SimpleStrategy {D2MCS} | R Documentation |
Simple 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.
Details
The strategy is suitable for all features that are valid for the indicated heuristics. Invalid features are automatically grouped into a specific cluster named as 'unclustered'.
Super class
D2MCS::GenericClusteringStrategy
-> SimpleStrategy
Methods
Public methods
Inherited methods
Method new()
Method for initializing the object arguments during runtime.
Usage
SimpleStrategy$new( subset, heuristic, configuration = StrategyConfiguration$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
SimpleStrategy$execute(verbose = FALSE)
Arguments
verbose
A logical value to specify if more verbosity is needed.
Method getBestClusterDistribution()
The function obtains the best clustering distribution.
Usage
SimpleStrategy$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
SimpleStrategy$getUnclustered()
Returns
A character vector containing the unclassified features.
Method getDistribution()
Function used to obtain a specific cluster distribution.
Usage
SimpleStrategy$getDistribution( num.clusters = NULL, num.groups = NULL, include.unclustered = FALSE )
Arguments
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
SimpleStrategy$createTrain( subset, num.clusters = NULL, num.groups = NULL, include.unclustered = FALSE )
Arguments
subset
The
Subset
object used as a basis to create the train set (seeTrainset
class).num.clusters
A numeric value to select the number of clusters (define the distribution).
num.groups
A single or numeric vector value to identify a specific group that forms the clustering distribution.
include.unclustered
A logical value to determine if unclustered features should be included.
Details
If num.clusters
and num.groups
are not defined,
best clustering distribution is used to create the train set.
Returns
A Trainset
object.
Method plot()
The function is responsible for creating a plot to visualize the clustering distribution.
Usage
SimpleStrategy$plot(dir.path = NULL, file.name = NULL)
Arguments
dir.path
An optional 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
SimpleStrategy$saveCSV(dir.path, name = NULL, num.clusters = NULL)
Arguments
dir.path
The name of the directory to save the CSV file.
name
Defines the name of the CSV file.
num.clusters
An optional parameter to select the number of clusters to be saved. If not defined, all cluster distributions will be saved.
Method clone()
The objects of this class are cloneable with this method.
Usage
SimpleStrategy$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
See Also
GenericClusteringStrategy
,
StrategyConfiguration