dragon.ir.clustering.clustermodel

Class EuclideanClusterModel

java.lang.Object

dragon.ir.clustering.clustermodel.AbstractClusterModel

dragon.ir.clustering.clustermodel.EuclideanClusterModel

All Implemented Interfaces:

ClusterModel

public class EuclideanClusterModel
extends AbstractClusterModel

Cluster model which computes Euclidean distance between a document and a cluster

Copyright: Copyright (c) 2005

Company: IST, Drexel University

Version:

1.0

Author:

Davis Zhou

Field Summary

Fields inherited from class dragon.ir.clustering.clustermodel.AbstractClusterModel

clusterNum, featureFilter

Constructor Summary

Constructors

Constructor and Description
EuclideanClusterModel(int clusterNum, SparseMatrix docTerm)

Method Summary

Modifier and Type	Method and Description
double	getDistance(IRDoc doc, int clusterID)
void	setClusterNum(int clusterNum)
void	setDocCluster(DocCluster cluster) Iterative partional approaches (e.g.

Methods inherited from class dragon.ir.clustering.clustermodel.AbstractClusterModel

getClusterNum, getDistance, getFeatureFilter, setDocClusters, setFeatureFilter

Methods inherited from class java.lang.Object

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

Constructor Detail

EuclideanClusterModel

public EuclideanClusterModel(int clusterNum,
                             SparseMatrix docTerm)

Method Detail

setClusterNum

public void setClusterNum(int clusterNum)

Parameters:

clusterNum - the number of clusters

setDocCluster

public void setDocCluster(DocCluster cluster)

Description copied from interface: ClusterModel

Iterative partional approaches (e.g. K-Means) need to re-compute the cetroid of each cluster after each iteration. This method gives the chance to re-compute the centroid.

Parameters:

cluster - the new cluster

getDistance

public double getDistance(IRDoc doc,
                          int clusterID)