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Title: Cluster Extension for 'mlr3'
Version: 0.1.11
Description: Extends the 'mlr3' package with cluster analysis.
License: LGPL-3
URL: https://mlr3cluster.mlr-org.com, https://github.com/mlr-org/mlr3cluster
BugReports: https://github.com/mlr-org/mlr3cluster/issues
Depends: mlr3 (≥ 0.21.1), R (≥ 3.3.0)
Imports: backports (≥ 1.1.10), checkmate (≥ 2.0.0), clue, cluster, data.table (≥ 1.15.0), fpc, mlr3misc (≥ 0.15.0), paradox (≥ 1.0.1), R6, stats
Suggests: apcluster, ClusterR (≥ 1.3.1), dbscan, e1071, kernlab, LPCM, mclust, mlbench, RWeka, stream, testthat (≥ 3.0.0)
Config/testthat/edition: 3
Encoding: UTF-8
RoxygenNote: 7.3.2
Collate: 'LearnerClust.R' 'zzz.R' 'LearnerClustAffinityPropagation.R' 'LearnerClustAgnes.R' 'LearnerClustBICO.R' 'LearnerClustBIRCH.R' 'LearnerClustCMeans.R' 'LearnerClustCobweb.R' 'LearnerClustDBSCAN.R' 'LearnerClustDBSCANfpc.R' 'LearnerClustDiana.R' 'LearnerClustEM.R' 'LearnerClustFanny.R' 'LearnerClustFarthestFirst.R' 'LearnerClustFeatureless.R' 'LearnerClustHDBSCAN.R' 'LearnerClustHclust.R' 'LearnerClustKKMeans.R' 'LearnerClustKMeans.R' 'LearnerClustMclust.R' 'LearnerClustMeanShift.R' 'LearnerClustMiniBatchKMeans.R' 'LearnerClustOPTICS.R' 'LearnerClustPAM.R' 'LearnerClustSimpleKMeans.R' 'LearnerClustXMeans.R' 'MeasureClust.R' 'measures.R' 'MeasureClustInternal.R' 'PredictionClust.R' 'PredictionDataClust.R' 'TaskClust.R' 'TaskClust_ruspini.R' 'TaskClust_usarrest.R' 'as_prediction_clust.R' 'as_task_clust.R' 'bibentries.R' 'helper.R'
NeedsCompilation: no
Packaged: 2025-02-17 21:02:06 UTC; mmuecke
Author: Maximilian Mücke ORCID iD [aut, cre], Damir Pulatov [aut], Michel Lang ORCID iD [aut], Marc Becker ORCID iD [ctb]
Maintainer: Maximilian Mücke <muecke.maximilian@gmail.com>
Repository: CRAN
Date/Publication: 2025-02-19 00:00:02 UTC

mlr3cluster: Cluster Extension for 'mlr3'

Description

Extends the 'mlr3' package with cluster analysis.

Author(s)

Maintainer: Maximilian Mücke muecke.maximilian@gmail.com (ORCID)

Authors:

Other contributors:

See Also

Useful links:

Cluster Learner

Description

This Learner specializes mlr3::Learner for cluster problems:

Predefined learners can be found in the mlr3misc::Dictionary mlr3::mlr_learners.

Super class

mlr3::Learner -> LearnerClust

Public fields

assignments

(NULL | vector())
Cluster assignments from learned model.

save_assignments

(logical())
Should assignments for 'train' data be saved in the learner? Default is TRUE.

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClust$new(
  id,
  param_set = ps(),
  predict_types = "partition",
  feature_types = character(),
  properties = character(),
  packages = character(),
  label = NA_character_,
  man = NA_character_
)
Arguments
id

(character(1))
Identifier for the new instance.

param_set

(paradox::ParamSet)
Set of hyperparameters.

predict_types

(character())
Supported predict types. Must be a subset of mlr_reflections$learner_predict_types.

feature_types

(character())
Feature types the learner operates on. Must be a subset of mlr_reflections$task_feature_types.

properties

(character())
Set of properties of the mlr3::Learner. Must be a subset of mlr_reflections$learner_properties. The following properties are currently standardized and understood by learners in mlr3:

  • "missings": The learner can handle missing values in the data.

  • "weights": The learner supports observation weights.

  • "importance": The learner supports extraction of importance scores, i.e. comes with an ⁠$importance()⁠ extractor function (see section on optional extractors in mlr3::Learner).

  • "selected_features": The learner supports extraction of the set of selected features, i.e. comes with a ⁠$selected_features()⁠ extractor function (see section on optional extractors in mlr3::Learner).

  • "oob_error": The learner supports extraction of estimated out of bag error, i.e. comes with a oob_error() extractor function (see section on optional extractors in mlr3::Learner).

packages

(character())
Set of required packages. A warning is signaled by the constructor if at least one of the packages is not installed, but loaded (not attached) later on-demand via requireNamespace().

label

(character(1))
Label for the new instance.

man

(character(1))
String in the format ⁠[pkg]::[topic]⁠ pointing to a manual page for this object. The referenced help package can be opened via method ⁠$help()⁠.

Method reset()

Reset assignments field before calling parent's reset().

Usage
LearnerClust$reset()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClust$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

library(mlr3)
library(mlr3cluster)
ids = mlr_learners$keys("^clust")
ids

# get a specific learner from mlr_learners:
learner = lrn("clust.kmeans")
print(learner)

Cluster Measure

Description

This measure specializes mlr3::Measure for cluster analysis:

Predefined measures can be found in the mlr3misc::Dictionary mlr3::mlr_measures.

