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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("clust.cmeans")
mlr_learnerslrn("clust.cmeans")
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 | 2 | - | |
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 | 1 | - | |
control | untyped | - | - |
Super classes
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustCMeans
Examples
if (requireNamespace("e1071")) {
learner = mlr3::lrn("clust.cmeans")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerClustCMeans:clust.cmeans>: Fuzzy C-Means Clustering Learner
#> * Model: -
#> * Parameters: centers=2
#> * Packages: mlr3, mlr3cluster, e1071
#> * Predict Types: [partition], prob
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, fuzzy, partitional
#> [1] "centers" "iter.max" "verbose" "dist" "method" "m" "rate.par"
#> [8] "weights" "control"