Uses of Class
weka.clusterers.Clusterer

Packages that use Clusterer

weka.clusterers
weka.filters.unsupervised.attribute
weka.gui.explorer

Uses of Clusterer in weka.clusterers

Subclasses of Clusterer in weka.clusterers

class	Cobweb Class implementing the Cobweb and Classit clustering algorithms.
class	DensityBasedClusterer Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie. a probability distribution).
class	EM Simple EM (expectation maximisation) class.
class	FarthestFirst Implements the "Farthest First Traversal Algorithm" by Hochbaum and Shmoys 1985: A best possible heuristic for the k-center problem, Mathematics of Operations Research, 10(2):180-184, as cited by Sanjoy Dasgupta "performance guarantees for hierarchical clustering", colt 2002, sydney works as a fast simple approximate clusterer modelled after SimpleKMeans, might be a useful initializer for it Valid options are: -N Specify the number of clusters to generate.
class	MakeDensityBasedClusterer Class for wrapping a Clusterer to make it return a distribution and density.
class	SimpleKMeans Simple k means clustering class.

Fields in weka.clusterers declared as Clusterer

private Clusterer	MakeDensityBasedClusterer.m_wrappedClusterer The clusterer being wrapped
private Clusterer	ClusterEvaluation.m_Clusterer the clusterer

Methods in weka.clusterers that return Clusterer

Clusterer	MakeDensityBasedClusterer.getClusterer() Gets the clusterer being wrapped.
static Clusterer	Clusterer.forName(java.lang.String clustererName, java.lang.String[] options) Creates a new instance of a clusterer given it's class name and (optional) arguments to pass to it's setOptions method.
static Clusterer[]	Clusterer.makeCopies(Clusterer model, int num) Creates copies of the current clusterer.

Methods in weka.clusterers with parameters of type Clusterer

void	MakeDensityBasedClusterer.setClusterer(Clusterer toWrap) Sets the clusterer to wrap.
void	ClusterEvaluation.setClusterer(Clusterer clusterer) set the clusterer
static java.lang.String	ClusterEvaluation.evaluateClusterer(Clusterer clusterer, java.lang.String[] options) Evaluates a clusterer with the options given in an array of strings.
private static java.lang.String	ClusterEvaluation.printClusterStats(Clusterer clusterer, java.lang.String fileName) Print the cluster statistics for either the training or the testing data.
private static java.lang.String	ClusterEvaluation.printClusterings(Clusterer clusterer, Instances train, java.lang.String testFileName, Range attributesToOutput) Print the cluster assignments for either the training or the testing data.
private static java.lang.String	ClusterEvaluation.makeOptionString(Clusterer clusterer) Make up the help string giving all the command line options
static Clusterer[]	Clusterer.makeCopies(Clusterer model, int num) Creates copies of the current 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

Fields in weka.filters.unsupervised.attribute declared as Clusterer

protected Clusterer

AddCluster.m_Clusterer The clusterer used to do the cleansing

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.explorer

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

protected void	ClustererPanel.saveClusterer(java.lang.String name, Clusterer clusterer, Instances trainHeader, int[] ignoredAtts) Saves the currently selected clusterer
protected void	ClustererPanel.reevaluateModel(java.lang.String name, Clusterer clusterer, Instances trainHeader, int[] ignoredAtts) Re-evaluates the named clusterer with the current test set.