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

Packages that use MeanModel

Package	Description
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
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.

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

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>>	KMeansLloyd.run(Database database, Relation<V> relation) Run k-means
Clustering<MeanModel<V>>	KMeansMacQueen.run(Database database, Relation<V> relation) Run k-means

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<V,?>> Cluster model of an EM cluster, providing a mean and a full covariance Matrix.
class	SubspaceModel<V extends FeatureVector<V,?>> 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.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.Factory.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

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

Modifier and Type	Field and Description
(package private) Clustering<MeanModel<? extends NumberVector<?,?>>>	ClusterMeanVisualization.clustering Clustering to visualize.
(package private) Clustering<MeanModel<? extends NumberVector<?,?>>>	VoronoiVisualization.clustering The result we work on

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 Clustering<MeanModel<? extends NumberVector<?,?>>>	ClusterMeanVisualization.Factory.findMeanModel(Clustering<?> c) Test if the given clustering has a mean model.
private static Clustering<MeanModel<? extends NumberVector<?,?>>>	VoronoiVisualization.Factory.findMeanModel(Clustering<?> c) Test if the given clustering has a mean model.
private static <NV extends NumberVector<NV,?>> Clustering<MeanModel<NV>>	EMClusterVisualization.Factory.findMeanModel(Clustering<?> c) Test if the given clustering has a mean model.