Fuzzy C-Means Clustering Learner

A LearnerClust for fuzzy clustering implemented in e1071::cmeans(). e1071::cmeans() doesn't have a default value for the number of clusters. Therefore, the centers parameter here is set to 2 by default. The predict method uses clue::cl_predict() 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.cmeans")
lrn("clust.cmeans")

Meta Information

• Task type: “clust”

• Predict Types: “partition”, “prob”

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

• Required Packages: mlr3, mlr3cluster, e1071

Parameters

Id	Type	Default	Levels	Range
centers	untyped	2		-
iter.max	integer	100		\([1, \infty)\)
verbose	logical	FALSE	TRUE, FALSE	-
dist	character	euclidean	euclidean, manhattan	-
method	character	cmeans	cmeans, ufcl	-
m	numeric	2		\([1, \infty)\)
rate.par	numeric	-		\([0, 1]\)
weights	untyped	1		-
control	untyped	-		-

References

Dimitriadou, Evgenia, Hornik, Kurt, Leisch, Friedrich, Meyer, David, Weingessel, Andreas (2008). “Misc functions of the Department of Statistics (e1071), TU Wien.” R package, 1, 5--24.

Bezdek, C J (2013). Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media.

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.MBatchKMeans, mlr_learners_clust.SimpleKMeans, mlr_learners_clust.agnes, mlr_learners_clust.ap, 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

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustCMeans

Methods

Public methods

• LearnerClustCMeans$new()

• LearnerClustCMeans$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

LearnerClustCMeans$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustCMeans$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("e1071")) {
  learner = mlr3::lrn("clust.cmeans")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerClustCMeans:clust.cmeans>: Fuzzy C-Means Clustering Learner
#> * Model: -
#> * Parameters: centers=2
#> * Packages: mlr3, mlr3cluster, e1071
#> * Predict Types:  [partition], prob
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, fuzzy, partitional
#> [1] "centers"  "iter.max" "verbose"  "dist"     "method"   "m"        "rate.par"
#> [8] "weights"  "control"