Density-Based Clustering Learner with fpc
Source:R/LearnerClustDBSCANfpc.R
mlr_learners_clust.dbscan_fpc.Rd
A LearnerClust for density-based clustering implemented in fpc::dbscan()
.
The predict method uses fpc::predict.dbscan()
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_fpc")
mlr_learnerslrn("clust.dbscan_fpc")
Meta Information
Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, fpc
Parameters
Id | Type | Default | Levels | Range |
eps | numeric | - | \([0, \infty)\) | |
MinPts | integer | 5 | \([0, \infty)\) | |
scale | logical | FALSE | TRUE, FALSE | - |
method | character | - | hybrid, raw, dist | - |
seeds | logical | TRUE | TRUE, FALSE | - |
showplot | untyped | FALSE | - | |
countmode | untyped | - |
Super classes
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustDBSCANfpc
Examples
if (requireNamespace("fpc")) {
learner = mlr3::lrn("clust.dbscan_fpc")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerClustDBSCANfpc:clust.dbscan_fpc>: Density-Based Clustering with fpc
#> * Model: -
#> * Parameters: MinPts=5, scale=FALSE, seeds=TRUE, showplot=FALSE
#> * Packages: mlr3, mlr3cluster, fpc
#> * Predict Types: [partition]
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, exclusive, partitional
#> [1] "eps" "MinPts" "scale" "method" "seeds" "showplot"
#> [7] "countmode"