mlr_learners_clust.MBatchKMeans | R Documentation |
A LearnerClust for mini batch k-means clustering implemented in ClusterR::MiniBatchKmeans()
.
ClusterR::MiniBatchKmeans()
doesn't have a default value for the number of clusters.
Therefore, the clusters
parameter here is set to 2 by default.
The predict method uses ClusterR::predict_MBatchKMeans()
to compute the
cluster memberships for new data.
The learner supports both partitional and fuzzy clustering.
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
mlr_learners$get("clust.MBatchKMeans") lrn("clust.MBatchKMeans")
Task type: “clust”
Predict Types: “partition”, “prob”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, ClusterR
Id | Type | Default | Levels | Range |
clusters | integer | 2 | [1, Inf) | |
batch_size | integer | 10 | [1, Inf) | |
num_init | integer | 1 | [1, Inf) | |
max_iters | integer | 100 | [1, Inf) | |
init_fraction | numeric | 1 | [0, 1] | |
initializer | character | kmeans++ | optimal_init, quantile_init, kmeans++, random | - |
early_stop_iter | integer | 10 | [1, Inf) | |
verbose | logical | FALSE | TRUE, FALSE | - |
CENTROIDS | untyped | - | ||
tol | numeric | 1e-04 | [0, Inf) | |
tol_optimal_init | numeric | 0.3 | [0, Inf) | |
seed | integer | 1 | (-Inf, Inf) | |
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustMiniBatchKMeans
new()
Creates a new instance of this R6 class.
LearnerClustMiniBatchKMeans$new()
clone()
The objects of this class are cloneable with this method.
LearnerClustMiniBatchKMeans$clone(deep = FALSE)
deep
Whether to make a deep clone.
if (requireNamespace("ClusterR")) { learner = mlr3::lrn("clust.MBatchKMeans") print(learner) # available parameters: learner$param_set$ids() }
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