A LearnerClust for fuzzy clustering implemented in cluster::fanny(). cluster::fanny() doesn't have a deafult value for the number of clusters. Therefore, the k parameter which correponds 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")

Super classes

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

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClustFanny$new()


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

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