Super class

mlr3::Measure -> MeasureClust

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
MeasureClust$new(
  id,
  range,
  minimize = NA,
  aggregator = NULL,
  properties = character(),
  predict_type = "partition",
  task_properties = character(),
  packages = character(),
  label = NA_character_,
  man = NA_character_
)
Arguments
id

(character(1))
Identifier for the new instance.

range

(numeric(2))
Feasible range for this measure as c(lower_bound, upper_bound). Both bounds may be infinite.

minimize

(logical(1))
Set to TRUE if good predictions correspond to small values, and to FALSE if good predictions correspond to large values. If set to NA (default), tuning this measure is not possible.

aggregator

(⁠function(x)⁠)
Function to aggregate individual performance scores x where x is a numeric vector. If NULL, defaults to mean().

properties

(character())
Properties of the measure. Must be a subset of mlr_reflections$measure_properties. Supported by mlr3:

  • "requires_task" (requires the complete mlr3::Task),

  • "requires_learner" (requires the trained mlr3::Learner),

  • "requires_train_set" (requires the training indices from the mlr3::Resampling), and

  • "na_score" (the measure is expected to occasionally return NA or NaN).

predict_type

(character(1))
Required predict type of the mlr3::Learner. Possible values are stored in mlr_reflections$learner_predict_types.

task_properties

(character())
Required task properties, see mlr3::Task.

packages

(character())
Set of required packages. A warning is signaled by the constructor if at least one of the packages is not installed, but loaded (not attached) later on-demand via requireNamespace().

label

(character(1))
Label for the new instance.

man

(character(1))
String in the format ⁠[pkg]::[topic]⁠ pointing to a manual page for this object. The referenced help package can be opened via method ⁠$help()⁠.

See Also

Example cluster measures: clust.dunn

Prediction Object for Cluster Analysis

Description

This object wraps the predictions returned by a learner of class LearnerClust, i.e. the predicted partition and cluster probability.

Super class

mlr3::Prediction -> PredictionClust

Active bindings

partition

(integer())
Access the stored partition.

prob

(matrix())
Access to the stored probabilities.

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
PredictionClust$new(
  task = NULL,
  row_ids = task$row_ids,
  partition = NULL,
  prob = NULL,
  check = TRUE
)
Arguments
task

(TaskClust)
Task, used to extract defaults for row_ids.

row_ids

(integer())
Row ids of the predicted observations, i.e. the row ids of the test set.

partition

(integer())
Vector of cluster partitions.

prob

(matrix())
Numeric matrix of cluster membership probabilities with one column for each cluster and one row for each observation. Columns must be named with cluster numbers, row names are automatically removed. If prob is provided, but partition is not, the cluster memberships are calculated from the probabilities using max.col() with ties.method set to "first".

check

(logical(1))
If TRUE, performs some argument checks and predict type conversions.

Method clone()

The objects of this class are cloneable with this method.

Usage
PredictionClust$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

library(mlr3)
library(mlr3cluster)
task = tsk("usarrests")
learner = lrn("clust.kmeans")
p = learner$train(task)$predict(task)
p$predict_types
head(as.data.table(p))

Cluster Task

Description

This task specializes mlr3::Task for cluster problems. As an unsupervised task, this task has no target column. The task_type is set to "clust".

Predefined tasks are stored in the dictionary mlr3::mlr_tasks.

Super classes

mlr3::Task -> mlr3::TaskUnsupervised -> TaskClust

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
TaskClust$new(id, backend, label = NA_character_)
Arguments
id

(character(1))
Identifier for the new instance.

backend

(mlr3::DataBackend)
Either a mlr3::DataBackend, or any object which is convertible to a mlr3::DataBackend with as_data_backend(). E.g., a data.frame() will be converted to a mlr3::DataBackendDataTable.

label

(character(1))
Label for the new instance.

Method clone()

The objects of this class are cloneable with this method.

Usage
TaskClust$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

Other Task: mlr_tasks_ruspini, mlr_tasks_usarrests

Examples

library(mlr3)
library(mlr3cluster)
task = TaskClust$new("usarrests", backend = USArrests)
task$task_type

# possible properties:
mlr_reflections$task_properties$clust

Convert to a Cluster Prediction

Description

Convert object to a PredictionClust.

Usage

as_prediction_clust(x, ...)

## S3 method for class 'PredictionClust'
as_prediction_clust(x, ...)

## S3 method for class 'data.frame'
as_prediction_clust(x, ...)

Arguments

x

(any)
Object to convert.

...

(any)
Additional arguments.

Value

PredictionClust.

Examples

if (requireNamespace("e1071")) {
  # create a prediction object
  task = tsk("usarrests")
  learner = lrn("clust.kmeans")
  learner = lrn("clust.cmeans", predict_type = "prob")
  learner$train(task)
  p = learner$predict(task)

  # convert to a data.table
  tab = as.data.table(p)

  # convert back to a Prediction
  as_prediction_clust(tab)

  # split data.table into a 3 data.tables based on UrbanPop
  f = cut(task$data(rows = tab$row_ids)$UrbanPop, 3)
  tabs = split(tab, f)

  # convert back to list of predictions
  preds = lapply(tabs, as_prediction_clust)

  # calculate performance in each group
  sapply(preds, function(p) p$score(task = task))
}

Convert to a Cluster Task

Description

Convert object to a TaskClust. This is a S3 generic, specialized for at least the following objects:

  1. TaskClust: ensure the identity.

  2. data.frame() and mlr3::DataBackend: provides an alternative to calling constructor of TaskClust.

Usage

as_task_clust(x, ...)

## S3 method for class 'TaskClust'
as_task_clust(x, clone = FALSE, ...)

## S3 method for class 'data.frame'
as_task_clust(x, id = deparse1(substitute(x)), ...)

## S3 method for class 'DataBackend'
as_task_clust(x, id = deparse1(substitute(x)), ...)

## S3 method for class 'formula'
as_task_clust(x, data, id = deparse1(substitute(data)), ...)

Arguments

x

(any)
Object to convert.

