Uses of Interface
weka.clusterers.Clusterer

Packages that use Clusterer

Package	Description
weka.attributeSelection
weka.clusterers
weka.filters.unsupervised.attribute
weka.gui.beans
weka.gui.explorer
weka.knowledgeflow.steps

Uses of Clusterer in weka.attributeSelection

Methods in weka.attributeSelection that return Clusterer

Modifier and Type	Method	Description
abstract Clusterer	UnsupervisedSubsetEvaluator.getClusterer()	Get the clusterer

Methods in weka.attributeSelection with parameters of type Clusterer

Modifier and Type	Method	Description
abstract void	UnsupervisedSubsetEvaluator.setClusterer(Clusterer d)	Set the clusterer to use

Uses of Clusterer in weka.clusterers

Subinterfaces of Clusterer in weka.clusterers

Modifier and Type	Interface	Description
interface	DensityBasedClusterer	Interface for clusterers that can estimate the density for a given instance.

Classes in weka.clusterers that implement Clusterer

Modifier and Type	Class	Description
class	AbstractClusterer	Abstract clusterer.
class	AbstractDensityBasedClusterer	Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
class	Canopy	Cluster data using the capopy clustering algorithm, which requires just one pass over the data.
class	Cobweb	Class implementing the Cobweb and Classit clustering algorithms. Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers.
class	EM	Simple EM (expectation maximisation) class. EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters.
class	FarthestFirst	Cluster data using the FarthestFirst algorithm. For more information see: Hochbaum, Shmoys (1985).
class	FilteredClusterer	Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.
class	HierarchicalClusterer	Hierarchical clustering class.
class	MakeDensityBasedClusterer	Class for wrapping a Clusterer to make it return a distribution and density.
class	RandomizableClusterer	Abstract utility class for handling settings common to randomizable clusterers.
class	RandomizableDensityBasedClusterer	Abstract utility class for handling settings common to randomizable clusterers.
class	RandomizableSingleClustererEnhancer	Abstract utility class for handling settings common to randomizable clusterers.
class	SimpleKMeans	Cluster data using the k means algorithm.
class	SingleClustererEnhancer	Meta-clusterer for enhancing a base clusterer.

Methods in weka.clusterers that return Clusterer

Modifier and Type	Method	Description
static Clusterer	AbstractClusterer.forName(String clustererName, String[] options)	Creates a new instance of a clusterer given it's class name and (optional) arguments to pass to it's setOptions method.
Clusterer	CheckClusterer.getClusterer()	Get the clusterer used as the clusterer
Clusterer	MakeDensityBasedClusterer.getClusterer()	Gets the clusterer being wrapped.
Clusterer	SingleClustererEnhancer.getClusterer()	Get the clusterer used as the base clusterer.
static Clusterer[]	AbstractClusterer.makeCopies(Clusterer model, int num)	Creates copies of the current clusterer.
static Clusterer	AbstractClusterer.makeCopy(Clusterer model)	Creates a deep copy of the given clusterer using serialization.

Methods in weka.clusterers with parameters of type Clusterer

Modifier and Type	Method	Description
static String	ClusterEvaluation.evaluateClusterer(Clusterer clusterer, String[] options)	Evaluates a clusterer with the options given in an array of strings.
static Clusterer[]	AbstractClusterer.makeCopies(Clusterer model, int num)	Creates copies of the current clusterer.
static Clusterer	AbstractClusterer.makeCopy(Clusterer model)	Creates a deep copy of the given clusterer using serialization.
static void	AbstractClusterer.runClusterer(Clusterer clusterer, String[] options)	runs the clusterer instance with the given options.
void	CheckClusterer.setClusterer(Clusterer newClusterer)	Set the clusterer for testing.
void	ClusterEvaluation.setClusterer(Clusterer clusterer)	set the clusterer
void	MakeDensityBasedClusterer.setClusterer(Clusterer toWrap)	Sets the clusterer to wrap.
void	SingleClustererEnhancer.setClusterer(Clusterer value)	Set the base clusterer.

Constructors in weka.clusterers with parameters of type Clusterer

Modifier	Constructor	Description
	MakeDensityBasedClusterer(Clusterer toWrap)	Contructs a MakeDensityBasedClusterer wrapping a given Clusterer.

Uses of Clusterer in weka.filters.unsupervised.attribute

Methods in weka.filters.unsupervised.attribute that return Clusterer

Modifier and Type	Method	Description
Clusterer	AddCluster.getClusterer()	Gets the clusterer used by the filter.

Methods in weka.filters.unsupervised.attribute with parameters of type Clusterer

Modifier and Type	Method	Description
void	AddCluster.setClusterer(Clusterer clusterer)	Sets the clusterer to assign clusters with.

Uses of Clusterer in weka.gui.beans

Methods in weka.gui.beans that return Clusterer

Modifier and Type	Method	Description
Clusterer	BatchClustererEvent.getClusterer()	Get the clusterer
Clusterer	Clusterer.getClusterer()	Get the clusterer currently set for this wrapper

Methods in weka.gui.beans with parameters of type Clusterer

Modifier and Type	Method	Description
void	Clusterer.setClusterer(Clusterer c)	Set the clusterer for this wrapper

Constructors in weka.gui.beans with parameters of type Clusterer

Modifier	Constructor	Description
	BatchClustererEvent(Object source, Clusterer scheme, DataSetEvent tstI, int setNum, int maxSetNum, int testOrTrain)	Creates a new BatchClustererEvent instance.

Uses of Clusterer in weka.gui.explorer

Methods in weka.gui.explorer that return Clusterer

Modifier and Type	Method	Description
Clusterer	ClustererAssignmentsPlotInstances.getClusterer()	Returns the currently set clusterer.

Methods in weka.gui.explorer with parameters of type Clusterer

Modifier and Type	Method	Description
void	ClustererAssignmentsPlotInstances.setClusterer(Clusterer value)	Sets the classifier used for making the predictions.

Uses of Clusterer in weka.knowledgeflow.steps

Methods in weka.knowledgeflow.steps that return Clusterer

Modifier and Type	Method	Description
Clusterer	Clusterer.getClusterer()	Get the clusterer to train
Clusterer	Clusterer.processPrimary(Integer setNum, Integer maxSetNum, Data data, PairedDataHelper<Clusterer> helper)	Process a training split (primary data handled by the PairedDataHelper)

Methods in weka.knowledgeflow.steps with parameters of type Clusterer

Modifier and Type	Method	Description
void	Clusterer.setClusterer(Clusterer clusterer)	Set the clusterer to train

Method parameters in weka.knowledgeflow.steps with type arguments of type Clusterer

Modifier and Type	Method	Description
Clusterer	Clusterer.processPrimary(Integer setNum, Integer maxSetNum, Data data, PairedDataHelper<Clusterer> helper)	Process a training split (primary data handled by the PairedDataHelper)
void	Clusterer.processSecondary(Integer setNum, Integer maxSetNum, Data data, PairedDataHelper<Clusterer> helper)	Process a test split/fold (secondary data handled by PairedDataHelper)