GenericClusteringStrategy {D2MCS}

Abstract Feature Clustering Strategy class.

Description

Abstract class used as a template to ensure the proper definition of new customized clustering strategies.

Details

The GenericClusteringStrategy is an archetype class so it cannot be instantiated.

Methods

Public methods

• GenericClusteringStrategy$new()

• GenericClusteringStrategy$getDescription()

• GenericClusteringStrategy$getHeuristic()

• GenericClusteringStrategy$getConfiguration()

• GenericClusteringStrategy$getBestClusterDistribution()

• GenericClusteringStrategy$getUnclustered()

• GenericClusteringStrategy$execute()

• GenericClusteringStrategy$getDistribution()

• GenericClusteringStrategy$createTrain()

• GenericClusteringStrategy$plot()

• GenericClusteringStrategy$saveCSV()

• GenericClusteringStrategy$clone()

Method new()

A function responsible for creating a GenericClusteringStrategy object.

Usage

GenericClusteringStrategy$new(subset, heuristic, description, configuration)

Arguments

subset

A Subset object to perform the clustering strategy.

heuristic

The heuristic to be applied. Must inherit from GenericHeuristic class.

description

A character vector describing the strategy operation.

configuration

Optional customized configuration parameters for the strategy. Must inherited from StrategyConfiguration abstract class.

Method getDescription()

The function is used to obtain the description of the strategy.

Usage

GenericClusteringStrategy$getDescription()

Returns

A character vector of NULL if not defined.

Method getHeuristic()

The function returns the heuristic applied for the clustering strategy.

Usage

GenericClusteringStrategy$getHeuristic()

Returns

An object inherited from GenericClusteringStrategy class.

Method getConfiguration()

The function returns the configuration parameters used to perform the clustering strategy.

Usage

GenericClusteringStrategy$getConfiguration()

Returns

An object inherited from StrategyConfiguration class.

Method getBestClusterDistribution()

The function obtains the best clustering distribution.

Usage

GenericClusteringStrategy$getBestClusterDistribution()

Returns

A list of clusters. Each list element represents a feature group.

Method getUnclustered()

The function is used to return the features that cannot be clustered due to incompatibilities with the used heuristic.

Usage

GenericClusteringStrategy$getUnclustered()

Returns

A character vector containing the unclassified features.

Method execute()

Abstract function responsible of performing the clustering strategy over the defined Subset.

Usage

GenericClusteringStrategy$execute(verbose, ...)

Arguments

verbose

A logical value to specify if more verbosity is needed.

...

Further arguments passed down to execute function.

Method getDistribution()

Abstract function used to obtain the set of features following an specific clustering distribution.

Usage

GenericClusteringStrategy$getDistribution(
  num.clusters = NULL,
  num.groups = NULL,
  include.unclustered = FALSE
)

Arguments

num.clusters

A numeric value to select the number of clusters (define the distribution).

num.groups

A single or numeric vector value to identify a specific group that forms the clustering distribution.

include.unclustered

A logical value to determine if unclustered features should be included.

Returns

A list with the features comprising an specific clustering distribution.

Method createTrain()

Abstract function in charge of creating a Trainset object for training purposes.

Usage

GenericClusteringStrategy$createTrain(
  subset,
  num.cluster = NULL,
  num.groups = NULL,
  include.unclustered = FALSE
)

Arguments

subset

A Subset object used as a basis to create the Trainset

num.cluster

A numeric value to select the number of clusters (define the distribution).

num.groups

A single or numeric vector value to identify a specific group that forms the clustering distribution.

include.unclustered

A logical value to determine if unclustered features should be included.

Method plot()

Abstract function responsible of creating a plot to visualize the clustering distribution.

Usage

GenericClusteringStrategy$plot(dir.path = NULL, file.name = NULL, ...)

Arguments

dir.path

An optional character argument to define the name of the directory where the exported plot will be saved. If not defined, the file path will be automatically assigned to the current working directory, 'getwd()'.

file.name

The name of the PDF file where the plot is exported.

...

Further arguments passed down to execute function.

Method saveCSV()

Abstract function to save the clustering distribution to a CSV file.

Usage

GenericClusteringStrategy$saveCSV(dir.path, name, num.clusters = NULL)

Arguments

dir.path

The name of the directory to save the CSV file.

name

Defines the name of the CSV file.

num.clusters

An optional parameter to select the number of clusters to be saved. If not defined, all clusters will be saved.

Method clone()

The objects of this class are cloneable with this method.

Usage

GenericClusteringStrategy$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Subset, GenericHeuristic