gov.sandia.cognition.learning.algorithm.clustering.divergence

Class ClusterMeanLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.function.distance.DefaultDivergenceFunctionContainer<DataType,DataType>

gov.sandia.cognition.learning.algorithm.clustering.divergence.AbstractClusterToClusterDivergenceFunction<ClusterType,DataType>

gov.sandia.cognition.learning.algorithm.clustering.divergence.ClusterMeanLinkDivergenceFunction<ClusterType,DataType>

Type Parameters:

ClusterType - type of Cluster<DataType> used in the learn() method

DataType - The algorithm operates on a Collection<DataType>, so DataType will be something like Vector or String

All Implemented Interfaces:

ClusterToClusterDivergenceFunction<ClusterType,DataType>, DivergenceFunctionContainer<DataType,DataType>, DivergenceFunction<ClusterType,ClusterType>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

@CodeReview(reviewer="Kevin R. Dixon",
            date="2008-07-23",
            changesNeeded=false,
            comments={"Cleaned up javadoc a little bit with code annotations.","Otherwise, looks fine."})
public class ClusterMeanLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>
extends AbstractClusterToClusterDivergenceFunction<ClusterType,DataType>

The ClusterMeanLinkDivergenceFunction class implements the mean linkage distance metric between two clusters. That is, the value returned by the metric is the mean distance between any point in the first cluster and any point in the second cluster.

Since:

1.0

Author:

Justin Basilico

See Also:

Serialized Form

Field Summary

Fields inherited from class gov.sandia.cognition.learning.function.distance.DefaultDivergenceFunctionContainer

divergenceFunction

Constructor Summary

Constructors

Constructor and Description
ClusterMeanLinkDivergenceFunction() Creates a new instance of ClusterMeanLinkDivergenceFunction.
ClusterMeanLinkDivergenceFunction(DivergenceFunction<? super DataType,? super DataType> divergenceFunction) Creates a new instance of ClusterMeanLinkDivergenceFunction using the given divergence function for elements.

Method Summary

Modifier and Type	Method and Description
double	evaluate(ClusterType from, ClusterType to) The method computes the complete link distance between the two given Clusters.

Methods inherited from class gov.sandia.cognition.learning.function.distance.DefaultDivergenceFunctionContainer

clone, getDivergenceFunction, setDivergenceFunction

Methods inherited from class java.lang.Object

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

Constructor Detail

ClusterMeanLinkDivergenceFunction

public ClusterMeanLinkDivergenceFunction()

Creates a new instance of ClusterMeanLinkDivergenceFunction.

ClusterMeanLinkDivergenceFunction

public ClusterMeanLinkDivergenceFunction(DivergenceFunction<? super DataType,? super DataType> divergenceFunction)

Creates a new instance of ClusterMeanLinkDivergenceFunction using the given divergence function for elements.

Parameters:

divergenceFunction - The divergence function for elements.

Method Detail

evaluate

public double evaluate(ClusterType from,
                       ClusterType to)

The method computes the complete link distance between the two given Clusters. The distance returned is the mean distance between a member of the first cluster and a member of the second cluster.

Parameters:

from - The first Cluster.

to - The second Cluster.

Returns:

The mean link distance between the two given Clusters.