Fuzzy c-means clustering.
Calls e1071::cmeans() from package e1071.
The centers parameter is set to 2 by default since e1071::cmeans() doesn't have a default value for the number of
clusters. The predict method uses clue::cl_predict() 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():
Meta Information
Task type: “clust”
Predict Types: “partition”, “prob”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, e1071, clue
Parameters
| Id | Type | Default | Levels | Range |
| centers | untyped | - | - | |
| iter.max | integer | 100 | \([1, \infty)\) | |
| verbose | logical | FALSE | TRUE, FALSE | - |
| dist | character | euclidean | euclidean, manhattan | - |
| method | character | cmeans | cmeans, ufcl | - |
| m | numeric | 2 | \([1, \infty)\) | |
| rate.par | numeric | - | \([0, 1]\) | |
| weights | untyped | 1L | - | |
| control | untyped | - | - |
References
Dimitriadou, Evgenia, Hornik, Kurt, Leisch, Friedrich, Meyer, David, Weingessel, Andreas (2008). “Misc functions of the Department of Statistics (e1071), TU Wien.” R package, 1, 5–24.
Bezdek, C J (2013). Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media.
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.
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.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.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 -> LearnerClustCMeans
Examples
# Define the Learner and set parameter values
learner = lrn("clust.cmeans")
print(learner)
#>
#> ── <LearnerClustCMeans> (clust.cmeans): Fuzzy C-Means ──────────────────────────
#> • Model: -
#> • Parameters: centers=2
#> • Packages: mlr3, mlr3cluster, e1071, and clue
#> • Predict Types: [partition] and prob
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: complete, 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)
#> Fuzzy c-means clustering with 2 clusters
#>
#> Cluster centers:
#> Assault Murder Rape UrbanPop
#> 1 256.7300 11.647440 28.07171 68.53665
#> 2 105.4632 4.832808 15.99931 62.71935
#>
#> Memberships:
#> 1 2
#> [1,] 0.9667380980 0.033261902
#> [2,] 0.9724258595 0.027574140
#> [3,] 0.9590234854 0.040976515
#> [4,] 0.6006262497 0.399373750
#> [5,] 0.9670343776 0.032965622
#> [6,] 0.7774177857 0.222582214
#> [7,] 0.0112955002 0.988704500
#> [8,] 0.9699756568 0.030024343
#> [9,] 0.8945892347 0.105410765
#> [10,] 0.8380827337 0.161917266
#> [11,] 0.0814450853 0.918554915
#> [12,] 0.0151758412 0.984824159
#> [13,] 0.9865828320 0.013417168
#> [14,] 0.0044455530 0.995554447
#> [15,] 0.0579231799 0.942076820
#> [16,] 0.0052659093 0.994734091
#> [17,] 0.0067523701 0.993247630
#> [18,] 0.9945040869 0.005495913
#> [19,] 0.0226008290 0.977399171
#> [20,] 0.9530334541 0.046966546
#> [21,] 0.1654321787 0.834567821
#> [22,] 0.9964137511 0.003586249
#> [23,] 0.0319892774 0.968010723
#> [24,] 0.9698621859 0.030137814
#> [25,] 0.4689109665 0.531089034
#> [26,] 0.0048561862 0.995143814
#> [27,] 0.0005394172 0.999460583
#> [28,] 0.9785289659 0.021471034
#> [29,] 0.0569207290 0.943079271
#> [30,] 0.2617253617 0.738274638
#> [31,] 0.9755250008 0.024474999
#> [32,] 0.9862720430 0.013727957
#> [33,] 0.8831650439 0.116834956
#> [34,] 0.0818339193 0.918166081
#> [35,] 0.0206997695 0.979300230
#> [36,] 0.1583686472 0.841631353
#> [37,] 0.2417810352 0.758218965
#> [38,] 0.0038994967 0.996100503
#> [39,] 0.4116430350 0.588356965
#> [40,] 0.9695648410 0.030435159
#> [41,] 0.0229334592 0.977066541
#> [42,] 0.5928060923 0.407193908
#> [43,] 0.7469423869 0.253057613
#> [44,] 0.0287518441 0.971248156
#> [45,] 0.0862851590 0.913714841
#> [46,] 0.2018226269 0.798177373
#> [47,] 0.1236896318 0.876310368
#> [48,] 0.0361848698 0.963815130
#> [49,] 0.0625489696 0.937451030
#> [50,] 0.2474251023 0.752574898
#>
#> Closest hard clustering:
#> [1] 1 1 1 1 1 1 2 1 1 1 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
#> [39] 2 1 2 1 1 2 2 2 2 2 2 2
#>
#> Available components:
#> [1] "centers" "size" "cluster" "membership" "iter"
#> [6] "withinerror" "call"
# Make predictions for the task
prediction = learner$predict(task)
# Score the predictions
prediction$score(task = task)
#> clust.dunn
#> 0.1033191