Mini Batch K-Means Clustering Learner

A LearnerClust for mini batch k-means clustering implemented in ClusterR::MiniBatchKmeans(). ClusterR::MiniBatchKmeans() doesn't have a default value for the number of clusters. Therefore, the clusters parameter here is set to 2 by default. The predict method uses ClusterR::predict_MBatchKMeans() to compute the cluster memberships for new data. The learner supports both partitional and fuzzy clustering.

Dictionary

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

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

Meta Information

• Task type: “clust”

• Predict Types: “partition”, “prob”

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

• Required Packages: mlr3, mlr3cluster, ClusterR

Parameters

Id	Type	Default	Levels	Range
clusters	integer	2		\([1, \infty)\)
batch_size	integer	10		\([1, \infty)\)
num_init	integer	1		\([1, \infty)\)
max_iters	integer	100		\([1, \infty)\)
init_fraction	numeric	1		\([0, 1]\)
initializer	character	kmeans++	optimal_init, quantile_init, kmeans++, random	-
early_stop_iter	integer	10		\([1, \infty)\)
verbose	logical	FALSE	TRUE, FALSE	-
CENTROIDS	untyped			-
tol	numeric	1e-04		\([0, \infty)\)
tol_optimal_init	numeric	0.3		\([0, \infty)\)
seed	integer	1		\((-\infty, \infty)\)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustMiniBatchKMeans

Methods

Public methods

• LearnerClustMiniBatchKMeans$new()

• LearnerClustMiniBatchKMeans$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

LearnerClustMiniBatchKMeans$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustMiniBatchKMeans$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("ClusterR")) {
  learner = mlr3::lrn("clust.MBatchKMeans")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerClustMiniBatchKMeans:clust.MBatchKMeans>: Mini Batch K-Means
#> * Model: -
#> * Parameters: clusters=2
#> * Packages: mlr3, mlr3cluster, ClusterR
#> * Predict Types:  [partition], prob
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, exclusive, fuzzy, partitional
#>  [1] "clusters"         "batch_size"       "num_init"         "max_iters"       
#>  [5] "init_fraction"    "initializer"      "early_stop_iter"  "verbose"         
#>  [9] "CENTROIDS"        "tol"              "tol_optimal_init" "seed"