mlr_learners_clust.SimpleKMeans | R Documentation |
A LearnerClust for Simple K Means clustering implemented in RWeka::SimpleKMeans()
.
The predict method uses RWeka::predict.Weka_clusterer()
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$get("clust.SimpleKMeans") lrn("clust.SimpleKMeans")
Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, RWeka
Id | Type | Default | Levels | Range |
A | untyped | weka.core.EuclideanDistance | - | |
C | logical | FALSE | TRUE, FALSE | - |
fast | logical | FALSE | TRUE, FALSE | - |
I | integer | 100 | [1, \infty) |
|
init | integer | 0 | [0, 3] |
|
M | logical | FALSE | TRUE, FALSE | - |
max_candidates | integer | 100 | [1, \infty) |
|
min_density | integer | 2 | [1, \infty) |
|
N | integer | 2 | [1, \infty) |
|
num_slots | integer | 1 | [1, \infty) |
|
O | logical | FALSE | TRUE, FALSE | - |
periodic_pruning | integer | 10000 | [1, \infty) |
|
S | integer | 10 | [0, \infty) |
|
t2 | numeric | -1 | (-\infty, \infty) |
|
t1 | numeric | -1.5 | (-\infty, \infty) |
|
V | logical | FALSE | TRUE, FALSE | - |
output_debug_info | logical | FALSE | TRUE, FALSE | - |
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustSimpleKMeans
new()
Creates a new instance of this R6 class.
LearnerClustSimpleKMeans$new()
clone()
The objects of this class are cloneable with this method.
LearnerClustSimpleKMeans$clone(deep = FALSE)
deep
Whether to make a deep clone.
## Not run:
if (requireNamespace("RWeka")) {
learner = mlr3::lrn("clust.SimpleKMeans")
print(learner)
# available parameters:
learner$param_set$ids()
}
## End(Not run)
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