weka.clusterers
Class Clusterer

java.lang.Object
  |
  +--weka.clusterers.Clusterer

All Implemented Interfaces:

java.lang.Cloneable, java.io.Serializable

Direct Known Subclasses:

Cobweb, DensityBasedClusterer, FarthestFirst, SimpleKMeans

public abstract class Clusterer

extends java.lang.Object

implements java.lang.Cloneable, java.io.Serializable

Abstract clusterer.

Version:

$Revision: 1.10 $

Author:

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

See Also:

Serialized Form

Constructor Summary

Clusterer()

Method Summary

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(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[]	makeCopies(Clusterer model, int num) Creates copies of the current clusterer.
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

Clusterer

public Clusterer()

Method Detail

buildClusterer

public abstract 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

public 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

public 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

public abstract 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

forName

public static Clusterer forName(java.lang.String clustererName,
                                java.lang.String[] options)
                         throws java.lang.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:

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

Returns:

the newly created search object, ready for use.

Throws:

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

makeCopies

public static Clusterer[] makeCopies(Clusterer model,
                                     int num)
                              throws java.lang.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:

java.lang.Exception - if an error occurs