Package moa.clusterers.clustream
Class WithKmeans
- java.lang.Object
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- moa.AbstractMOAObject
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- moa.options.AbstractOptionHandler
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- moa.clusterers.AbstractClusterer
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- moa.clusterers.clustream.WithKmeans
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- All Implemented Interfaces:
Configurable
,Serializable
,Clusterer
,AWTRenderable
,MOAObject
,OptionHandler
public class WithKmeans extends AbstractClusterer
- See Also:
- Serialized Form
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Field Summary
Fields Modifier and Type Field Description IntOption
kernelRadiFactorOption
IntOption
kOption
IntOption
maxNumKernelsOption
IntOption
timeWindowOption
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Fields inherited from class moa.clusterers.AbstractClusterer
clustererRandom, clustering, evaluateMicroClusteringOption, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel
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Fields inherited from class moa.options.AbstractOptionHandler
config
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Constructor Summary
Constructors Constructor Description WithKmeans()
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
adjustParameters()
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(Instance inst)
boolean
implementsMicroClusterer()
Miscellaneousboolean
isRandomizable()
protected static Clustering
kMeans(int k, Cluster[] centers, List<? extends Cluster> data)
(The Actual Algorithm) k-means of (micro)clusters, with specified initialization points.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.void
resetLearningImpl()
void
trainOnInstanceImpl(Instance instance)
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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
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Methods inherited from class moa.options.AbstractOptionHandler
getCLICreationString, getOptions, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse
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Methods inherited from class moa.AbstractMOAObject
copy, measureByteSize, measureByteSize, toString
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Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
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Methods inherited from interface moa.MOAObject
measureByteSize
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Methods inherited from interface moa.options.OptionHandler
getCLICreationString, getOptions, prepareForUse, prepareForUse
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Method Detail
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resetLearningImpl
public void resetLearningImpl()
- Specified by:
resetLearningImpl
in classAbstractClusterer
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trainOnInstanceImpl
public void trainOnInstanceImpl(Instance instance)
- Specified by:
trainOnInstanceImpl
in classAbstractClusterer
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getMicroClusteringResult
public Clustering getMicroClusteringResult()
- Specified by:
getMicroClusteringResult
in interfaceClusterer
- Overrides:
getMicroClusteringResult
in classAbstractClusterer
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getClusteringResult
public Clustering getClusteringResult()
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getClusteringResult
public Clustering getClusteringResult(Clustering gtClustering)
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getName
public String getName()
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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
-clustering
-- Returns:
- (macro)clustering - CFClusters
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kMeans_rand
public static Clustering kMeans_rand(int k, Clustering clustering)
k-means of (micro)clusters, with randomized initialization.- Parameters:
k
-clustering
-- Returns:
- (macro)clustering - CFClusters
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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 centersdata
-- Returns:
- (macro)clustering - SphereClusters
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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:
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implementsMicroClusterer
public boolean implementsMicroClusterer()
Miscellaneous- Specified by:
implementsMicroClusterer
in interfaceClusterer
- Overrides:
implementsMicroClusterer
in classAbstractClusterer
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isRandomizable
public boolean isRandomizable()
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getVotesForInstance
public double[] getVotesForInstance(Instance inst)
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getModelMeasurementsImpl
protected Measurement[] getModelMeasurementsImpl()
- Specified by:
getModelMeasurementsImpl
in classAbstractClusterer
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getModelDescription
public void getModelDescription(StringBuilder out, int indent)
- Specified by:
getModelDescription
in classAbstractClusterer
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adjustParameters
public void adjustParameters()
- Overrides:
adjustParameters
in classAbstractClusterer
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