Fuzzy Analysis Cluster Learner

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

• LearnerClustFanny$new()

• LearnerClustFanny$clone()

Inherited methods

• 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

LearnerClustFanny$new()

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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"