mlr3cluster (version 0.1.9)

mlr_learners_clust.MBatchKMeans: Mini Batch K-Means Clustering Learner

Description

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.

References

Sculley, David (2010). “Web-scale k-means clustering.” In Proceedings of the 19th international conference on World wide web, 1177--1178.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_clust.SimpleKMeans, mlr_learners_clust.agnes, mlr_learners_clust.ap, mlr_learners_clust.cmeans, mlr_learners_clust.cobweb, mlr_learners_clust.dbscan, mlr_learners_clust.dbscan_fpc, mlr_learners_clust.diana, mlr_learners_clust.em, mlr_learners_clust.fanny, mlr_learners_clust.featureless, mlr_learners_clust.ff, mlr_learners_clust.hclust, mlr_learners_clust.hdbscan, mlr_learners_clust.kkmeans, mlr_learners_clust.kmeans, mlr_learners_clust.mclust, mlr_learners_clust.meanshift, mlr_learners_clust.optics, mlr_learners_clust.pam, mlr_learners_clust.xmeans

Examples

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

  # available parameters:
  learner$param_set$ids()
}