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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

IdTypeDefaultLevelsRange
clustersinteger2\([1, \infty)\)
batch_sizeinteger10\([1, \infty)\)
num_initinteger1\([1, \infty)\)
max_itersinteger100\([1, \infty)\)
init_fractionnumeric1\([0, 1]\)
initializercharacterkmeans++optimal_init, quantile_init, kmeans++, random-
early_stop_iterinteger10\([1, \infty)\)
verboselogicalFALSETRUE, FALSE-
CENTROIDSuntyped-
tolnumeric1e-04\([0, \infty)\)
tol_optimal_initnumeric0.3\([0, \infty)\)
seedinteger1\((-\infty, \infty)\)

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.


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"