "output_debug_info"