gov.sandia.cognition.learning.algorithm.clustering.initializer

Class GreedyClusterInitializer<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.initializer.AbstractMinDistanceFixedClusterInitializer<ClusterType,DataType>

gov.sandia.cognition.learning.algorithm.clustering.initializer.GreedyClusterInitializer<ClusterType,DataType>

Type Parameters:

ClusterType - Type of Cluster<DataType> used in theaceous learn() method.

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

All Implemented Interfaces:

FixedClusterInitializer<ClusterType,DataType>, DivergenceFunctionContainer<DataType,DataType>, CloneableSerializable, Randomized, java.io.Serializable, java.lang.Cloneable

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

Implements a FixedClusterInitializer that greedily attempts to create the initial clusters. The algorithm works by first selecting a random member to be the first cluster and then successively finding the member that is furthest from the selected clusters. This method of initialization can work well with the K-means algorithm.

Since:

1.0

Author:

Justin Basilico, Kevin R. Dixon

See Also:

Serialized Form

Field Summary

Fields inherited from class gov.sandia.cognition.learning.algorithm.clustering.initializer.AbstractMinDistanceFixedClusterInitializer

creator, random

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

divergenceFunction

Constructor Summary

Constructors

Constructor and Description
GreedyClusterInitializer() Creates a new, empty instance of GreedyClusterInitializer.
GreedyClusterInitializer(DivergenceFunction<? super DataType,? super DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator, java.util.Random random) Creates a new instance of GreedyClusterInitializer.

Method Summary

Modifier and Type	Method and Description
GreedyClusterInitializer<ClusterType,DataType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
protected int	selectNextClusterIndex(double[] minDistances, boolean[] selected) Select the index for the next cluster based on the given minimum distances and array indicating which clusters have already been selected.

Methods inherited from class gov.sandia.cognition.learning.algorithm.clustering.initializer.AbstractMinDistanceFixedClusterInitializer

getCreator, getRandom, initializeClusters, setCreator, setRandom

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

getDivergenceFunction, setDivergenceFunction

Methods inherited from class java.lang.Object

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

Constructor Detail

GreedyClusterInitializer

public GreedyClusterInitializer()

Creates a new, empty instance of GreedyClusterInitializer.

GreedyClusterInitializer

public GreedyClusterInitializer(DivergenceFunction<? super DataType,? super DataType> divergenceFunction,
                                ClusterCreator<ClusterType,DataType> creator,
                                java.util.Random random)

Creates a new instance of GreedyClusterInitializer.

Parameters:

divergenceFunction - The divergence function to use.

creator - The cluster creator to use.

random - The random number generator to use.

Method Detail

clone

public GreedyClusterInitializer<ClusterType,DataType> clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractMinDistanceFixedClusterInitializer<ClusterType extends Cluster<DataType>,DataType>

Returns:

A clone of this object.

selectNextClusterIndex

protected int selectNextClusterIndex(double[] minDistances,
                                     boolean[] selected)

Description copied from class: AbstractMinDistanceFixedClusterInitializer

Select the index for the next cluster based on the given minimum distances and array indicating which clusters have already been selected.

Specified by:

selectNextClusterIndex in class AbstractMinDistanceFixedClusterInitializer<ClusterType extends Cluster<DataType>,DataType>

Parameters:

minDistances - The array of minimum distances.

selected - The array corresponding to whether or not an item has already been selected.

Returns:

The index of the next cluster to include. -1 means that there is nothing left to include.