Finite Mixture Model Clustering Learner

Finite mixture model clustering via the EM algorithm. Calls flexmix::flexmix() from package flexmix.

The component model is selected through the model parameter, exposing the multivariate normal, univariate normal, multivariate binary, and multivariate Poisson drivers shipped with flexmix. The predict method calls flexmix::clusters() for cluster assignments and flexmix::posterior() for component probabilities on new data.

Note that EM can prune components whose prior falls below minprior during fitting, so the final number of components may be smaller than k.

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.flexmix")
lrn("clust.flexmix")

Meta Information

• Task type: “clust”

• Predict Types: “partition”, “prob”

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

• Required Packages: mlr3, mlr3cluster, flexmix

Parameters

Id	Type	Default	Levels	Range
k	integer	-		\([1, \infty)\)
model	character	FLXMCmvnorm	FLXMCmvnorm, FLXMCnorm1, FLXMCmvbinary, FLXMCmvpois	-
diagonal	logical	TRUE	TRUE, FALSE	-
truncated	logical	FALSE	TRUE, FALSE	-
cluster	untyped	-		-
iter.max	integer	200		\([1, \infty)\)
minprior	numeric	0.05		\([0, 1]\)
tolerance	numeric	1e-06		\([0, \infty)\)
verbose	integer	0		\([0, \infty)\)
classify	character	auto	auto, weighted, CEM, SEM, hard, random	-
nrep	integer	1		\([1, \infty)\)

References

Leisch, Friedrich (2004). “FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R.” Journal of Statistical Software, 11(8), 1–18. doi:10.18637/jss.v011.i08 .

Grün, Bettina, Leisch, Friedrich (2008). “FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters.” Journal of Statistical Software, 28(4), 1–35. doi:10.18637/jss.v028.i04 .

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.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.stdbscan, mlr_learners_clust.tclust, mlr_learners_clust.xmeans

Super classes

mlr3::Learner -> LearnerClust -> LearnerClustFlexmix

Methods

Public methods

• LearnerClustFlexmix$new()

• LearnerClustFlexmix$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()

---

LearnerClustFlexmix$new()

Creates a new instance of this R6 class.

Usage

LearnerClustFlexmix$new()

---

LearnerClustFlexmix$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustFlexmix$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("clust.flexmix")
print(learner)
#> 
#> ── <LearnerClustFlexmix> (clust.flexmix): Finite Mixture Model ─────────────────
#> • Model: -
#> • Parameters: k=2, model=FLXMCmvnorm
#> • Packages: mlr3, mlr3cluster, and flexmix
#> • 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)
#> 
#> Call:
#> flexmix::flexmix(formula = formula, data = data, k = 2L, model = driver, 
#>     control = control)
#> 
#> Cluster sizes:
#>  1  2 
#> 28 22 
#> 
#> convergence after 13 iterations

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

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