...

(any)
Additional arguments.

clone

(logical(1))
If TRUE, ensures that the returned object is not the same as the input x.

id

(character(1))
Id for the new task. Defaults to the (deparsed and substituted) name of the data argument.

data

(data.frame())
Data frame containing all columns specified in formula x.

Value

TaskClust.

Examples

as_task_clust(datasets::USArrests)

Mini Batch K-Means Clustering Learner

Description

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.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.MBatchKMeans")
lrn("clust.MBatchKMeans")

Meta Information

Parameters

Id Type Default Levels Range
clusters integer 2 [1, \infty)
batch_size integer 10 [1, \infty)
num_init integer 1 [1, \infty)
max_iters integer 100 [1, \infty)
init_fraction numeric 1 [0, 1]
initializer character kmeans++ optimal_init, quantile_init, kmeans++, random -
early_stop_iter integer 10 [1, \infty)
verbose logical FALSE TRUE, FALSE -
CENTROIDS untyped NULL -
tol numeric 1e-04 [0, \infty)
tol_optimal_init numeric 0.3 [0, \infty)
seed integer 1 (-\infty, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustMiniBatchKMeans

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustMiniBatchKMeans$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustMiniBatchKMeans$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Sculley, David (2010). “Web-scale k-means clustering.” In Proceedings of the 19th international conference on World wide web, 1177–1178.

See Also

Other Learner: mlr_learners_clust.SimpleKMeans, mlr_learners_clust.agnes, mlr_learners_clust.ap, mlr_learners_clust.bico, mlr_learners_clust.birch, mlr_learners_clust.cmeans, 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

Examples

if (requireNamespace("ClusterR")) {
  learner = mlr3::lrn("clust.MBatchKMeans")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

K-Means Clustering Learner from Weka

Description

A LearnerClust for Simple K Means clustering implemented in RWeka::SimpleKMeans(). The predict method uses RWeka::predict.Weka_clusterer() 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(): 

mlr_learners$get("clust.SimpleKMeans")
lrn("clust.SimpleKMeans")

Meta Information

Parameters

Id Type Default Levels Range
A untyped "weka.core.EuclideanDistance" -
C logical FALSE TRUE, FALSE -
fast logical FALSE TRUE, FALSE -
I integer 100 [1, \infty)
init integer 0 [0, 3]
M logical FALSE TRUE, FALSE -
max_candidates integer 100 [1, \infty)
min_density integer 2 [1, \infty)
N integer 2 [1, \infty)
num_slots integer 1 [1, \infty)
O logical FALSE TRUE, FALSE -
periodic_pruning integer 10000 [1, \infty)
S integer 10 [0, \infty)
t2 numeric -1 (-\infty, \infty)
t1 numeric -1.5 (-\infty, \infty)
V logical FALSE TRUE, FALSE -
output_debug_info logical FALSE TRUE, FALSE -

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustSimpleKMeans

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustSimpleKMeans$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustSimpleKMeans$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Witten, H I, Frank, Eibe (2002). “Data mining: practical machine learning tools and techniques with Java implementations.” Acm Sigmod Record, 31(1), 76–77.

Forgy, W E (1965). “Cluster analysis of multivariate data: efficiency versus interpretability of classifications.” Biometrics, 21, 768–769.

Lloyd, P S (1982). “Least squares quantization in PCM.” IEEE Transactions on Information Theory, 28(2), 129–137.

MacQueen, James (1967). “Some methods for classification and analysis of multivariate observations.” In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, volume 1, 281–297.

See Also

Other Learner: mlr_learners_clust.MBatchKMeans, mlr_learners_clust.agnes, mlr_learners_clust.ap, mlr_learners_clust.bico, mlr_learners_clust.birch, mlr_learners_clust.cmeans, 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

Examples

if (requireNamespace("RWeka")) {
  learner = mlr3::lrn("clust.SimpleKMeans")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Agglomerative Hierarchical Clustering Learner

Description

A LearnerClust for agglomerative hierarchical clustering implemented in cluster::agnes(). The predict method uses stats::cutree() which cuts the tree resulting from hierarchical clustering into specified number of groups (see parameter k). The default number for k is 2.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.agnes")
lrn("clust.agnes")

Meta Information

Parameters

Id Type Default Levels Range
metric character euclidean euclidean, manhattan -
stand logical FALSE TRUE, FALSE -
method character average average, single, complete, ward, weighted, flexible, gaverage -
trace.lev integer 0 [0, \infty)
k integer 2 [1, \infty)
par.method untyped - -

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustAgnes

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustAgnes$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustAgnes$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Kaufman, Leonard, Rousseeuw, J P (2009). Finding groups in data: an introduction to cluster analysis. John Wiley & Sons.

See Also

Other Learner: mlr_learners_clust.MBatchKMeans, mlr_learners_clust.SimpleKMeans, mlr_learners_clust.ap, mlr_learners_clust.bico, mlr_learners_clust.birch, mlr_learners_clust.cmeans, 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

Examples

if (requireNamespace("cluster")) {
  learner = mlr3::lrn("clust.agnes")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Affinity Propagation Clustering Learner

Description

A LearnerClust for Affinity Propagation clustering implemented in apcluster::apcluster(). apcluster::apcluster() doesn't have set a default for similarity function. The predict method computes the closest cluster exemplar to find the cluster memberships for new data. The code is taken from StackOverflow answer by the apcluster package maintainer.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.ap")
lrn("clust.ap")

Meta Information

Parameters

Id Type Default Levels Range
s untyped - -
p untyped NA -
q numeric - [0, 1]
maxits integer 1000 [1, \infty)
convits integer 100 [1, \infty)
lam numeric 0.9 [0.5, 1]
includeSim logical FALSE TRUE, FALSE -
details logical FALSE TRUE, FALSE -
nonoise logical FALSE TRUE, FALSE -
seed integer - (-\infty, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustAP

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustAP$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustAP$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Bodenhofer, Ulrich, Kothmeier, Andreas, Hochreiter, Sepp (2011). “APCluster: an R package for affinity propagation clustering.” Bioinformatics, 27(17), 2463–2464.

