Hierarchical DBSCAN (HDBSCAN) Clustering Learner

HDBSCAN (Hierarchical DBSCAN) clustering. Calls dbscan::hdbscan() from dbscan.

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.hdbscan")
lrn("clust.hdbscan")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3cluster, dbscan

Parameters

Id	Type	Default	Levels	Range
minPts	integer	-		\([0, \infty)\)
cluster_selection_epsilon	numeric	0		\((-\infty, \infty)\)
gen_hdbscan_tree	logical	FALSE	TRUE, FALSE	-
gen_simplified_tree	logical	FALSE	TRUE, FALSE	-
verbose	logical	FALSE	TRUE, FALSE	-

References

Hahsler M, Piekenbrock M, Doran D (2019). “dbscan: Fast Density-Based Clustering with R.” Journal of Statistical Software, 91(1), 1–30. doi:10.18637/jss.v091.i01 .

Campello, JGB R, Moulavi, Davoud, Sander, Jörg (2013). “Density-based clustering based on hierarchical density estimates.” In Pacific-Asia conference on knowledge discovery and data mining, 160–172. Springer.

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.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.hclust, 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

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustHDBSCAN

Methods

Public methods

• LearnerClustHDBSCAN$new()

• LearnerClustHDBSCAN$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()
• mlr3cluster::LearnerClust$reset()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClustHDBSCAN$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustHDBSCAN$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("clust.hdbscan")
print(learner)
#> 
#> ── <LearnerClustHDBSCAN> (clust.hdbscan): HDBSCAN Clustering ───────────────────
#> • Model: -
#> • Parameters: minPts=5
#> • Packages: mlr3, mlr3cluster, and dbscan
#> • Predict Types: [partition]
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: complete, density, and exclusive
#> • Other settings: use_weights = 'error'

# Define a Task
task = tsk("usarrests")

# Train the learner on the task
learner$train(task)

# Print the model
print(learner$model)
#> HDBSCAN clustering for 50 objects.
#> Parameters: minPts = 5
#> The clustering contains 3 cluster(s) and 13 noise points.
#> 
#>  0  1  2  3 
#> 13 17 11  9 
#> 
#> Available fields: cluster, minPts, coredist, cluster_scores,
#>                   membership_prob, outlier_scores, hc, data

# Make predictions for the task
prediction = learner$predict(task)

# Score the predictions
prediction$score(task = task)
#> clust.dunn 
#>  0.2423918