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 assigns each new observation to the cluster whose centroid is nearest in the kernel-induced feature space, computed from the stored training data. The model is therefore a list containing the fitted kernlab::kkmeans() object along with the training data and per-cluster kernel statistics. The task must have at least 2 features.

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	1		\([1, \infty)\)
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.

• Package mlr3viz for some generic visualizations.

• 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.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 -> LearnerClustKKMeans

Active bindings

native_model

(any)
The fitted model.

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()
• LearnerClust$reset()

---

LearnerClustKKMeans$new()

Creates a new instance of this R6 class.

Usage

LearnerClustKKMeans$new()

---

LearnerClustKKMeans$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', predict_raw = 'FALSE'

# 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)
#> $model
#> Spectral Clustering object of class "specc" 
#> 
#>  Cluster memberships: 
#>  
#> 1 1 1 2 1 2 2 1 1 2 2 2 1 2 2 2 2 1 2 1 2 1 2 1 2 2 2 1 2 2 1 1 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 
#>  
#> Gaussian Radial Basis kernel function. 
#>  Hyperparameter : sigma =  0.000499499921223448 
#> 
#> Centers:  
#>          [,1]      [,2]     [,3]     [,4]
#> [1,] 272.5625 11.812500 28.37500 68.31250
#> [2,] 122.8529  5.894118 17.87059 64.23529
#> 
#> Cluster size:  
#> [1] 16 34
#> 
#> Within-cluster sum of squares:  
#> [1] 1445710.9  733848.1
#> 
#> 
#> $data
#>       Assault Murder Rape UrbanPop
#>  [1,]     236   13.2 21.2       58
#>  [2,]     263   10.0 44.5       48
#>  [3,]     294    8.1 31.0       80
#>  [4,]     190    8.8 19.5       50
#>  [5,]     276    9.0 40.6       91
#>  [6,]     204    7.9 38.7       78
#>  [7,]     110    3.3 11.1       77
#>  [8,]     238    5.9 15.8       72
#>  [9,]     335   15.4 31.9       80
#> [10,]     211   17.4 25.8       60
#> [11,]      46    5.3 20.2       83
#> [12,]     120    2.6 14.2       54
#> [13,]     249   10.4 24.0       83
#> [14,]     113    7.2 21.0       65
#> [15,]      56    2.2 11.3       57
#> [16,]     115    6.0 18.0       66
#> [17,]     109    9.7 16.3       52
#> [18,]     249   15.4 22.2       66
#> [19,]      83    2.1  7.8       51
#> [20,]     300   11.3 27.8       67
#> [21,]     149    4.4 16.3       85
#> [22,]     255   12.1 35.1       74
#> [23,]      72    2.7 14.9       66
#> [24,]     259   16.1 17.1       44
#> [25,]     178    9.0 28.2       70
#> [26,]     109    6.0 16.4       53
#> [27,]     102    4.3 16.5       62
#> [28,]     252   12.2 46.0       81
#> [29,]      57    2.1  9.5       56
#> [30,]     159    7.4 18.8       89
#> [31,]     285   11.4 32.1       70
#> [32,]     254   11.1 26.1       86
#> [33,]     337   13.0 16.1       45
#> [34,]      45    0.8  7.3       44
#> [35,]     120    7.3 21.4       75
#> [36,]     151    6.6 20.0       68
#> [37,]     159    4.9 29.3       67
#> [38,]     106    6.3 14.9       72
#> [39,]     174    3.4  8.3       87
#> [40,]     279   14.4 22.5       48
#> [41,]      86    3.8 12.8       45
#> [42,]     188   13.2 26.9       59
#> [43,]     201   12.7 25.5       80
#> [44,]     120    3.2 22.9       80
#> [45,]      48    2.2 11.2       32
#> [46,]     156    8.5 20.7       63
#> [47,]     145    4.0 26.2       73
#> [48,]      81    5.7  9.3       39
#> [49,]      53    2.6 10.8       66
#> [50,]     161    6.8 15.6       60
#> 
#> $clusters
#> [1] 1 2
#> 
#> $within
#> [1] 0.4682575 0.3366644
#> 

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

# Score the predictions
prediction$score(task = task)
#> clust.dunn 
#>  0.1532626