weka.clusterers

Interface Clusterer

All Known Subinterfaces:

DensityBasedClusterer

All Known Implementing Classes:

AbstractClusterer, AbstractDensityBasedClusterer, CLOPE, Cobweb, DBSCAN, EM, FarthestFirst, FilteredClusterer, HierarchicalClusterer, MakeDensityBasedClusterer, OPTICS, RandomizableClusterer, RandomizableDensityBasedClusterer, RandomizableSingleClustererEnhancer, sIB, SimpleKMeans, SingleClustererEnhancer, XMeans

public interface Clusterer

Interface for clusterers. Clients will typically extend either AbstractClusterer or AbstractDensityBasedClusterer.

Author:

Mark Hall (mhall@cs.waikato.ac.nz)

Method Summary

Modifier and Type	Method and Description
void	buildClusterer(Instances data) Generates a clusterer.
int	clusterInstance(Instance instance) Classifies a given instance.
double[]	distributionForInstance(Instance instance) Predicts the cluster memberships for a given instance.
Capabilities	getCapabilities() Returns the Capabilities of this clusterer.
int	numberOfClusters() Returns the number of clusters.

Method Detail

buildClusterer

void buildClusterer(Instances data)
             throws java.lang.Exception

Generates a clusterer. Has to initialize all fields of the clusterer that are not being set via options.

Parameters:

data - set of instances serving as training data

Throws:

java.lang.Exception - if the clusterer has not been generated successfully

clusterInstance

int clusterInstance(Instance instance)
             throws java.lang.Exception

Classifies a given instance. Either this or distributionForInstance() needs to be implemented by subclasses.

Parameters:

instance - the instance to be assigned to a cluster

Returns:

the number of the assigned cluster as an integer

Throws:

java.lang.Exception - if instance could not be clustered successfully

distributionForInstance

double[] distributionForInstance(Instance instance)
                          throws java.lang.Exception

Predicts the cluster memberships for a given instance. Either this or clusterInstance() needs to be implemented by subclasses.

Parameters:

instance - the instance to be assigned a cluster.

Returns:

an array containing the estimated membership probabilities of the test instance in each cluster (this should sum to at most 1)

Throws:

java.lang.Exception - if distribution could not be computed successfully

numberOfClusters

int numberOfClusters()
              throws java.lang.Exception

Returns the number of clusters.

Returns:

the number of clusters generated for a training dataset.

Throws:

java.lang.Exception - if number of clusters could not be returned successfully

getCapabilities

Capabilities getCapabilities()

Returns the Capabilities of this clusterer. Derived classifiers have to override this method to enable capabilities.

Returns:

the capabilities of this object

See Also:

Capabilities