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D2MCS (version 1.0.1)

TypeBasedStrategy: Feature clustering strategy.

Description

Features are sorted by descendant according to the relevance value obtained after applying an specific heuristic. Next, features are distributed into N clusters following a card-dealing methodology. Finally best distribution is assigned to the distribution having highest homogeneity.

Arguments

Super class

D2MCS::GenericClusteringStrategy -> TypeBasedStrategy

Methods

Inherited methods

Method new()

Method for initializing the object arguments during runtime.

Usage

TypeBasedStrategy$new(
  subset,
  heuristic,
  configuration = StrategyConfiguration$new()
)

Arguments

subset

The Subset used to apply the feature-clustering strategy.

heuristic

The heuristic used to compute the relevance of each feature. Must inherit from GenericHeuristic abstract class.

configuration

Optional parameter to customize configuration parameters for the strategy. Must inherited from StrategyConfiguration abstract class.

Method execute()

Function responsible of performing the clustering strategy over the defined Subset.

Usage

TypeBasedStrategy$execute(verbose = FALSE)

Arguments

verbose

A logical value to specify if more verbosity is needed.

Method getDistribution()

Function used to obtain a specific cluster distribution.

Usage

TypeBasedStrategy$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()

The function is used to create a Trainset object from a specific clustering distribution.

Usage

TypeBasedStrategy$createTrain(
  subset,
  num.clusters = NULL,
  num.groups = NULL,
  include.unclustered = FALSE
)

Arguments

subset

The Subset object used as a basis to create the train set (see Trainset class).

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.

Details

If num.clusters and num.groups are not defined, best clustering distribution is used to create the train set.

Returns

A Trainset object.

Method plot()

The function is responsible for creating a plot to visualize the clustering distribution.

Usage

TypeBasedStrategy$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

A character to define the name of the PDF file where the plot is exported.

Method saveCSV()

The function is used to save the clustering distribution to a CSV file.

Usage

TypeBasedStrategy$saveCSV(dir.path = NULL, name = NULL, 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 cluster distributions will be saved.

Method clone()

The objects of this class are cloneable with this method.

Usage

TypeBasedStrategy$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

The strategy is suitable only for binary and real features. Other features are automatically grouped into a specific cluster named as 'unclustered'.

See Also

GenericClusteringStrategy, StrategyConfiguration