A LearnerClust for kernel k-means clustering implemented in kernlab::kkmeans()
.
kernlab::kkmeans()
doesn't have a default value for the number of clusters.
Therefore, the centers
parameter here is set to 2 by default.
Kernel parameters have to be passed directly and not by using the kpar
list in kkmeans
.
The predict method finds the nearest center in kernel distance to
assign clusters for new data points.
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
mlr_learners$get("clust.kkmeans") lrn("clust.kkmeans")
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustKKMeans
new()
Creates a new instance of this R6 class.
LearnerClustKKMeans$new()
clone()
The objects of this class are cloneable with this method.
LearnerClustKKMeans$clone(deep = FALSE)
deep
Whether to make a deep clone.
#> <LearnerClustKKMeans:clust.kkmeans> #> * Model: - #> * Parameters: centers=2 #> * Packages: kernlab #> * Predict Type: partition #> * Feature types: logical, integer, numeric #> * Properties: complete, exclusive, partitional# available parameters: learner$param_set$ids()#> [1] "centers" "kernel" "sigma" "degree" "scale" "offset" "order" #> [8] "alg" "p"