Frey, J B, Dueck, Delbert (2007). “Clustering by passing messages between data points.” science, 315(5814), 972–976.

See Also

Other Learner: mlr_learners_clust.MBatchKMeans, mlr_learners_clust.SimpleKMeans, mlr_learners_clust.agnes, mlr_learners_clust.bico, mlr_learners_clust.birch, mlr_learners_clust.cmeans, 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

Examples

if (requireNamespace("apcluster")) {
  learner = mlr3::lrn("clust.ap")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

BICO Clustering Learner

Description

BICO (Fast computation of k-means coresets in a data stream) clustering. Calls stream::DSC_BICO() from stream.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.bico")
lrn("clust.bico")

Meta Information

Parameters

Id Type Default Range
k integer 5 [1, \infty)
space integer 10 [1, \infty)
p integer 10 [1, \infty)
iterations integer 10 [1, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustBICO

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustBICO$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustBICO$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Fichtenberger, Hendrik, Gille, Marc, Schmidt, Melanie, Schwiegelshohn, Chris, Sohler, Christian (2013). “BICO: BIRCH Meets Coresets for k-Means Clustering.” In Algorithms–ESA 2013: 21st Annual European Symposium, Sophia Antipolis, France, September 2-4, 2013. Proceedings 21, 481–492. Springer.

Hahsler M, Bolaños M, Forrest J (2017). “Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R.” Journal of Statistical Software, 76(14), 1–50. doi:10.18637/jss.v076.i14.

See Also

Other Learner: mlr_learners_clust.MBatchKMeans, mlr_learners_clust.SimpleKMeans, mlr_learners_clust.agnes, mlr_learners_clust.ap, mlr_learners_clust.birch, mlr_learners_clust.cmeans, 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

Examples

if (requireNamespace("stream")) {
  learner = mlr3::lrn("clust.bico")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

BIRCH Clustering Learner

Description

BIRCH (Balanced Iterative Reducing Clustering using Hierarchies) clustering. Calls stream::DSC_BIRCH() from stream.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.birch")
lrn("clust.birch")

Meta Information

Parameters

Id Type Default Range
threshold numeric - [0, \infty)
branching integer - [1, \infty)
maxLeaf integer - [1, \infty)
maxMem integer 0 [0, \infty)
outlierThreshold numeric 0.25 (-\infty, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustBIRCH

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustBIRCH$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustBIRCH$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Zhang, Tian, Ramakrishnan, Raghu, Livny, Miron (1996). “BIRCH: An Efficient Data Clustering Method for Very Large Databases.” ACM sigmod record, 25(2), 103–114.

Zhang, Tian, Ramakrishnan, Raghu, Livny, Miron (1997). “BIRCH: A new data clustering algorithm and its applications.” Data Mining and Knowledge Discovery, 1, 141–182.

Hahsler M, Bolaños M, Forrest J (2017). “Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R.” Journal of Statistical Software, 76(14), 1–50. doi:10.18637/jss.v076.i14.

See Also

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.cmeans, 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

Examples

if (requireNamespace("stream")) {
  learner = mlr3::lrn("clust.birch")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Fuzzy C-Means Clustering Learner

Description

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(): 

mlr_learners$get("clust.cmeans")
lrn("clust.cmeans")

Meta Information

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 - -

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustCMeans

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustCMeans$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustCMeans$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

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

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

Examples

if (requireNamespace("e1071")) {
  learner = mlr3::lrn("clust.cmeans")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Cobweb Clustering Learner

Description

A LearnerClust for Cobweb clustering implemented in RWeka::Cobweb(). The predict method uses RWeka::predict.Weka_clusterer() 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(): 

mlr_learners$get("clust.cobweb")
lrn("clust.cobweb")

Meta Information

Parameters

Id Type Default Range
A numeric 1 [0, \infty)
C numeric 0.002 [0, \infty)
S integer 42 [1, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustCobweb

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustCobweb$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustCobweb$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Witten, H I, Frank, Eibe (2002). “Data mining: practical machine learning tools and techniques with Java implementations.” Acm Sigmod Record, 31(1), 76–77.

Fisher, H D (1987). “Knowledge acquisition via incremental conceptual clustering.” Machine learning, 2, 139–172.

Gennari, H J, Langley, Pat, Fisher, Doug (1989). “Models of incremental concept formation.” Artificial intelligence, 40(1-3), 11–61.

See Also

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.cmeans, 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

Examples

if (requireNamespace("RWeka")) {
  learner = mlr3::lrn("clust.cobweb")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Density-based Spatial Clustering of Applications with Noise (DBSCAN) Clustering Learner

Description

DBSCAN (Density-based spatial clustering of applications with noise) clustering. Calls dbscan::dbscan() from dbscan.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.dbscan")
lrn("clust.dbscan")

Meta Information

Parameters

Id Type Default Levels Range
eps numeric - [0, \infty)
minPts integer 5 [0, \infty)
borderPoints logical TRUE TRUE, FALSE -
weights untyped - -
search character kdtree kdtree, linear, dist -
bucketSize integer 10 [1, \infty)
splitRule character SUGGEST STD, MIDPT, FAIR, SL_MIDPT, SL_FAIR, SUGGEST -
approx numeric 0 (-\infty, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustDBSCAN

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustDBSCAN$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustDBSCAN$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Hahsler M, Piekenbrock M, Doran D (2019). “dbscan: Fast Density-Based Clustering with R.” Journal of Statistical Software, 91(1), 1–30. doi:10.18637/jss.v091.i01.

