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.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("clust.dbscan")
mlr_learnerslrn("clust.dbscan")
Meta Information
Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, dbscan
Parameters
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 | \((-\infty, \infty)\) |
Super classes
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustDBSCAN
Examples
if (requireNamespace("dbscan")) {
learner = mlr3::lrn("clust.dbscan")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerClustDBSCAN:clust.dbscan>: Density-Based Clustering
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3cluster, dbscan
#> * Predict Types: [partition]
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, exclusive, partitional
#> [1] "eps" "minPts" "borderPoints" "weights" "search"
#> [6] "bucketSize" "splitRule" "approx"