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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

IdTypeDefaultLevelsRange
centersuntyped2-
iter.maxinteger100\([1, \infty)\)
verboselogicalFALSETRUE, FALSE-
distcharactereuclideaneuclidean, manhattan-
methodcharactercmeanscmeans, ufcl-
mnumeric2\([1, \infty)\)
rate.parnumeric-\([0, 1]\)
weightsuntyped1-
controluntyped--

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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"