Ester, Martin, Kriegel, Hans-Peter, Sander, Jörg, Xu, Xiaowei, others (1996). “A density-based algorithm for discovering clusters in large spatial databases with noise.” In kdd, volume 96 number 34, 226–231.

See Also

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.cmeans, mlr_learners_clust.cobweb, 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

Examples

if (requireNamespace("dbscan")) {
  learner = mlr3::lrn("clust.dbscan")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Density-based Spatial Clustering of Applications with Noise (DBSCAN) Clustering Learner

Description

DBSCAN (Density-based spatial clustering of applications with noise) clustering. Calls fpc::dbscan() from fpc.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.dbscan_fpc")
lrn("clust.dbscan_fpc")

Meta Information

Parameters

Id Type Default Levels Range
eps numeric - [0, \infty)
MinPts integer 5 [0, \infty)
scale logical FALSE TRUE, FALSE -
method character - hybrid, raw, dist -
seeds logical TRUE TRUE, FALSE -
showplot untyped FALSE -
countmode untyped NULL -

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustDBSCANfpc

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustDBSCANfpc$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustDBSCANfpc$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Ester, Martin, Kriegel, Hans-Peter, Sander, Jörg, Xu, Xiaowei, others (1996). “A density-based algorithm for discovering clusters in large spatial databases with noise.” In kdd, volume 96 number 34, 226–231.

See Also

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.cmeans, mlr_learners_clust.cobweb, mlr_learners_clust.dbscan, 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

Examples

if (requireNamespace("fpc")) {
  learner = mlr3::lrn("clust.dbscan_fpc")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Divisive Hierarchical Clustering Learner

Description

A LearnerClust for divisive hierarchical clustering implemented in cluster::diana(). The predict method uses stats::cutree() which cuts the tree resulting from hierarchical clustering into specified number of groups (see parameter k). The default value for k is 2.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.diana")
lrn("clust.diana")

Meta Information

Parameters

Id Type Default Levels Range
metric character euclidean euclidean, manhattan -
stand logical FALSE TRUE, FALSE -
trace.lev integer 0 [0, \infty)
k integer 2 [1, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustDiana

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustDiana$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustDiana$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Kaufman, Leonard, Rousseeuw, J P (2009). Finding groups in data: an introduction to cluster analysis. John Wiley & Sons.

See Also

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.cmeans, mlr_learners_clust.cobweb, mlr_learners_clust.dbscan, mlr_learners_clust.dbscan_fpc, 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

Examples

if (requireNamespace("cluster")) {
  learner = mlr3::lrn("clust.diana")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Expectation-Maximization Clustering Learner

Description

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 mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.em")
lrn("clust.em")

Meta Information

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

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustEM$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustEM$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Witten, H I, Frank, Eibe (2002). “Data mining: practical machine learning tools and techniques with Java implementations.” Acm Sigmod Record, 31(1), 76–77.

Dempster, P A, Laird, M N, Rubin, B D (1977). “Maximum likelihood from incomplete data via the EM algorithm.” Journal of the royal statistical society: series B (methodological), 39(1), 1–22.

See Also

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.cmeans, mlr_learners_clust.cobweb, mlr_learners_clust.dbscan, mlr_learners_clust.dbscan_fpc, mlr_learners_clust.diana, 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

Examples

if (requireNamespace("RWeka")) {
  learner = mlr3::lrn("clust.em")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Fuzzy Analysis Clustering Learner

Description

A LearnerClust for fuzzy clustering implemented in cluster::fanny(). cluster::fanny() doesn't have a default value for the number of clusters. Therefore, the k parameter which corresponds to the number of clusters here is set to 2 by default. The predict method copies cluster assignments and memberships generated for train data. The predict does not work for new data.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.fanny")
lrn("clust.fanny")

Meta Information

Parameters

Id Type Default Levels Range
k integer - [1, \infty)
memb.exp numeric 2 [1, \infty)
metric character euclidean euclidean, manhattan, SqEuclidean -
stand logical FALSE TRUE, FALSE -
maxit integer 500 [0, \infty)
tol numeric 1e-15 [0, \infty)
trace.lev integer 0 [0, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustFanny

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustFanny$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustFanny$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Kaufman, Leonard, Rousseeuw, J P (2009). Finding groups in data: an introduction to cluster analysis. John Wiley & Sons.

See Also

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.cmeans, mlr_learners_clust.cobweb, mlr_learners_clust.dbscan, mlr_learners_clust.dbscan_fpc, mlr_learners_clust.diana, mlr_learners_clust.em, 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

Examples

if (requireNamespace("cluster")) {
  learner = mlr3::lrn("clust.fanny")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Featureless Clustering Learner

Description

A simple LearnerClust which randomly (but evenly) assigns observations to num_clusters partitions (default: 1 partition).

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.featureless")
lrn("clust.featureless")

Meta Information

Parameters

Id Type Default Range
num_clusters integer - [1, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustFeatureless

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustFeatureless$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustFeatureless$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

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.cmeans, 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.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

Examples

if (requireNamespace("mlr3")) {
  learner = mlr3::lrn("clust.featureless")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Farthest First Clustering Learner

Description

A LearnerClust for Farthest First clustering implemented in RWeka::FarthestFirst(). The predict method uses RWeka::predict.Weka_clusterer() 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(): 

mlr_learners$get("clust.ff")
lrn("clust.ff")

Meta Information

Parameters

Id Type Default Levels Range
N integer 2 [1, \infty)
S integer 1 [1, \infty)
output_debug_info logical FALSE TRUE, FALSE -

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustFF

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustFarthestFirst$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustFarthestFirst$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Witten, H I, Frank, Eibe (2002). “Data mining: practical machine learning tools and techniques with Java implementations.” Acm Sigmod Record, 31(1), 76–77.

