gov.sandia.cognition.learning.algorithm.clustering

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

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm<ResultType>

gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner<java.util.Collection<? extends DataType>,java.util.Collection<ClusterType>>

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

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

Type Parameters:

DataType - The type of the data to cluster. This is typically defined by the divergence function used.

ClusterType - The type of Cluster created by the algorithm. This is typically defined by the cluster creator function used.

All Implemented Interfaces:

AnytimeAlgorithm<java.util.Collection<ClusterType>>, IterativeAlgorithm, MeasurablePerformanceAlgorithm, StoppableAlgorithm, AnytimeBatchLearner<java.util.Collection<? extends DataType>,java.util.Collection<ClusterType>>, BatchLearner<java.util.Collection<? extends DataType>,java.util.Collection<ClusterType>>, BatchClusterer<DataType,ClusterType>, DivergenceFunctionContainer<ClusterType,DataType>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

@CodeReview(reviewer="Kevin R. Dixon",
            date="2008-07-22",
            changesNeeded=false,
            comments={"Made setRemovalThreshold check to ensure removalThreshold is < 1.0","Cleaned up javadoc.","Code generally looks fine."})
public class KMeansClustererWithRemoval<DataType,ClusterType extends Cluster<DataType>>
extends KMeansClusterer<DataType,ClusterType>

Creates a k-means clustering algorithm that removes clusters that do not have sufficient membership to pass a simple statistical significance test.

Since:

1.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Field Summary

Fields inherited from class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer

assignments, clusterCounts, clusters, DEFAULT_MAX_ITERATIONS, DEFAULT_NUM_REQUESTED_CLUSTERS, divergenceFunction, initializer, numRequestedClusters

Fields inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

data, keepGoing

Fields inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

maxIterations

Fields inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

DEFAULT_ITERATION, iteration

Constructor Summary

Constructors

Constructor and Description
KMeansClustererWithRemoval() Default constructor
KMeansClustererWithRemoval(int numRequestedClusters, int maxIterations, FixedClusterInitializer<ClusterType,DataType> initializer, ClusterDivergenceFunction<ClusterType,DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator, double removalThreshold) Creates a new instance of KMeansClusterer using the given parameters.

Method Summary

Modifier and Type	Method and Description
double	getRemovalThreshold() Getter for removalThreshold
protected void	removeCluster(int clusterIndex) Removes the cluster at the specified index, and does the internal bookkeeping as well
void	setRemovalThreshold(double removalThreshold) Setter for removalThreshold
protected boolean	step() Do a step of the clustering algorithm.

Methods inherited from class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer

assignDataFromIndices, assignDataToClusters, cleanupAlgorithm, clone, createClustersFromAssignments, getAssignments, getClosestClusterIndex, getCluster, getClusterCounts, getClusters, getCreator, getDivergenceFunction, getInitializer, getNumChanged, getNumClusters, getNumElements, getNumRequestedClusters, getPerformance, getResult, initializeAlgorithm, setAssignment, setClusters, setCreator, setData, setDivergenceFunction, setInitializer, setNumChanged, setNumRequestedClusters

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

getData, getKeepGoing, learn, setKeepGoing, stop

Methods inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

getMaxIterations, isResultValid, setMaxIterations

Methods inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

addIterativeAlgorithmListener, fireAlgorithmEnded, fireAlgorithmStarted, fireStepEnded, fireStepStarted, getIteration, getListeners, removeIterativeAlgorithmListener, setIteration, setListeners

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner

learn

Methods inherited from interface gov.sandia.cognition.algorithm.AnytimeAlgorithm

getMaxIterations, setMaxIterations

Methods inherited from interface gov.sandia.cognition.algorithm.IterativeAlgorithm

addIterativeAlgorithmListener, getIteration, removeIterativeAlgorithmListener

Methods inherited from interface gov.sandia.cognition.algorithm.StoppableAlgorithm

isResultValid

Constructor Detail

KMeansClustererWithRemoval

public KMeansClustererWithRemoval()

Default constructor

KMeansClustererWithRemoval

public KMeansClustererWithRemoval(int numRequestedClusters,
                                  int maxIterations,
                                  FixedClusterInitializer<ClusterType,DataType> initializer,
                                  ClusterDivergenceFunction<ClusterType,DataType> divergenceFunction,
                                  ClusterCreator<ClusterType,DataType> creator,
                                  double removalThreshold)

Creates a new instance of KMeansClusterer using the given parameters.

Parameters:

numRequestedClusters - The number of clusters requested (k).

maxIterations - Number of iterations before stopping

initializer - The initializer for the clusters.

divergenceFunction - The divergence function.

creator - The cluster creator.

removalThreshold - fraction of the expected number of data points assigned to a cluster below which the cluster will be removed. (Suppose there are 1000 datapoint, 10 clusters, and removalThreshold=0.1. A cluster may be removed only if is has membership less than 0.1*1000/10= 10 elements assigned to it.)

Method Detail

getRemovalThreshold

public double getRemovalThreshold()

Getter for removalThreshold

Returns:

fraction of the expected number of data points assigned to a cluster below which the cluster will be removed. (Suppose there are 1000 datapoint, 10 clusters, and removalThreshold=0.1. A cluster may be removed only if is has membership less than 0.1*1000/10= 10 elements assigned to it.)

setRemovalThreshold

public void setRemovalThreshold(double removalThreshold)

Setter for removalThreshold

Parameters:

removalThreshold - fraction of the expected number of data points assigned to a cluster below which the cluster will be removed. (Suppose there are 1000 datapoint, 10 clusters, and removalThreshold=0.1. A cluster may be removed only if is has membership less than 0.1*1000/10= 10 elements assigned to it.) Must be less than 1.0.

removeCluster

protected void removeCluster(int clusterIndex)

Removes the cluster at the specified index, and does the internal bookkeeping as well

Parameters:

clusterIndex - zero-based cluster index to remove

step

protected boolean step()

Description copied from class: KMeansClusterer

Do a step of the clustering algorithm.

Overrides:

step in class KMeansClusterer<DataType,ClusterType extends Cluster<DataType>>

Returns:

true means keep going, false means stop clustering.