ST-DBSCAN Clustering Learner

ST-DBSCAN (spatio-temporal density-based spatial clustering of applications with noise) clustering. Calls stdbscan::st_dbscan() from package stdbscan.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("clust.stdbscan")
lrn("clust.stdbscan")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3cluster, stdbscan

Parameters

Id	Type	Default	Levels	Range
eps_spatial	numeric	-		\([0, \infty)\)
eps_temporal	numeric	-		\([0, \infty)\)
min_pts	integer	-		\([1, \infty)\)
borderPoints	logical	TRUE	TRUE, FALSE	-
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		\((-\infty, \infty)\)

References

Birant, Derya, Kut, Alp (2007). “ST-DBSCAN: An algorithm for clustering spatial-temporal data.” Data & Knowledge Engineering, 60(1), 208–221. doi:10.1016/j.datak.2006.01.013 .

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.

• Dictionary of Learners: mlr3::mlr_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.bico, mlr_learners_clust.birch, mlr_learners_clust.clara, mlr_learners_clust.cmeans, mlr_learners_clust.cobweb, mlr_learners_clust.dbscan, mlr_learners_clust.dbscan_fpc, mlr_learners_clust.diana, mlr_learners_clust.em, mlr_learners_clust.fanny, mlr_learners_clust.featureless, mlr_learners_clust.ff, mlr_learners_clust.flexmix, mlr_learners_clust.genie, mlr_learners_clust.hclust, mlr_learners_clust.hdbscan, mlr_learners_clust.kcca, mlr_learners_clust.kkmeans, mlr_learners_clust.kmeans, mlr_learners_clust.kproto, mlr_learners_clust.mclust, mlr_learners_clust.meanshift, mlr_learners_clust.movMF, mlr_learners_clust.optics, mlr_learners_clust.pam, mlr_learners_clust.protoclust, mlr_learners_clust.skmeans, mlr_learners_clust.som, mlr_learners_clust.specc, mlr_learners_clust.tclust, mlr_learners_clust.xmeans

Super classes

mlr3::Learner -> LearnerClust -> LearnerClustSTDBSCAN

Methods

Public methods

• LearnerClustSTDBSCAN$new()

• LearnerClustSTDBSCAN$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• LearnerClust$reset()

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LearnerClustSTDBSCAN$new()

Creates a new instance of this R6 class.

Usage

LearnerClustSTDBSCAN$new()

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LearnerClustSTDBSCAN$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustSTDBSCAN$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("clust.stdbscan")
print(learner)
#> 
#> ── <LearnerClustSTDBSCAN> (clust.stdbscan): ST-DBSCAN ──────────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3cluster, and stdbscan
#> • Predict Types: [partition]
#> • Feature Types: integer and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: complete, density, and exclusive
#> • Other settings: use_weights = 'error', predict_raw = 'FALSE'