Hochbaum, S D, Shmoys, B D (1985). “A best possible heuristic for the k-center problem.” Mathematics of operations research, 10(2), 180–184.

See Also

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.cmeans, 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.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

Examples

if (requireNamespace("RWeka")) {
  learner = mlr3::lrn("clust.ff")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Agglomerative Hierarchical Clustering Learner

Description

A LearnerClust for agglomerative hierarchical clustering implemented in stats::hclust(). Difference Calculation is done by stats::dist()

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.hclust")
lrn("clust.hclust")

Meta Information

Parameters

Id Type Default Levels Range
method character complete ward.D, ward.D2, single, complete, average, mcquitty, median, centroid -
members untyped NULL -
distmethod character euclidean euclidean, maximum, manhattan, canberra, binary, minkowski -
diag logical FALSE TRUE, FALSE -
upper logical FALSE TRUE, FALSE -
p numeric 2 (-\infty, \infty)
k integer 2 [1, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustHclust

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustHclust$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustHclust$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Becker, A R, Chambers, M J, Wilks, R A (1988). The New S Language. Wadsworth & Brooks/Cole.

Everitt, S B (1974). Cluster Analysis. Heinemann Educational Books.

Hartigan, A J (1975). Clustering Algorithms. John Wiley & Sons.

Sneath, HA P, Sokal, R R (1973). Numerical Taxonomy. Freeman.

Anderberg, R M (1973). Cluster Analysis for Applications. Academic Press.

Gordon, David A (1999). Classification, 2 edition. Chapman and Hall / CRC.

Murtagh, Fionn (1985). “Multidimensional Clustering Algorithms.” In COMPSTAT Lectures 4. Physica-Verlag.

McQuitty, L L (1966). “Similarity Analysis by Reciprocal Pairs for Discrete and Continuous Data.” Educational and Psychological Measurement, 26(4), 825–831. doi:10.1177/001316446602600402.

Legendre, Pierre, Legendre, Louis (2012). Numerical Ecology, 3 edition. Elsevier Science BV.

Murtagh, Fionn, Legendre, Pierre (2014). “Ward's Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward's Criterion?” Journal of Classification, 31, 274–295. doi:10.1007/s00357-014-9161-z.

See Also

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.cmeans, 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.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

Examples

if (requireNamespace("stats")) {
  learner = mlr3::lrn("clust.hclust")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Hierarchical DBSCAN (HDBSCAN) Clustering Learner

Description

HDBSCAN (Hierarchical DBSCAN) clustering. Calls dbscan::hdbscan() from dbscan.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.hdbscan")
lrn("clust.hdbscan")

Meta Information

Parameters

Id Type Default Levels Range
minPts integer - [0, \infty)
gen_hdbscan_tree logical FALSE TRUE, FALSE -
gen_simplified_tree logical FALSE TRUE, FALSE -
verbose logical FALSE TRUE, FALSE -

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustHDBSCAN

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustHDBSCAN$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustHDBSCAN$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Hahsler M, Piekenbrock M, Doran D (2019). “dbscan: Fast Density-Based Clustering with R.” Journal of Statistical Software, 91(1), 1–30. doi:10.18637/jss.v091.i01.

Campello, JGB R, Moulavi, Davoud, Sander, Jörg (2013). “Density-based clustering based on hierarchical density estimates.” In Pacific-Asia conference on knowledge discovery and data mining, 160–172. Springer.

See Also

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.cmeans, 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.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

Examples

if (requireNamespace("dbscan")) {
  learner = mlr3::lrn("clust.hdbscan")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Kernel K-Means Clustering Learner

Description

A LearnerClust for kernel k-means clustering implemented in kernlab::kkmeans(). kernlab::kkmeans() doesn't have a default value for the number of clusters. Therefore, the centers parameter here is set to 2 by default. Kernel parameters have to be passed directly and not by using the kpar list in kkmeans. The predict method finds the nearest center in kernel distance to assign clusters for new data points.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.kkmeans")
lrn("clust.kkmeans")

Meta Information

Parameters

Id Type Default Levels Range
centers untyped - -
kernel character rbfdot vanilladot, polydot, rbfdot, tanhdot, laplacedot, besseldot, anovadot, splinedot -
sigma numeric - [0, \infty)
degree integer 3 [1, \infty)
scale numeric 1 [0, \infty)
offset numeric 1 (-\infty, \infty)
order integer 1 (-\infty, \infty)
alg character kkmeans kkmeans, kerninghan -
p numeric 1 (-\infty, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustKKMeans

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustKKMeans$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustKKMeans$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Karatzoglou, Alexandros, Smola, Alexandros, Hornik, Kurt, Zeileis, Achim (2004). “kernlab-an S4 package for kernel methods in R.” Journal of statistical software, 11, 1–20.

Dhillon, S I, Guan, Yuqiang, Kulis, Brian (2004). A unified view of kernel k-means, spectral clustering and graph cuts. Citeseer.

