Uses of Class
de.lmu.ifi.dbs.elki.data.model.MeanModel

Packages that use MeanModel

Package	Description
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality	Quality measures for k-Means results.
de.lmu.ifi.dbs.elki.data.model	Cluster models classes for various algorithms.
de.lmu.ifi.dbs.elki.visualization.visualizers.parallel.cluster	Visualizers for clustering results based on parallel coordinates.
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster	Visualizers for clustering results based on 2D projections.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.

Uses of MeanModel in de.lmu.ifi.dbs.elki.algorithm.clustering

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering that return types with arguments of type MeanModel

Modifier and Type	Method and Description
Clustering<MeanModel<V>>	NaiveMeanShiftClustering.run(Database database, Relation<V> relation) Run the mean-shift clustering algorithm.

Uses of MeanModel in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with type parameters of type MeanModel

Modifier and Type	Class and Description
class	AbstractKMeans<V extends NumberVector<?>,D extends Distance<D>,M extends MeanModel<V>> Abstract base class for k-means implementations.
class	BestOfMultipleKMeans<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> Run K-Means multiple times, and keep the best run.
static class	BestOfMultipleKMeans.Parameterizer<V extends NumberVector<?>,D extends Distance<D>,M extends MeanModel<V>> Parameterization class.
interface	KMeans<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> Some constants and options shared among kmeans family algorithms.
class	KMeansBisecting<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> The bisecting k-means algorithm works by starting with an initial partitioning into two clusters, then repeated splitting of the largest cluster to get additional clusters.
static class	KMeansBisecting.Parameterizer<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> Parameterization class.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans that return types with arguments of type MeanModel

Modifier and Type	Method and Description
Clustering<MeanModel<V>>	KMediansLloyd.run(Database database, Relation<V> relation)

Uses of MeanModel in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality with type arguments of type MeanModel

Modifier and Type	Method and Description
<V extends O> double	KMeansQualityMeasure.calculateCost(Clustering<? extends MeanModel<V>> clustering, PrimitiveDistanceFunction<? super V,? extends D> distanceFunction, Relation<V> relation) Calculates and returns the quality measure.
<V extends NumberVector<?>> double	WithinClusterVarianceQualityMeasure.calculateCost(Clustering<? extends MeanModel<V>> clustering, PrimitiveDistanceFunction<? super V,? extends NumberDistance<?,?>> distanceFunction, Relation<V> relation)
<V extends NumberVector<?>> double	WithinClusterMeanDistanceQualityMeasure.calculateCost(Clustering<? extends MeanModel<V>> clustering, PrimitiveDistanceFunction<? super V,? extends NumberDistance<?,?>> distanceFunction, Relation<V> relation)

Uses of MeanModel in de.lmu.ifi.dbs.elki.data.model

Subclasses of MeanModel in de.lmu.ifi.dbs.elki.data.model

Modifier and Type	Class and Description
class	EMModel<V extends FeatureVector<?>> Cluster model of an EM cluster, providing a mean and a full covariance Matrix.
class	KMeansModel<V extends NumberVector<?>> Trivial subclass of the MeanModel that indicates the clustering to be produced by k-means (so the Voronoi cell visualization is sensible).
class	SubspaceModel<V extends FeatureVector<?>> Model for Subspace Clusters.

Uses of MeanModel in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel.cluster

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel.cluster with type parameters of type MeanModel

Modifier and Type	Field and Description
private Clustering<MeanModel<? extends NumberVector<?>>>	ClusterParallelMeanVisualization.Instance.clustering The result we visualize.

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel.cluster that return types with arguments of type MeanModel

Modifier and Type	Method and Description
private static Clustering<MeanModel<? extends NumberVector<?>>>	ClusterParallelMeanVisualization.findMeanModel(Clustering<?> c) Test if the given clustering has a mean model.

Uses of MeanModel in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster that return types with arguments of type MeanModel

Modifier and Type	Method and Description
private static <NV extends NumberVector<?>> Clustering<MeanModel<NV>>	EMClusterVisualization.findMeanModel(Clustering<?> c) Test if the given clustering has a mean model.

Uses of MeanModel in tutorial.clustering

Methods in tutorial.clustering that return types with arguments of type MeanModel

Modifier and Type	Method and Description
Clustering<MeanModel<V>>	SameSizeKMeansAlgorithm.run(Database database, Relation<V> relation) Run k-means with cluster size constraints.