A LearnerClust for fuzzy clustering implemented in e1071::cmeans().
e1071::cmeans() doesn't have a default value for the number of clusters.
Therefore, the centers parameter here is set to 2 by default.
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
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.
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.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.kkmeans,
mlr_learners_clust.kmeans,
mlr_learners_clust.mclust,
mlr_learners_clust.meanshift,
mlr_learners_clust.optics,
mlr_learners_clust.pam,
mlr_learners_clust.xmeans
Super classes
mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustCMeans
Examples
# Define the Learner and set parameter values
learner = lrn("clust.cmeans")
print(learner)
#>
#> ── <LearnerClustCMeans> (clust.cmeans): Fuzzy C-Means Clustering Learner ───────
#> • Model: -
#> • Parameters: centers=2
#> • Packages: mlr3, mlr3cluster, and e1071
#> • Predict Types: [partition] and prob
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: complete, fuzzy, and partitional
#> • Other settings: use_weights = 'error'
# 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
#> 1 256.7319 11.64749 28.07181 68.53662
#> 2 105.4648 4.83287 15.99945 62.71959
#>
#> Memberships:
#> 1 2
#> [1,] 0.9667330458 0.033266954
#> [2,] 0.9724263619 0.027573638
#> [3,] 0.9590263993 0.040973601
#> [4,] 0.6006053983 0.399394602
#> [5,] 0.9670360643 0.032963936
#> [6,] 0.7774011162 0.222598884
#> [7,] 0.0112943613 0.988705639
#> [8,] 0.9699710448 0.030028955
#> [9,] 0.8945923718 0.105407628
#> [10,] 0.8380682177 0.161931782
#> [11,] 0.0814470396 0.918552960
#> [12,] 0.0151734014 0.984826599
#> [13,] 0.9865811308 0.013418869
#> [14,] 0.0044441806 0.995555819
#> [15,] 0.0579256091 0.942074391
#> [16,] 0.0052642074 0.994735793
#> [17,] 0.0067519083 0.993248092
#> [18,] 0.9945025471 0.005497453
#> [19,] 0.0226027708 0.977397229
#> [20,] 0.9530366186 0.046963381
#> [21,] 0.1654190733 0.834580927
#> [22,] 0.9964134489 0.003586551
#> [23,] 0.0319914595 0.968008540
#> [24,] 0.9698619166 0.030138083
#> [25,] 0.4688884810 0.531111519
#> [26,] 0.0048557682 0.995144232
#> [27,] 0.0005398602 0.999460140
#> [28,] 0.9785279146 0.021472085
#> [29,] 0.0569231594 0.943076841
#> [30,] 0.2617085057 0.738291494
#> [31,] 0.9755277110 0.024472289
#> [32,] 0.9862712681 0.013728732
#> [33,] 0.8831680227 0.116831977
#> [34,] 0.0818362790 0.918163721
#> [35,] 0.0206965125 0.979303488
#> [36,] 0.1583547711 0.841645229
#> [37,] 0.2417636817 0.758236318
#> [38,] 0.0038991441 0.996100856
#> [39,] 0.4116229359 0.588377064
#> [40,] 0.9695669054 0.030433095
#> [41,] 0.0229352057 0.977064794
#> [42,] 0.5927842391 0.407215761
#> [43,] 0.7469240237 0.253075976
#> [44,] 0.0287483285 0.971251671
#> [45,] 0.0862873842 0.913712616
#> [46,] 0.2018067414 0.798193259
#> [47,] 0.1236780046 0.876321995
#> [48,] 0.0361867277 0.963813272
#> [49,] 0.0625513208 0.937448679
#> [50,] 0.2474076095 0.752592390
#>
#> 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