GenericClusteringStrategy: Abstract Feature Clustering Strategy class.
In D2MCS: Data Driving Multiple Classifier System
GenericClusteringStrategy R Documentation
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
-
-
-
-
-
-
-
-
-
-
-
-
Method new()
A function responsible for creating a
GenericClusteringStrategy object.
Usage
GenericClusteringStrategy$new(subset, heuristic, description, configuration)
Arguments
subsetA Subset object to perform the clustering strategy.
heuristicThe heuristic to be applied. Must inherit from
GenericHeuristic class.
descriptionA character vector describing the strategy
operation.
configurationOptional customized configuration parameters for the
strategy. Must inherited from StrategyConfiguration
abstract class.
Method getDescription()
The function is used to obtain the description of the
strategy.
Usage
GenericClusteringStrategy$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$execute(verbose, ...)
Arguments
verboseA logical value to specify if more verbosity is
needed.
...Further arguments passed down to execute function.
Method getDistribution()
Abstract function used to obtain the set of features
following an specific clustering distribution.
Usage
GenericClusteringStrategy$getDistribution(
num.clusters = NULL,
num.groups = NULL,
include.unclustered = FALSE
)
Arguments
num.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.
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$createTrain(
subset,
num.cluster = NULL,
num.groups = NULL,
include.unclustered = FALSE
)
Arguments
subsetA Subset object used as a basis to create the
Trainset
num.clusterA numeric value to select the number of
clusters (define the distribution).
num.groupsA single or numeric vector value to identify a
specific group that forms the clustering distribution.
include.unclusteredA logical value to determine if
unclustered features should be included.
Method plot()
Abstract function responsible of creating a plot to
visualize the clustering distribution.
Usage
GenericClusteringStrategy$plot(dir.path = NULL, file.name = NULL, ...)
Arguments
dir.pathAn 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.nameThe 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$saveCSV(dir.path, name, num.clusters = NULL)
Arguments
dir.pathThe name of the directory to save the CSV file.
nameDefines the name of the CSV file.
num.clustersAn optional parameter to select the number of
clusters to be saved. If not defined, all clusters will be saved.
Method clone()
The objects of this class are cloneable with this method.
Usage
GenericClusteringStrategy$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
Subset, GenericHeuristic
D2MCS documentation built on Aug. 23, 2022, 5:07 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| GenericClusteringStrategy | R Documentation |
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
Method new()
A function responsible for creating a GenericClusteringStrategy object.
Usage
GenericClusteringStrategy$new(subset, heuristic, description, configuration)
Arguments
subsetA
Subsetobject to perform the clustering strategy.heuristicThe heuristic to be applied. Must inherit from
GenericHeuristicclass.descriptionA character vector describing the strategy operation.
configurationOptional customized configuration parameters for the strategy. Must inherited from
StrategyConfigurationabstract class.
Method getDescription()
The function is used to obtain the description of the strategy.
Usage
GenericClusteringStrategy$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$execute(verbose, ...)
Arguments
verboseA logical value to specify if more verbosity is needed.
...Further arguments passed down to
executefunction.
Method getDistribution()
Abstract function used to obtain the set of features following an specific clustering distribution.
Usage
GenericClusteringStrategy$getDistribution( num.clusters = NULL, num.groups = NULL, include.unclustered = FALSE )
Arguments
num.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.
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$createTrain( subset, num.cluster = NULL, num.groups = NULL, include.unclustered = FALSE )
Arguments
subsetA
Subsetobject used as a basis to create the Trainsetnum.clusterA numeric value to select the number of clusters (define the distribution).
num.groupsA single or numeric vector value to identify a specific group that forms the clustering distribution.
include.unclusteredA logical value to determine if unclustered features should be included.
Method plot()
Abstract function responsible of creating a plot to visualize the clustering distribution.
Usage
GenericClusteringStrategy$plot(dir.path = NULL, file.name = NULL, ...)
Arguments
dir.pathAn 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.nameThe name of the PDF file where the plot is exported.
...Further arguments passed down to
executefunction.
Method saveCSV()
Abstract function to save the clustering distribution to a CSV file.
Usage
GenericClusteringStrategy$saveCSV(dir.path, name, num.clusters = NULL)
Arguments
dir.pathThe name of the directory to save the CSV file.
nameDefines the name of the CSV file.
num.clustersAn optional parameter to select the number of clusters to be saved. If not defined, all clusters will be saved.
Method clone()
The objects of this class are cloneable with this method.
Usage
GenericClusteringStrategy$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
Subset, GenericHeuristic
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.