Expectation-Maximization Clustering Learner

A LearnerClust for Expectation-Maximization clustering implemented in RWeka::list_Weka_interfaces(). 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.em")
lrn("clust.em")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

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

• Required Packages: mlr3, mlr3cluster, RWeka

Parameters

Id	Type	Default	Levels	Range
I	integer	100		\([1, \infty)\)
ll_cv	numeric	1e-06		\([1e-06, \infty)\)
ll_iter	numeric	1e-06		\([1e-06, \infty)\)
M	numeric	1e-06		\([1e-06, \infty)\)
max	integer	-1		\([-1, \infty)\)
N	integer	-1		\([-1, \infty)\)
num_slots	integer	1		\([1, \infty)\)
S	integer	100		\([0, \infty)\)
X	integer	10		\([1, \infty)\)
K	integer	10		\([1, \infty)\)
V	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-

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.

Dempster, P A, Laird, M N, Rubin, B D (1977). “Maximum likelihood from incomplete data via the EM algorithm.” Journal of the royal statistical society: series B (methodological), 39(1), 1–22.

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.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 -> LearnerClustEM

Methods

Public methods

• LearnerClustEM$new()

• LearnerClustEM$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

LearnerClustEM$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustEM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("clust.em")
print(learner)
#> 
#> ── <LearnerClustEM> (clust.em): Expectation-Maximization 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)
#> 
#> EM
#> ==
#> 
#> Number of clusters selected by cross validation: 2
#> Number of iterations performed: 12
#> 
#> 
#>              Cluster
#> Attribute          0        1
#>               (0.42)   (0.58)
#> ==============================
#> Assault
#>   mean       252.4139 110.5952
#>   std. dev.   43.9813  43.1685
#> 
#> Murder
#>   mean        11.8783   4.7742
#>   std. dev.    2.8171   2.2431
#> 
#> Rape
#>   mean        28.5285  15.8558
#>   std. dev.    8.3686   5.4395
#> 
#> UrbanPop
#>   mean         68.058  63.6846
#>   std. dev.     14.02  14.2715
#> 
#> 

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

# Score the predictions
prediction$score(task = task)
#> clust.dunn 
#>  0.1220028