A LearnerClust for Expectation-Maximization clustering implemented in
RWeka::list_Weka_interfaces()
.
The predict method uses RWeka::predict.Weka_clusterer()
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.em")
mlr_learnerslrn("clust.em")
Meta Information
Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, RWeka
Parameters
Id | Type | Default | Levels | Range |
I | integer | 100 | \([1, \infty)\) | |
ll_cv | numeric | 1e-06 | \([1e-06, \infty)\) | |
ll_iter | numeric | 1e-06 | \([1e-06, \infty)\) | |
M | numeric | 1e-06 | \([1e-06, \infty)\) | |
max | integer | -1 | \([-1, \infty)\) | |
N | integer | -1 | \([-1, \infty)\) | |
num_slots | integer | 1 | \([1, \infty)\) | |
S | integer | 100 | \([0, \infty)\) | |
X | integer | 10 | \([1, \infty)\) | |
K | integer | 10 | \([1, \infty)\) | |
V | logical | FALSE | TRUE, FALSE | - |
output_debug_info | logical | FALSE | TRUE, FALSE | - |
Super classes
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustEM
Examples
if (requireNamespace("RWeka")) {
learner = mlr3::lrn("clust.em")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerClustEM:clust.em>: Expectation-Maximization Clustering
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3cluster, RWeka
#> * Predict Types: [partition]
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, exclusive, partitional
#> [1] "I" "ll_cv" "ll_iter"
#> [4] "M" "max" "N"
#> [7] "num_slots" "S" "X"
#> [10] "K" "V" "output_debug_info"