mlr_learners_clust.dbscan_fpc {mlr3cluster} | R Documentation |
Density-based Spatial Clustering of Applications with Noise (DBSCAN) Clustering Learner
Description
DBSCAN (Density-based spatial clustering of applications with noise) clustering.
Calls fpc::dbscan()
from fpc.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
mlr_learners$get("clust.dbscan_fpc") lrn("clust.dbscan_fpc")
Meta Information
Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, fpc
Parameters
Id | Type | Default | Levels | Range |
eps | numeric | - | [0, \infty) |
|
MinPts | integer | 5 | [0, \infty) |
|
scale | logical | FALSE | TRUE, FALSE | - |
method | character | - | hybrid, raw, dist | - |
seeds | logical | TRUE | TRUE, FALSE | - |
showplot | untyped | FALSE | - | |
countmode | untyped | - | ||
Super classes
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustDBSCANfpc
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
LearnerClustDBSCANfpc$new()
Method clone()
The objects of this class are cloneable with this method.
Usage
LearnerClustDBSCANfpc$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
References
Ester, Martin, Kriegel, Hans-Peter, Sander, Jörg, Xu, Xiaowei, others (1996). “A density-based algorithm for discovering clusters in large spatial databases with noise.” In kdd, volume 96 number 34, 226–231.
See Also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners
Package mlr3extralearners for more learners.
-
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages). -
mlr3pipelines to combine learners with pre- and postprocessing steps.
Extension packages for additional task types:
-
mlr3proba for probabilistic supervised regression and survival analysis.
-
mlr3cluster for unsupervised clustering.
-
-
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Other Learner:
mlr_learners_clust.MBatchKMeans
,
mlr_learners_clust.SimpleKMeans
,
mlr_learners_clust.agnes
,
mlr_learners_clust.ap
,
mlr_learners_clust.cmeans
,
mlr_learners_clust.cobweb
,
mlr_learners_clust.dbscan
,
mlr_learners_clust.diana
,
mlr_learners_clust.em
,
mlr_learners_clust.fanny
,
mlr_learners_clust.featureless
,
mlr_learners_clust.ff
,
mlr_learners_clust.hclust
,
mlr_learners_clust.hdbscan
,
mlr_learners_clust.kkmeans
,
mlr_learners_clust.kmeans
,
mlr_learners_clust.mclust
,
mlr_learners_clust.meanshift
,
mlr_learners_clust.optics
,
mlr_learners_clust.pam
,
mlr_learners_clust.xmeans
Examples
if (requireNamespace("fpc")) {
learner = mlr3::lrn("clust.dbscan_fpc")
print(learner)
# available parameters:
learner$param_set$ids()
}