X-means Clustering Learner

A LearnerClust for X-means clustering implemented in RWeka::XMeans(). 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.xmeans")
lrn("clust.xmeans")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

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

• Required Packages: mlr3, mlr3cluster, RWeka

Parameters

Id	Type	Default	Levels	Range
B	numeric	1		\([0, \infty)\)
C	numeric	0		\([0, \infty)\)
D	untyped	weka.core.EuclideanDistance		-
H	integer	4		\([1, \infty)\)
I	integer	1		\([1, \infty)\)
J	integer	1000		\([1, \infty)\)
K	untyped			-
L	integer	2		\([1, \infty)\)
M	integer	1000		\([1, \infty)\)
S	integer	10		\([1, \infty)\)
U	integer	0		\([0, \infty)\)
use_kdtree	logical	FALSE	TRUE, FALSE	-
N	untyped	-		-
O	untyped	-		-
Y	untyped	-		-
output_debug_info	logical	FALSE	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustXMeans

Methods

Public methods

• LearnerClustXMeans$new()

• LearnerClustXMeans$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

LearnerClustXMeans$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustXMeans$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("RWeka")) {
  learner = mlr3::lrn("clust.xmeans")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerClustXMeans:clust.xmeans>: X-means
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3cluster, RWeka
#> * Predict Types:  [partition]
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, exclusive, partitional
#>  [1] "B"                 "C"                 "D"                
#>  [4] "H"                 "I"                 "J"                
#>  [7] "K"                 "L"                 "M"                
#> [10] "S"                 "U"                 "use_kdtree"       
#> [13] "N"                 "O"                 "Y"                
#> [16] "output_debug_info"