Uses of Interface
weka.clusterers.Clusterer

Packages that use Clusterer

weka.attributeSelection
weka.classifiers.meta
weka.clusterers
weka.filters.unsupervised.attribute
weka.gui.beans

Uses of Clusterer in weka.attributeSelection

Methods in weka.attributeSelection that return Clusterer

abstract Clusterer

UnsupervisedSubsetEvaluator.getClusterer() Get the clusterer

Methods in weka.attributeSelection with parameters of type Clusterer

abstract void

UnsupervisedSubsetEvaluator.setClusterer(Clusterer d) Set the clusterer to use

Uses of Clusterer in weka.classifiers.meta

Methods in weka.classifiers.meta that return Clusterer

Clusterer

ClassificationViaClustering.getClusterer() Get the clusterer used as the base learner.

Methods in weka.classifiers.meta with parameters of type Clusterer

void	ClassificationViaClustering.setClusterer(Clusterer value) Set the base clusterer.

Uses of Clusterer in weka.clusterers

Subinterfaces of Clusterer in weka.clusterers

interface

DensityBasedClusterer Interface for clusterers that can estimate the density for a given instance.

Classes in weka.clusterers that implement Clusterer

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	CLOPE Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional 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	DBScan Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.
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	OPTICS Mihael Ankerst, Markus M.
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	sIB Cluster data using the sequential information bottleneck algorithm. Note: only hard clustering scheme is supported.
class	SimpleKMeans Cluster data using the k means algorithm Valid options are:
class	SingleClustererEnhancer Meta-clusterer for enhancing a base clusterer.
class	XMeans Cluster data using the X-means algorithm. X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region.

Methods in weka.clusterers that return Clusterer

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

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.
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

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

Clusterer

AddCluster.getClusterer() Gets the clusterer used by the filter.

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

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

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

void	Clusterer.setClusterer(Clusterer c) Set the clusterer for this wrapper

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

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