See Also

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.cmeans, 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.kmeans, mlr_learners_clust.mclust, mlr_learners_clust.meanshift, mlr_learners_clust.optics, mlr_learners_clust.pam, mlr_learners_clust.xmeans

Examples

if (requireNamespace("kernlab")) {
  learner = mlr3::lrn("clust.kkmeans")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

K-Means Clustering Learner

Description

A LearnerClust for k-means clustering implemented in stats::kmeans(). stats::kmeans() 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(): 

mlr_learners$get("clust.kmeans")
lrn("clust.kmeans")

Meta Information

Parameters

Id Type Default Levels Range
centers untyped - -
iter.max integer 10 [1, \infty)
algorithm character Hartigan-Wong Hartigan-Wong, Lloyd, Forgy, MacQueen -
nstart integer 1 [1, \infty)
trace integer 0 [0, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustKMeans

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustKMeans$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustKMeans$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Forgy, W E (1965). “Cluster analysis of multivariate data: efficiency versus interpretability of classifications.” Biometrics, 21, 768–769.

Hartigan, A J, Wong, A M (1979). “Algorithm AS 136: A K-means clustering algorithm.” Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100–108. doi:10.2307/2346830.

Lloyd, P S (1982). “Least squares quantization in PCM.” IEEE Transactions on Information Theory, 28(2), 129–137.

MacQueen, James (1967). “Some methods for classification and analysis of multivariate observations.” In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, volume 1, 281–297.

See Also

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.cmeans, 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.mclust, mlr_learners_clust.meanshift, mlr_learners_clust.optics, mlr_learners_clust.pam, mlr_learners_clust.xmeans

Examples

if (requireNamespace("stats") && requireNamespace("clue")) {
  learner = mlr3::lrn("clust.kmeans")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Gaussian Mixture Models-Based Clustering Learner

Description

A LearnerClust for model-based clustering implemented in mclust::Mclust(). The predict method uses mclust::predict.Mclust() 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(): 

mlr_learners$get("clust.mclust")
lrn("clust.mclust")

Meta Information

Parameters

Id Type Default
G untyped 1:9
modelNames untyped -
prior untyped -
control untyped mclust::emControl()
initialization untyped -
x untyped -

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustMclust

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustMclust$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustMclust$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Scrucca, Luca, Fop, Michael, Murphy, Brendan T, Raftery, E A (2016). “mclust 5: clustering, classification and density estimation using Gaussian finite mixture models.” The R journal, 8(1), 289.

Fraley, Chris, Raftery, E A (2002). “Model-based clustering, discriminant analysis, and density estimation.” Journal of the American statistical Association, 97(458), 611–631.

See Also

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.cmeans, 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.meanshift, mlr_learners_clust.optics, mlr_learners_clust.pam, mlr_learners_clust.xmeans

Examples

if (requireNamespace("mclust")) {
  learner = mlr3::lrn("clust.mclust")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Mean Shift Clustering Learner

Description

A LearnerClust for Mean Shift clustering implemented in LPCM::ms(). There is no predict method for LPCM::ms(), so the method returns cluster labels for the 'training' data.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.meanshift")
lrn("clust.meanshift")

Meta Information

Parameters

Id Type Default Range
h untyped - -
subset untyped - -
scaled integer 1 [0, \infty)
iter integer 200 [1, \infty)
thr numeric 0.01 (-\infty, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustMeanShift

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustMeanShift$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustMeanShift$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Cheng, Yizong (1995). “Mean shift, mode seeking, and clustering.” IEEE transactions on pattern analysis and machine intelligence, 17(8), 790–799.

See Also

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.cmeans, 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.optics, mlr_learners_clust.pam, mlr_learners_clust.xmeans

Examples

if (requireNamespace("LPCM")) {
  learner = mlr3::lrn("clust.meanshift")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Ordering Points to Identify the Clustering Structure (OPTICS) Clustering Learner

Description

OPTICS (Ordering points to identify the clustering structure) point ordering clustering. Calls dbscan::optics() from dbscan.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn(): 

mlr_learners$get("clust.optics")
lrn("clust.optics")

Meta Information

Parameters

Id Type Default Levels Range
eps numeric NULL [0, \infty)
minPts integer 5 [0, \infty)
search character kdtree kdtree, linear, dist -
bucketSize integer 10 [1, \infty)
splitRule character SUGGEST STD, MIDPT, FAIR, SL_MIDPT, SL_FAIR, SUGGEST -
approx numeric 0 (-\infty, \infty)
eps_cl numeric - [0, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustOPTICS

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustOPTICS$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustOPTICS$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Hahsler M, Piekenbrock M, Doran D (2019). “dbscan: Fast Density-Based Clustering with R.” Journal of Statistical Software, 91(1), 1–30. doi:10.18637/jss.v091.i01.

Ankerst, Mihael, Breunig, M M, Kriegel, Hans-Peter, Sander, Jörg (1999). “OPTICS: Ordering points to identify the clustering structure.” ACM Sigmod record, 28(2), 49–60.

See Also

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.cmeans, 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.pam, mlr_learners_clust.xmeans

Examples

if (requireNamespace("dbscan")) {
  learner = mlr3::lrn("clust.optics")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Partitioning Around Medoids Clustering Learner

Description

A LearnerClust for PAM clustering implemented in cluster::pam(). cluster::pam() doesn't have a default value for the number of clusters. Therefore, the k parameter which corresponds to the number of clusters 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(): 

mlr_learners$get("clust.pam")
lrn("clust.pam")

Meta Information

Parameters

Id Type Default Levels Range
k integer - [1, \infty)
metric character - euclidian, manhattan -
medoids untyped NULL -
stand logical FALSE TRUE, FALSE -
do.swap logical TRUE TRUE, FALSE -
pamonce integer 0 [0, 5]
trace.lev integer 0 [0, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustPAM

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustPAM$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustPAM$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Reynolds, P A, Richards, Graeme, de la Iglesia, Beatriz, Rayward-Smith, J V (2006). “Clustering rules: a comparison of partitioning and hierarchical clustering algorithms.” Journal of Mathematical Modelling and Algorithms, 5, 475–504.

