von Mises-Fisher Mixture Clustering Learner

Fits a mixture of von Mises-Fisher distributions via EM, the directional-data analogue of a Gaussian mixture for points on the unit hypersphere. Calls movMF::movMF() from package movMF.

The k parameter is set to 2 by default since movMF::movMF() has no default value for the number of mixture components. Rows of x are standardised to unit length internally by movMF::movMF(). Predictions use the predict() method from movMF; prob returns the soft memberships.

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.movMF")
lrn("clust.movMF")

Meta Information

• Task type: “clust”

• Predict Types: “partition”, “prob”

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

• Required Packages: mlr3, mlr3cluster, movMF

Parameters

Id	Type	Default	Levels	Range
k	integer	-		\([1, \infty)\)
E	character	softmax	softmax, hardmax, stochmax	-
kappa	untyped	-		-
start	untyped	"p"		-
nruns	integer	1		\([1, \infty)\)
maxiter	integer	100		\([1, \infty)\)
reltol	numeric	-		\([0, \infty)\)
minalpha	numeric	0		\([0, \infty)\)
converge	logical	TRUE	TRUE, FALSE	-
verbose	logical	FALSE	TRUE, FALSE	-

References

Banerjee, Arindam, Dhillon, S I, Ghosh, Joydeep, Sra, Suvrit (2005). “Clustering on the Unit Hypersphere using von Mises-Fisher Distributions.” Journal of Machine Learning Research, 6(46), 1345–1382.

Hornik, Kurt, Grün, Bettina (2014). “movMF: An R Package for Fitting Mixtures of von Mises-Fisher Distributions.” Journal of Statistical Software, 58(10), 1–31. doi:10.18637/jss.v058.i10 .

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.optics, mlr_learners_clust.pam, mlr_learners_clust.protoclust, mlr_learners_clust.skmeans, mlr_learners_clust.som, mlr_learners_clust.specc, mlr_learners_clust.stdbscan, mlr_learners_clust.tclust, mlr_learners_clust.xmeans

Super classes

mlr3::Learner -> LearnerClust -> LearnerClustMovMF

Methods

Public methods

• LearnerClustMovMF$new()

• LearnerClustMovMF$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()

---

LearnerClustMovMF$new()

Creates a new instance of this R6 class.

Usage

LearnerClustMovMF$new()

---

LearnerClustMovMF$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustMovMF$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("clust.movMF")
print(learner)
#> 
#> ── <LearnerClustMovMF> (clust.movMF): von Mises-Fisher Mixture ─────────────────
#> • Model: -
#> • Parameters: k=2
#> • Packages: mlr3, mlr3cluster, and movMF
#> • Predict Types: [partition] and prob
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: complete, exclusive, fuzzy, and partitional
#> • Other settings: use_weights = 'error', predict_raw = 'FALSE'

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

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

# Print the model
print(learner$model)
#> theta:
#>     Assault    Murder     Rape  UrbanPop
#> 1  92.85884  4.184017 14.70392  62.06895
#> 2 418.97623 19.709966 48.58421 122.62699
#> alpha:
#> [1] 0.496325 0.503675
#> L:
#> [1] 311.7197

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

# Score the predictions
prediction$score(task = task)
#> clust.dunn 
#>  0.0404121