
V - vector datatypeD - distance value typepublic class KMeansHybridLloydMacQueen<V extends NumberVector<?>,D extends Distance<D>> extends AbstractKMeans<V,D,KMeansModel<V>>
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static class |
KMeansHybridLloydMacQueen.Parameterizer<V extends NumberVector<?>,D extends Distance<D>>
Parameterization class.
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| Modifier and Type | Field and Description |
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private static Logging |
LOG
The logger for this class.
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initializer, k, maxiterdistanceFunctionINIT_ID, K_ID, MAXITER_ID, SEED_IDDISTANCE_FUNCTION_ID| Constructor and Description |
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KMeansHybridLloydMacQueen(PrimitiveDistanceFunction<NumberVector<?>,D> distanceFunction,
int k,
int maxiter,
KMeansInitialization<V> initializer)
Constructor.
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| Modifier and Type | Method and Description |
|---|---|
protected Logging |
getLogger()
Get the (STATIC) logger for this class.
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Clustering<KMeansModel<V>> |
run(Database database,
Relation<V> relation)
Run the clustering algorithm.
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assignToNearestCluster, getInputTypeRestriction, incrementalUpdateMean, macQueenIterate, means, medians, setDistanceFunction, setK, updateAssignmentgetDistanceFunctionmakeParameterDistanceFunction, runclone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitrungetDistanceFunctionprivate static final Logging LOG
public KMeansHybridLloydMacQueen(PrimitiveDistanceFunction<NumberVector<?>,D> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer)
distanceFunction - distance functionk - k parametermaxiter - Maxiter parameterinitializer - Initialization methodpublic Clustering<KMeansModel<V>> run(Database database, Relation<V> relation)
KMeansdatabase - Database to run on.relation - Relation to process.protected Logging getLogger()
AbstractAlgorithmgetLogger in class AbstractAlgorithm<Clustering<KMeansModel<V extends NumberVector<?>>>>