Cobweb Clustering Learner

A LearnerClust for Cobweb clustering implemented in RWeka::Cobweb(). The predict method uses RWeka::predict.Weka_clusterer() to compute the cluster memberships for new data.

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.cobweb")
lrn("clust.cobweb")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

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

• Required Packages: mlr3, mlr3cluster, RWeka

Parameters

Id	Type	Default	Range
A	numeric	1	\([0, \infty)\)
C	numeric	0.002	\([0, \infty)\)
S	integer	42	\([1, \infty)\)

References

Witten, H I, Frank, Eibe (2002). “Data mining: practical machine learning tools and techniques with Java implementations.” Acm Sigmod Record, 31(1), 76–77.

Fisher, H D (1987). “Knowledge acquisition via incremental conceptual clustering.” Machine learning, 2, 139–172.

Gennari, H J, Langley, Pat, Fisher, Doug (1989). “Models of incremental concept formation.” Artificial intelligence, 40(1-3), 11–61.

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.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.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

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustCobweb

Methods

Public methods

• LearnerClustCobweb$new()

• LearnerClustCobweb$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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClustCobweb$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustCobweb$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("clust.cobweb")
print(learner)
#> 
#> ── <LearnerClustCobweb> (clust.cobweb): Cobweb Clustering ──────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3cluster, and RWeka
#> • Predict Types: [partition]
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: complete, exclusive, and partitional
#> • 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)
#> Number of merges: 14
#> Number of splits: 10
#> Number of clusters: 65
#> 
#> node 0 [50]
#> |   node 1 [8]
#> |   |   leaf 2 [1]
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#> |   |   leaf 3 [1]
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#> |   node 1 [8]
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#> node 0 [50]
#> |   node 11 [25]
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#> node 0 [50]
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#> 
#> 

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

# Score the predictions
prediction$score(task = task)
#> clust.dunn 
#>          0