Schubert, Erich, Rousseeuw, J P (2019). “Faster k-medoids clustering: improving the PAM, CLARA, and CLARANS algorithms.” In Similarity Search and Applications: 12th International Conference, SISAP 2019, Newark, NJ, USA, October 2–4, 2019, Proceedings 12, 171–187. Springer.

See Also

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.cmeans, 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.xmeans

Examples

if (requireNamespace("cluster")) {
  learner = mlr3::lrn("clust.pam")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

X-means Clustering Learner

Description

A LearnerClust for X-means clustering implemented in RWeka::XMeans(). The predict method uses RWeka::predict.Weka_clusterer() 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(): 

mlr_learners$get("clust.xmeans")
lrn("clust.xmeans")

Meta Information

Parameters

Id Type Default Levels Range
B numeric 1 [0, \infty)
C numeric 0 [0, \infty)
D untyped "weka.core.EuclideanDistance" -
H integer 4 [1, \infty)
I integer 1 [1, \infty)
J integer 1000 [1, \infty)
K untyped "" -
L integer 2 [1, \infty)
M integer 1000 [1, \infty)
S integer 10 [1, \infty)
U integer 0 [0, \infty)
use_kdtree logical FALSE TRUE, FALSE -
N untyped - -
O untyped - -
Y untyped - -
output_debug_info logical FALSE TRUE, FALSE -

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustXMeans

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustXMeans$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustXMeans$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Witten, H I, Frank, Eibe (2002). “Data mining: practical machine learning tools and techniques with Java implementations.” Acm Sigmod Record, 31(1), 76–77.

Pelleg, Dan, Moore, W A, others (2000). “X-means: Extending k-means with efficient estimation of the number of clusters.” In Icml, volume 1, 727–734.

See Also

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.cmeans, 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

Examples

if (requireNamespace("RWeka")) {
  learner = mlr3::lrn("clust.xmeans")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

Calinski Harabasz Pseudo F-Statistic

Description

The score function calls fpc::cluster.stats() from package fpc. "ch" is used subset output of the function call.

Format

R6::R6Class() inheriting from MeasureClust.

Construction

This measures can be retrieved from the dictionary mlr3::mlr_measures: 

mlr_measures$get("clust.ch")
msr("clust.ch")

Meta Information

See Also

Dictionary of Measures: mlr3::mlr_measures

as.data.table(mlr_measures) for a complete table of all (also dynamically created) mlr3::Measure implementations.

Other cluster measures: mlr_measures_clust.dunn, mlr_measures_clust.silhouette, mlr_measures_clust.wss

Dunn Index

Description

The score function calls fpc::cluster.stats() from package fpc. "dunn" is used subset output of the function call.

Format

R6::R6Class() inheriting from MeasureClust.

Construction

This measures can be retrieved from the dictionary mlr3::mlr_measures: 

mlr_measures$get("clust.dunn")
msr("clust.dunn")

Meta Information

See Also

Dictionary of Measures: mlr3::mlr_measures

as.data.table(mlr_measures) for a complete table of all (also dynamically created) mlr3::Measure implementations.

Other cluster measures: mlr_measures_clust.ch, mlr_measures_clust.silhouette, mlr_measures_clust.wss

Rousseeuw's Silhouette Quality Index

Description

The score function calls cluster::silhouette() from package cluster. "sil_width" is used subset output of the function call.

Format

R6::R6Class() inheriting from MeasureClust.

Construction

This measures can be retrieved from the dictionary mlr3::mlr_measures: 

mlr_measures$get("clust.silhouette")
msr("clust.silhouette")

Meta Information

See Also

Dictionary of Measures: mlr3::mlr_measures

as.data.table(mlr_measures) for a complete table of all (also dynamically created) mlr3::Measure implementations.

Other cluster measures: mlr_measures_clust.ch, mlr_measures_clust.dunn, mlr_measures_clust.wss

Within Sum of Squares

Description

The score function calls fpc::cluster.stats() from package fpc. "within.cluster.ss" is used subset output of the function call.

Format

R6::R6Class() inheriting from MeasureClust.

Construction

This measures can be retrieved from the dictionary mlr3::mlr_measures: 

mlr_measures$get("clust.wss")
msr("clust.wss")

Meta Information

See Also

Dictionary of Measures: mlr3::mlr_measures

as.data.table(mlr_measures) for a complete table of all (also dynamically created) mlr3::Measure implementations.

Other cluster measures: mlr_measures_clust.ch, mlr_measures_clust.dunn, mlr_measures_clust.silhouette

Ruspini Cluster Task

Description

A cluster task for the cluster::ruspini data set.

Format

R6::R6Class inheriting from TaskClust.

Dictionary

This mlr3::Task can be instantiated via the dictionary mlr3::mlr_tasks or with the associated sugar function mlr3::tsk(): 

mlr_tasks$get("ruspini")
tsk("ruspini")

Meta Information

References

Ruspini EH (1970). “Numerical methods for fuzzy clustering.” Information Sciences, 2(3), 319-350. doi:10.1016/S0020-0255(70)80056-1.

See Also

Other Task: TaskClust, mlr_tasks_usarrests

US Arrests Cluster Task

Description

A cluster task for the datasets::USArrests data set. Rownames are stored as variable "states" with column role "name".

Format

R6::R6Class inheriting from TaskClust.

Dictionary

This mlr3::Task can be instantiated via the dictionary mlr3::mlr_tasks or with the associated sugar function mlr3::tsk(): 

mlr_tasks$get("usarrests")
tsk("usarrests")

Meta Information

References

Berry, Brian J (1979). “Interactive Data Analysis: A Practical Primer.” Journal of the Royal Statistical Society: Series C (Applied Statistics), 28, 181.

See Also

Other Task: TaskClust, mlr_tasks_ruspini

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.