mlr_learners_clust.kmeans | R Documentation |
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$get("clust.kmeans") lrn("clust.kmeans")
Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, 'stats', clue
Id | Type | Default | Levels | Range |
centers | untyped | 2 | - | |
iter.max | integer | 10 | [1, \infty) |
|
algorithm | character | Hartigan-Wong | Hartigan-Wong, Lloyd, Forgy, MacQueen | - |
nstart | integer | 1 | [1, \infty) |
|
trace | integer | 0 | [0, \infty) |
|
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustKMeans
new()
Creates a new instance of this R6 class.
LearnerClustKMeans$new()
clone()
The objects of this class are cloneable with this method.
LearnerClustKMeans$clone(deep = FALSE)
deep
Whether to make a deep clone.
if (requireNamespace("stats") && requireNamespace("clue")) {
learner = mlr3::lrn("clust.kmeans")
print(learner)
# available parameters:
learner$param_set$ids()
}
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