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.
D2MCS::GenericClusteringStrategy -> SimpleStrategy
new()Method for initializing the object arguments during runtime.
SimpleStrategy$new(
subset,
heuristic,
configuration = StrategyConfiguration$new()
)subsetThe Subset used to apply the
feature-clustering strategy.
heuristicThe heuristic used to compute the relevance of each
feature. Must inherit from GenericHeuristic abstract class.
configurationOptional parameter to customize configuration
parameters for the strategy. Must inherited from
StrategyConfiguration abstract class.
execute()Function responsible of performing the clustering
strategy over the defined Subset.
SimpleStrategy$execute(verbose = FALSE)verboseA logical value to specify if more verbosity is needed.
getBestClusterDistribution()The function obtains the best clustering distribution.
SimpleStrategy$getBestClusterDistribution()A list of clusters. Each list element represents a feature group.
getUnclustered()The function is used to return the features that cannot be clustered due to incompatibilities with the used heuristic.
SimpleStrategy$getUnclustered()A character vector containing the unclassified features.
getDistribution()Function used to obtain a specific cluster distribution.
SimpleStrategy$getDistribution(
num.clusters = NULL,
num.groups = NULL,
include.unclustered = FALSE
)num.clustersA numeric value to select the number of clusters (define the distribution).
num.groupsA single or numeric vector value to identify a specific group that forms the clustering distribution.
include.unclusteredA logical value to determine if unclustered features should be included.
A list with the features comprising an specific clustering distribution.
createTrain()The function is used to create a Trainset
object from a specific clustering distribution.
SimpleStrategy$createTrain(
subset,
num.clusters = NULL,
num.groups = NULL,
include.unclustered = FALSE
)subsetThe Subset object used as a basis to create
the train set (see Trainset class).
num.clustersA numeric value to select the number of clusters (define the distribution).
num.groupsA single or numeric vector value to identify a specific group that forms the clustering distribution.
include.unclusteredA logical value to determine if unclustered features should be included.
If num.clusters and num.groups are not defined,
best clustering distribution is used to create the train set.
A Trainset object.
plot()The function is responsible for creating a plot to visualize the clustering distribution.
SimpleStrategy$plot(dir.path = NULL, file.name = NULL)dir.pathAn 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.nameA character to define the name of the PDF file where the plot is exported.
saveCSV()The function is used to save the clustering distribution to a CSV file.
SimpleStrategy$saveCSV(dir.path, name = NULL, num.clusters = NULL)dir.pathThe name of the directory to save the CSV file.
nameDefines the name of the CSV file.
num.clustersAn optional parameter to select the number of clusters to be saved. If not defined, all cluster distributions will be saved.
clone()The objects of this class are cloneable with this method.
SimpleStrategy$clone(deep = FALSE)deepWhether to make a deep clone.
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'.
GenericClusteringStrategy,
StrategyConfiguration