Expectation-Maximization Clustering Learner

A LearnerClust for Expectation-Maximization clustering implemented in RWeka::list_Weka_interfaces(). The predict method uses RWeka::predict.Weka_clusterer() 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.em")
lrn("clust.em")

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustEM

Methods

Public methods

• LearnerClustEM$new()

• LearnerClustEM$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

LearnerClustEM$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustEM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("clust.em")
print(learner)
#> <LearnerClustEM:clust.em>
#> * Model: -
#> * Parameters: list()
#> * Packages: RWeka
#> * Predict Type: partition
#> * Feature types: logical, integer, numeric
#> * Properties: complete, exclusive, partitional

# available parameters:
learner$param_set$ids()
#>  [1] "I"                 "ll_cv"             "ll_iter"          
#>  [4] "M"                 "max"               "N"                
#>  [7] "num_slots"         "S"                 "X"                
#> [10] "K"                 "V"                 "output_debug_info"