DependencyBasedStrategyConfiguration {D2MCS}

Custom Strategy Configuration handler for the DependencyBasedStrategy strategy.

Description

Define the default configuration parameters for the DependencyBasedStrategy strategy.

Super class

D2MCS::StrategyConfiguration -> DependencyBasedStrategyConfiguration

Methods

Public methods

• DependencyBasedStrategyConfiguration$new()

• DependencyBasedStrategyConfiguration$minNumClusters()

• DependencyBasedStrategyConfiguration$maxNumClusters()

• DependencyBasedStrategyConfiguration$getBinaryCutoff()

• DependencyBasedStrategyConfiguration$getRealCutoff()

• DependencyBasedStrategyConfiguration$setBinaryCutoff()

• DependencyBasedStrategyConfiguration$setRealCutoff()

• DependencyBasedStrategyConfiguration$tiebreak()

• DependencyBasedStrategyConfiguration$qualityOfCluster()

• DependencyBasedStrategyConfiguration$isImprovingClustering()

• DependencyBasedStrategyConfiguration$clone()

Method new()

Method for initializing the object arguments during runtime.

Usage

DependencyBasedStrategyConfiguration$new(
  binaryCutoff = 0.6,
  realCutoff = 0.6,
  tiebreakMethod = "lfdc",
  metric = "dep.tar"
)

Arguments

binaryCutoff

The numeric value of binary cutoff.

realCutoff

The numeric value of real cutoff.

tiebreakMethod

The character value of tie-break method. The two tiebreak methods available are "lfdc" (less dependence cluster with the features) and "ltdc" (less dependence cluster with the target). These methods are used to add the features in the candidate feature clusters.

metric

The character value of the metric to apply the mean to obtain the quality of a cluster. The two metrics available are "dep.tar" (Dependence of cluster features on the target) and "dep.fea" (Dependence between cluster features).

Method minNumClusters()

Function used to return the minimum number of clusters distributions used. By default the minimum is set in 2.

Usage

DependencyBasedStrategyConfiguration$minNumClusters(...)

Arguments

...

Further arguments passed down to minNumClusters function.

Returns

A numeric vector of length 1.

Method maxNumClusters()

The function is responsible of returning the maximum number of cluster distributions used. By default the maximum number is set in 50.

Usage

DependencyBasedStrategyConfiguration$maxNumClusters(...)

Arguments

...

Further arguments passed down to maxNumClusters function.

Returns

A numeric vector of length 1.

Method getBinaryCutoff()

Gets the cutoff to consider the dependency between binary features.

Usage

DependencyBasedStrategyConfiguration$getBinaryCutoff()

Returns

The numeric value of binary cutoff.

Method getRealCutoff()

Gets the cutoff to consider the dependency between real features.

Usage

DependencyBasedStrategyConfiguration$getRealCutoff()

Returns

The numeric value of real cutoff.

Method setBinaryCutoff()

Sets the cutoff to consider the dependency between binary features.

Usage

DependencyBasedStrategyConfiguration$setBinaryCutoff(cutoff)

Arguments

cutoff

The new numeric value of binary cutoff.

Method setRealCutoff()

Sets the cutoff to consider the dependency between real features.

Usage

DependencyBasedStrategyConfiguration$setRealCutoff(cutoff)

Arguments

cutoff

The new numeric value of real cutoff.

Method tiebreak()

The function solves the ties between two (or more) features.

Usage

DependencyBasedStrategyConfiguration$tiebreak(
  feature,
  clus.candidates,
  fea.dep.dist.clus,
  corpus,
  heuristic,
  class,
  class.name
)

Arguments

feature

A character containing the name of the feature

clus.candidates

A single or numeric vector value to identify the candidate groups to insert the feature.

fea.dep.dist.clus

A list containing the groups chosen for the features.

corpus

A data.frame containing the features of the initial data.

heuristic

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

class

A character vector containing all the values of the target class.

class.name

A character value representing the name of the target class.

Method qualityOfCluster()

The function determines the quality of a cluster.

Usage

DependencyBasedStrategyConfiguration$qualityOfCluster(clusters, metrics)

Arguments

clusters

A list with the feature distribution of each cluster.

metrics

A numeric list with the metrics associated to the cluster (dependency between all features and dependency between the features and the class).

Returns

A numeric vector of length 1.

Method isImprovingClustering()

The function indicates if clustering is getting better as the number of them increases.

Usage

DependencyBasedStrategyConfiguration$isImprovingClustering(clusters.deltha)

Arguments

clusters.deltha

A numeric vector value with the quality values of the built clusters.

Returns

A numeric vector of length 1.

Method clone()

The objects of this class are cloneable with this method.

Usage

DependencyBasedStrategyConfiguration$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

StrategyConfiguration, DependencyBasedStrategy