mlr_learners_clust.dbscan | R Documentation |
A LearnerClust for density-based clustering implemented in dbscan::dbscan()
.
The predict method uses dbscan::predict.dbscan_fast()
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$get("clust.dbscan") lrn("clust.dbscan")
Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, dbscan
Id | Type | Default | Levels | Range |
eps | numeric | - | [0, \infty) |
|
minPts | integer | 5 | [0, \infty) |
|
borderPoints | logical | TRUE | TRUE, FALSE | - |
weights | untyped | - | - | |
search | character | kdtree | kdtree, linear, dist | - |
bucketSize | integer | 10 | [1, \infty) |
|
splitRule | character | SUGGEST | STD, MIDPT, FAIR, SL_MIDPT, SL_FAIR, SUGGEST | - |
approx | numeric | 0 | (-\infty, \infty) |
|
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustDBSCAN
new()
Creates a new instance of this R6 class.
LearnerClustDBSCAN$new()
clone()
The objects of this class are cloneable with this method.
LearnerClustDBSCAN$clone(deep = FALSE)
deep
Whether to make a deep clone.
if (requireNamespace("dbscan")) {
learner = mlr3::lrn("clust.dbscan")
print(learner)
# available parameters:
learner$param_set$ids()
}
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