weka.clusterers

Class AbstractClusterer

java.lang.Object

weka.clusterers.AbstractClusterer

All Implemented Interfaces:

Serializable, Cloneable, Clusterer, CapabilitiesHandler, RevisionHandler

Direct Known Subclasses:

AbstractDensityBasedClusterer, CLOPE, DBSCAN, HierarchicalClusterer, OPTICS, RandomizableClusterer, RandomizableSingleClustererEnhancer, SingleClustererEnhancer

public abstract class AbstractClusterer
extends Object
implements Clusterer, Cloneable, Serializable, CapabilitiesHandler, RevisionHandler

Abstract clusterer.

Version:

$Revision: 5537 $

Author:

Mark Hall ([email protected])

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
AbstractClusterer()

Method Summary

Methods

Modifier and Type	Method and Description
abstract 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.
static Clusterer	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.
Capabilities	getCapabilities() Returns the Capabilities of this clusterer.
String	getRevision() Returns the revision string.
static Clusterer[]	makeCopies(Clusterer model, int num) Creates copies of the current clusterer.
static Clusterer	makeCopy(Clusterer model) Creates a deep copy of the given clusterer using serialization.
abstract int	numberOfClusters() Returns the number of clusters.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

AbstractClusterer

public AbstractClusterer()

Method Detail

buildClusterer

public abstract void buildClusterer(Instances data)
                             throws Exception

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

Specified by:

buildClusterer in interface Clusterer

Parameters:

data - set of instances serving as training data

Throws:

Exception - if the clusterer has not been generated successfully

clusterInstance

public int clusterInstance(Instance instance)
                    throws Exception

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

Specified by:

clusterInstance in interface Clusterer

Parameters:

instance - the instance to be assigned to a cluster

Returns:

the number of the assigned cluster as an integer

Throws:

Exception - if instance could not be clustered successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws Exception

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

Specified by:

distributionForInstance in interface Clusterer

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:

Exception - if distribution could not be computed successfully

numberOfClusters

public abstract int numberOfClusters()
                              throws Exception

Returns the number of clusters.

Specified by:

numberOfClusters in interface Clusterer

Returns:

the number of clusters generated for a training dataset.

Throws:

Exception - if number of clusters could not be returned successfully

forName

public static Clusterer forName(String clustererName,
                String[] options)
                         throws Exception

Creates a new instance of a clusterer given it's class name and (optional) arguments to pass to it's setOptions method. If the clusterer implements OptionHandler and the options parameter is non-null, the clusterer will have it's options set.

Parameters:

clustererName - the fully qualified class name of the clusterer

options - an array of options suitable for passing to setOptions. May be null.

Returns:

the newly created search object, ready for use.

Throws:

Exception - if the clusterer class name is invalid, or the options supplied are not acceptable to the clusterer.

makeCopy

public static Clusterer makeCopy(Clusterer model)
                          throws Exception

Creates a deep copy of the given clusterer using serialization.

Parameters:

model - the clusterer to copy

Returns:

a deep copy of the clusterer

Throws:

Exception - if an error occurs

makeCopies

public static Clusterer[] makeCopies(Clusterer model,
                     int num)
                              throws Exception

Creates copies of the current clusterer. Note that this method now uses Serialization to perform a deep copy, so the Clusterer object must be fully Serializable. Any currently built model will now be copied as well.

Parameters:

model - an example clusterer to copy

num - the number of clusterer copies to create.

Returns:

an array of clusterers.

Throws:

Exception - if an error occurs

getCapabilities

public Capabilities getCapabilities()

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

Specified by:

getCapabilities in interface Clusterer

Specified by:

getCapabilities in interface CapabilitiesHandler

Returns:

the capabilities of this object

See Also:

Capabilities

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Returns:

the revision