moa.clusterers.clustream

Class WithKmeans

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.clusterers.AbstractClusterer

moa.clusterers.clustream.WithKmeans

All Implemented Interfaces:

Serializable, Clusterer, AWTRenderable, MOAObject, OptionHandler

public class WithKmeans
extends AbstractClusterer

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
IntOption	kernelRadiFactorOption
IntOption	kOption
IntOption	maxNumKernelsOption
IntOption	timeWindowOption

Fields inherited from class moa.clusterers.AbstractClusterer

clustererRandom, clustering, evaluateMicroClusteringOption, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel

Fields inherited from class moa.options.AbstractOptionHandler

classOptionNamesToPreparedObjects, options

Constructor Summary

Constructors

Constructor and Description
WithKmeans()

Method Summary

Methods

Modifier and Type	Method and Description
protected static Clustering	cleanUpKMeans(Clustering kMeansResult, ArrayList<CFCluster> microclusters) Rearrange the k-means result into a set of CFClusters, cleaning up the redundancies.
Clustering	getClusteringResult()
Clustering	getClusteringResult(Clustering gtClustering)
Clustering	getMicroClusteringResult()
void	getModelDescription(StringBuilder out, int indent)
protected Measurement[]	getModelMeasurementsImpl()
String	getName()
double[]	getVotesForInstance(weka.core.Instance inst)
boolean	implementsMicroClusterer() Miscellaneous
boolean	isRandomizable()
static Clustering	kMeans_gta(int k, Clustering clustering, Clustering gtClustering) k-means of (micro)clusters, with ground-truth-aided initialization.
static Clustering	kMeans_rand(int k, Clustering clustering) k-means of (micro)clusters, with randomized initialization.
protected static Clustering	kMeans(int k, Cluster[] centers, List<? extends Cluster> data) (The Actual Algorithm) k-means of (micro)clusters, with specified initialization points.
void	resetLearningImpl()
void	trainOnInstanceImpl(weka.core.Instance instance)

Methods inherited from class moa.clusterers.AbstractClusterer

contextIsCompatible, copy, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModelContext, getModelMeasurements, getNominalValueString, getPurposeString, getSubClusterers, keepClassLabel, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance

Methods inherited from class moa.options.AbstractOptionHandler

discoverOptionsViaReflection, getCLICreationString, getOptions, getPreparedClassOption, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse

Methods inherited from class moa.AbstractMOAObject

copy, measureByteSize, measureByteSize, toString

Methods inherited from class java.lang.Object

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

Methods inherited from interface moa.options.OptionHandler

getCLICreationString, getOptions, prepareForUse, prepareForUse

Methods inherited from interface moa.MOAObject

measureByteSize

Field Detail

timeWindowOption

public IntOption timeWindowOption

maxNumKernelsOption

public IntOption maxNumKernelsOption

kernelRadiFactorOption

public IntOption kernelRadiFactorOption

kOption

public IntOption kOption

Constructor Detail

WithKmeans

public WithKmeans()

Method Detail

resetLearningImpl

public void resetLearningImpl()

Specified by:

resetLearningImpl in class AbstractClusterer

trainOnInstanceImpl

public void trainOnInstanceImpl(weka.core.Instance instance)

Specified by:

trainOnInstanceImpl in class AbstractClusterer

getMicroClusteringResult

public Clustering getMicroClusteringResult()

Specified by:

getMicroClusteringResult in interface Clusterer

Overrides:

getMicroClusteringResult in class AbstractClusterer

getClusteringResult

public Clustering getClusteringResult()

getClusteringResult

public Clustering getClusteringResult(Clustering gtClustering)

getName

public String getName()

kMeans_gta

public static Clustering kMeans_gta(int k,
                    Clustering clustering,
                    Clustering gtClustering)

k-means of (micro)clusters, with ground-truth-aided initialization. (to produce best results)

Parameters:

k -

data -

Returns:

(macro)clustering - CFClusters

kMeans_rand

public static Clustering kMeans_rand(int k,
                     Clustering clustering)

k-means of (micro)clusters, with randomized initialization.

Parameters:

k -

data -

Returns:

(macro)clustering - CFClusters

kMeans

protected static Clustering kMeans(int k,
                Cluster[] centers,
                List<? extends Cluster> data)

(The Actual Algorithm) k-means of (micro)clusters, with specified initialization points.

Parameters:

k -

centers - - initial centers

data -

Returns:

(macro)clustering - SphereClusters

cleanUpKMeans

protected static Clustering cleanUpKMeans(Clustering kMeansResult,
                       ArrayList<CFCluster> microclusters)

Rearrange the k-means result into a set of CFClusters, cleaning up the redundancies.

Parameters:

kMeansResult -

microclusters -

Returns:

implementsMicroClusterer

public boolean implementsMicroClusterer()

Miscellaneous

Specified by:

implementsMicroClusterer in interface Clusterer

Overrides:

implementsMicroClusterer in class AbstractClusterer

isRandomizable

public boolean isRandomizable()

getVotesForInstance

public double[] getVotesForInstance(weka.core.Instance inst)

getModelMeasurementsImpl

protected Measurement[] getModelMeasurementsImpl()

Specified by:

getModelMeasurementsImpl in class AbstractClusterer

getModelDescription

public void getModelDescription(StringBuilder out,
                       int indent)

Specified by:

getModelDescription in class AbstractClusterer