A simple LearnerClust which assigns first n observations to cluster 1,
second n observations to cluster 2, and so on.
num_clusters controls the number of clusters and is
set to 1 by default.
The train method tries to assign cluster memberships to each
observation such that each cluster has an equal amount of observations.
The predict method uses does the same thing as the train but for new data.
Creates a new instance of this R6 class.
The objects of this class are cloneable with this method.
LearnerClustFeatureless$clone(deep = FALSE)
Whether to make a deep clone.
#> <LearnerClustKMeans:clust.kmeans> #> * Model: - #> * Parameters: centers=2 #> * Packages: stats, clue #> * Predict Type: partition #> * Feature types: logical, integer, numeric #> * Properties: complete, exclusive, partitional# available parameters: learner$param_set$ids()#>  "centers" "iter.max" "algorithm" "nstart" "trace"