dragon.ir.clustering

Class BasicKMean

java.lang.Object

dragon.ir.clustering.AbstractClustering

dragon.ir.clustering.BasicKMean

All Implemented Interfaces:

Clustering

public class BasicKMean
extends AbstractClustering

Basic KMeans clustering

Copyright: Copyright (c) 2005

Company: IST, Drexel University

Version:

1.0

Author:

Davis Zhou

Field Summary

Fields

Modifier and Type	Field and Description
protected ClusterModel	distMetric
protected boolean	initialized
protected int	maxIteration

Fields inherited from class dragon.ir.clustering.AbstractClustering

clusterNum, clusterSet, featureFilter, indexReader, randomSeed, showProgress

Constructor Summary

Constructors

Constructor and Description
BasicKMean(IndexReader indexReader, ClusterModel distMetric, DocClusterSet initClusterSet)
BasicKMean(IndexReader indexReader, ClusterModel distMetric, int clusterNum)
BasicKMean(IndexReader indexReader, ClusterModel distMetric, int clusterNum, boolean initAllObjs)

Method Summary

Modifier and Type	Method and Description
boolean	cluster(IRDoc[] arrDoc) Cluster given testing documents to the given number of clusters
int	getMaxIteration()
boolean	getUseAllObjectForInitialization()
protected boolean	initialize(IRDoc[] arrDoc)
void	setMaxIteration(int iteration)
void	setUseAllObjectForInitialization(boolean option) If this option is true, the algorithm randomly assign all objects to the given number of clusters during initialization.

Methods inherited from class dragon.ir.clustering.AbstractClustering

cluster, getCluster, getClusterNum, getClusterSet, getFeatureFilter, getIndexReader, getRandomSeed, setFeatureFilter, setRandomSeed, setShowProgress

Methods inherited from class java.lang.Object

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

Field Detail

distMetric

protected ClusterModel distMetric

initialized

protected boolean initialized

maxIteration

protected int maxIteration

Constructor Detail

BasicKMean

public BasicKMean(IndexReader indexReader,
                  ClusterModel distMetric,
                  int clusterNum)

BasicKMean

public BasicKMean(IndexReader indexReader,
                  ClusterModel distMetric,
                  int clusterNum,
                  boolean initAllObjs)

BasicKMean

public BasicKMean(IndexReader indexReader,
                  ClusterModel distMetric,
                  DocClusterSet initClusterSet)

Method Detail

setUseAllObjectForInitialization

public void setUseAllObjectForInitialization(boolean option)

If this option is true, the algorithm randomly assign all objects to the given number of clusters during initialization. Otherwise, it picks only one object for each cluster during initialization.

Parameters:

option - whether use all objects for initialization

getUseAllObjectForInitialization

public boolean getUseAllObjectForInitialization()

initialize

protected boolean initialize(IRDoc[] arrDoc)

cluster

public boolean cluster(IRDoc[] arrDoc)

Description copied from interface: Clustering

Cluster given testing documents to the given number of clusters

Parameters:

arrDoc - the document set for clustering

Returns:

true if clustering successfully

getMaxIteration

public int getMaxIteration()

setMaxIteration

public void setMaxIteration(int iteration)