A LearnerClust for k-means clustering implemented in stats::kmeans()
.
stats::kmeans()
doesn't have a default value for the number of clusters.
Therefore, the centers
parameter here is set to 2 by default.
The predict method uses clue::cl_predict()
to compute the
cluster memberships for new data.
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
mlr_learners$get("clust.kmeans") lrn("clust.kmeans")
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustKMeans
new()
Creates a new instance of this R6 class.
LearnerClustKMeans$new()
clone()
The objects of this class are cloneable with this method.
LearnerClustKMeans$clone(deep = FALSE)
deep
Whether to make a deep clone.
#> <LearnerClustKMeans:clust.kmeans> #> * Model: - #> * Parameters: centers=2 #> * Packages: stats, clue #> * Predict Type: partition #> * Feature types: logical, integer, numeric #> * Properties: complete, exclusive, partitional# available parameters: learner$param_set$ids()#> [1] "centers" "iter.max" "algorithm" "nstart" "trace"