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")

Super classes

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

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClustMiniBatchKMeans$new()


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

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