Kernel K-Means Clustering Learner

Kernel k-means clustering. Calls kernlab::kkmeans() from package kernlab.

The centers parameter is set to 2 by default since kernlab::kkmeans() doesn't have a default value for the number of clusters. Kernel parameters have to be passed directly and not by using the kpar list in kernlab::kkmeans(). The predict method finds the nearest center in kernel distance to assign clusters for new data points.

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.kkmeans")
lrn("clust.kkmeans")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

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

• Required Packages: mlr3, mlr3cluster, kernlab

Parameters

Id	Type	Default	Levels	Range
centers	untyped	-		-
kernel	character	rbfdot	rbfdot, polydot, vanilladot, tanhdot, laplacedot, besseldot, anovadot, splinedot	-
sigma	numeric	-		\([0, \infty)\)
degree	integer	3		\([1, \infty)\)
scale	numeric	1		\([0, \infty)\)
offset	numeric	1		\((-\infty, \infty)\)
order	integer	1		\((-\infty, \infty)\)
alg	character	kkmeans	kkmeans, kerninghan	-
p	numeric	1		\((-\infty, \infty)\)

References

Karatzoglou, Alexandros, Smola, Alexandros, Hornik, Kurt, Zeileis, Achim (2004). “kernlab-an S4 package for kernel methods in R.” Journal of statistical software, 11, 1–20.

Dhillon, S I, Guan, Yuqiang, Kulis, Brian (2004). A unified view of kernel k-means, spectral clustering and graph cuts. Citeseer.

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.hclust, mlr_learners_clust.hdbscan, 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.specc, mlr_learners_clust.xmeans

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustKKMeans

Methods

Public methods

• LearnerClustKKMeans$new()

• LearnerClustKKMeans$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

LearnerClustKKMeans$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustKKMeans$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("clust.kkmeans")
print(learner)
#> 
#> ── <LearnerClustKKMeans> (clust.kkmeans): Kernel K-Means ───────────────────────
#> • Model: -
#> • Parameters: centers=2
#> • Packages: mlr3, mlr3cluster, and kernlab
#> • 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)
#> Using automatic sigma estimation (sigest) for RBF or laplace kernel 

# Print the model
print(learner$model)
#> Spectral Clustering object of class "specc" 
#> 
#>  Cluster memberships: 
#>  
#> 1 1 1 1 1 1 2 1 1 1 2 2 1 2 2 2 2 1 2 1 1 1 2 1 1 2 2 1 2 1 1 1 1 2 2 1 1 2 1 1 2 1 1 2 2 1 1 2 2 1 
#>  
#> Gaussian Radial Basis kernel function. 
#>  Hyperparameter : sigma =  0.00043279660093855 
#> 
#> Centers:  
#>          [,1]     [,2]     [,3]  [,4]
#> [1,] 226.2333 10.13333 25.79333 69.40
#> [2,]  87.5500  4.27000 14.39000 59.75
#> 
#> Cluster size:  
#> [1] 30 20
#> 
#> Within-cluster sum of squares:  
#> [1] 1799973.6  213075.9
#> 

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

# Score the predictions
prediction$score(task = task)
#> clust.dunn 
#> 0.04322918