Package 'mlr3cluster' reference manual

Title:	Cluster Extension for 'mlr3'
Description:	Extends the 'mlr3' package with cluster analysis.
Authors:	Damir Pulatov [cre, aut], Michel Lang [aut]
Maintainer:	Damir Pulatov <[email protected]>
License:	LGPL-3
Version:	0.1.9
Built:	2024-10-28 05:06:07 UTC
Source:	https://github.com/mlr-org/mlr3cluster

mlr3cluster: Cluster Extension for 'mlr3'

Description

Extends the 'mlr3' package with cluster analysis.

Author(s)

Maintainer: Damir Pulatov [email protected]

Authors:

Michel Lang [email protected] (ORCID)

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

TaskClust: ensure the identity.
data.frame() and 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 = deparse(substitute(x)), ...)

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

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

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

## S3 method for class 'formula'
as_task_clust(x, data, id = deparse(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)
as_task_clust(datasets::USArrests)

Cluster Learner

Description

This Learner specializes mlr3::Learner for cluster problems:

task_type is set to "clust".
Creates Predictions of class PredictionClust.
Possible values for predict_types are:
- "partition": Integer indicating the cluster membership.
- "prob": Probability for belonging to each cluster.

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

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

task_type is set to "clust".
Possible values for predict_type are "partition" and "prob".

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

Super class

mlr3::Measure -> MeasureClust

Methods

Public methods

MeasureClust$new()

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 Task),
"requires_learner" (requires the trained Learner),
"requires_train_set" (requires the training indices from the Resampling), and
"na_score" (the measure is expected to occasionally return NA or NaN).

predict_type

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

task_properties

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

packages

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()⁠.

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

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, cluster

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

LearnerClustAgnes$new()
LearnerClustAgnes$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, apcluster

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

LearnerClustAP$new()
LearnerClustAP$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
if (requireNamespace("apcluster")) {
  learner = mlr3::lrn("clust.ap")
  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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Methods

Public methods

LearnerClustCMeans$new()
LearnerClustCMeans$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, RWeka

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

LearnerClustCobweb$new()
LearnerClustCobweb$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, dbscan

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

LearnerClustDBSCAN$new()
LearnerClustDBSCAN$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, fpc

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			-

Super classes

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

Methods

Public methods

LearnerClustDBSCANfpc$new()
LearnerClustDBSCANfpc$clone()

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

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, cluster

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

LearnerClustDiana$new()
LearnerClustDiana$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Methods

Public methods

LearnerClustEM$new()
LearnerClustEM$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”, “prob”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, cluster

Parameters

Id	Type	Default	Levels	Range
k	integer	2		$[1, \infty)$
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

LearnerClustFanny$new()
LearnerClustFanny$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”, “prob”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster

Parameters

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

Super classes

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

Methods

Public methods

LearnerClustFeatureless$new()
LearnerClustFeatureless$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, RWeka

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

LearnerClustFarthestFirst$new()
LearnerClustFarthestFirst$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, 'stats'

Parameters

Id	Type	Default	Levels	Range
method	character	complete	ward.D, ward.D2, single, complete, average, mcquitty, median, centroid	-
members	untyped			-
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

LearnerClustHclust$new()
LearnerClustHclust$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, dbscan

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

LearnerClustHDBSCAN$new()
LearnerClustHDBSCAN$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, kernlab

Parameters

Id	Type	Default	Levels	Range
centers	untyped	2		-
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

LearnerClustKKMeans$new()
LearnerClustKKMeans$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, 'stats', clue

Parameters

Id	Type	Default	Levels	Range
centers	untyped	2		-
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

LearnerClustKMeans$new()
LearnerClustKMeans$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
if (requireNamespace("stats") && requireNamespace("clue")) {
  learner = mlr3::lrn("clust.kmeans")
  print(learner)

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

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

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

Meta Information

Task type: “clust”
Predict Types: “partition”, “prob”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, ClusterR

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			-
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

LearnerClustMiniBatchKMeans$new()
LearnerClustMiniBatchKMeans$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
if (requireNamespace("ClusterR")) {
  learner = mlr3::lrn("clust.MBatchKMeans")
  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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”, “prob”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, mclust

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

LearnerClustMclust$new()
LearnerClustMclust$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, LPCM

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

LearnerClustMeanShift$new()
LearnerClustMeanShift$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, dbscan

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

LearnerClustOPTICS$new()
LearnerClustOPTICS$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, cluster

Parameters

Id	Type	Default	Levels	Range
k	integer	2		$[1, \infty)$
metric	character	-	euclidian, manhattan	-
medoids	untyped			-
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

LearnerClustPAM$new()
LearnerClustPAM$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
if (requireNamespace("cluster")) {
  learner = mlr3::lrn("clust.pam")
  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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, RWeka

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

LearnerClustSimpleKMeans$new()
LearnerClustSimpleKMeans$clone()

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.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
if (requireNamespace("RWeka")) {
  learner = mlr3::lrn("clust.SimpleKMeans")
  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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

Meta Information

Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, RWeka

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

LearnerClustXMeans$new()
LearnerClustXMeans$clone()

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.

Examples

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

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

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

Meta Information

Range: $[0, \infty)$
Minimize: FALSE
Required predict type: partition

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 mlr_measures:

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

Meta Information

Range: $[0, \infty)$
Minimize: FALSE
Required predict type: partition

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 mlr_measures:

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

Meta Information

Range: $[0, \infty)$
Minimize: FALSE
Required predict type: partition

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 mlr_measures:

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

Meta Information

Range: $[0, \infty)$
Minimize: TRUE
Required predict type: partition

Ruspini Cluster Task

Description

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

Format

R6::R6Class inheriting from TaskClust.

Construction

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

Source

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

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.

Construction

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

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

PredictionClust$new()
PredictionClust$clone()

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

Super classes

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

Methods

Public methods

TaskClust$new()
TaskClust$clone()

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

Examples

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

# possible properties:
mlr_reflections$task_properties$clust
library(mlr3)
library(mlr3cluster)
task = TaskClust$new("usarrests", backend = USArrests)
task$task_type

# possible properties:
mlr_reflections$task_properties$clust

Package 'mlr3cluster'

Help Index

mlr3cluster: Cluster Extension for 'mlr3'

Description

Author(s)

See Also

Convert to a Cluster Prediction

Description

Usage

Arguments

Value

Examples

Convert to a Cluster Task

Description

Usage

Arguments

Value

Examples

Cluster Learner

Description

Super class

Public fields

Methods

Public methods

Method new()

Usage

Arguments

Method reset()

Usage

Method clone()

Usage

Arguments

Examples

Cluster Measure

Description

Super class

Methods

Public methods

Method new()

Usage

Arguments

See Also

Agglomerative Hierarchical Clustering Learner

Description

Dictionary

Meta Information

Parameters

Super classes

Methods

Public methods

Method new()

Usage

Method clone()

Usage

Arguments

References

See Also

Examples

Affinity Propagation Clustering Learner

Description

Dictionary

Meta Information

Parameters

Super classes

Methods

Public methods

Method new()

Usage

Method clone()

Usage

Arguments

References

See Also

Examples

Fuzzy C-Means Clustering Learner

Description

Dictionary

Meta Information

Parameters

Super classes

Method `new()`

Method `reset()`

Method `clone()`

Method `new()`

Method `new()`

Method `clone()`

Method `new()`

Method `clone()`

Method `new()`

Method `clone()`

Method `new()`

Method `clone()`

Method `new()`

Method `clone()`

Method `new()`

Method `clone()`

Method `new()`

Method `clone()`