K Means Clustering Algorithm Learner from Weka

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

• LearnerClustSimpleKMeans$new()

• LearnerClustSimpleKMeans$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

LearnerClustSimpleKMeans$new()

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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"