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A LearnerClust for fuzzy clustering implemented in cluster::fanny(). cluster::fanny() doesn't have a default value for the number of clusters. Therefore, the k parameter which corresponds to the number of clusters here is set to 2 by default. The predict method copies cluster assignments and memberships generated for train data. The predict does not work 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.fanny")
lrn("clust.fanny")

Meta Information

  • Task type: “clust”

  • Predict Types: “partition”, “prob”

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

  • Required Packages: mlr3, mlr3cluster, cluster

Parameters

IdTypeDefaultLevelsRange
kinteger2\([1, \infty)\)
memb.expnumeric2\([1, \infty)\)
metriccharactereuclideaneuclidean, manhattan, SqEuclidean-
standlogicalFALSETRUE, FALSE-
maxitinteger500\([0, \infty)\)
tolnumeric1e-15\([0, \infty)\)
trace.levinteger0\([0, \infty)\)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustFanny

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

LearnerClustFanny$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("cluster")) {
  learner = mlr3::lrn("clust.fanny")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerClustFanny:clust.fanny>: Fuzzy Analysis Clustering
#> * Model: -
#> * Parameters: k=2
#> * Packages: mlr3, mlr3cluster, cluster
#> * Predict Types:  [partition], prob
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, fuzzy, partitional
#> [1] "k"         "memb.exp"  "metric"    "stand"     "maxit"     "tol"      
#> [7] "trace.lev"