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 Clustering algorithms are supposed to implement the Algorithm-Interface.
de.lmu.ifi.dbs.elki.algorithm.clustering.em	Expectation-Maximization clustering algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch	BIRCH clustering.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization	Initialization strategies for k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality	Quality measures for k-Means results.
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering	Clustering based outlier detection.
de.lmu.ifi.dbs.elki.data.model	Cluster models classes for various algorithms
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>	NaiveMeanShiftClustering.run(Database database, Relation<V> relation) Run the mean-shift clustering algorithm.

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

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

Modifier and Type	Class and Description
class	AbstractEMModelFactory<V extends NumberVector,M extends MeanModel> Abstract base class for initializing EM.
class	EM<V extends NumberVector,M extends MeanModel> Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.
static class	EM.Parameterizer<V extends NumberVector,M extends MeanModel> Parameterization class.
interface	EMClusterModel<M extends MeanModel> Models useable in EM clustering.
interface	EMClusterModelFactory<V extends NumberVector,M extends MeanModel> Factory for initializing the EM models.

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

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

Modifier and Type	Method and Description
Clustering<MeanModel>	BIRCHLeafClustering.run(Relation<NumberVector> relation) Run the 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	BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel> Run K-Means multiple times, and keep the best run.
static class	BestOfMultipleKMeans.Parameterizer<V extends NumberVector,M extends MeanModel> Parameterization class.
class	KMeansBisecting<V extends NumberVector,M extends MeanModel> 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,M extends MeanModel> Parameterization class.
class	XMeans<V extends NumberVector,M extends MeanModel> X-means: Extending K-means with Efficient Estimation on the Number of Clusters.
static class	XMeans.Parameterizer<V extends NumberVector,M extends MeanModel> 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
protected Clustering<MeanModel>	KMediansLloyd.Instance.buildMediansResult()
Clustering<MeanModel>	KMediansLloyd.run(Database database, Relation<V> relation)

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

Modifier and Type	Method and Description
protected double[][]	XMeans.splitCentroid(Cluster<? extends MeanModel> parentCluster, Relation<V> relation) Split an existing centroid into two initial centers.

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

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

Modifier and Type	Method and Description
void	PredefinedInitialMeans.setInitialClusters(java.util.List<? extends Cluster<? extends MeanModel>> initialMeans) Set the initial means.

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
static <V extends NumberVector> double	AbstractKMeansQualityMeasure.logLikelihood(Relation<V> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction) Computes log likelihood of an entire clustering.
static <V extends NumberVector> double	BayesianInformationCriterionZhao.logLikelihoodZhao(Relation<V> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction) Computes log likelihood of an entire clustering.
static int	AbstractKMeansQualityMeasure.numberOfFreeParameters(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering) Compute the number of free parameters.
static int	AbstractKMeansQualityMeasure.numPoints(Clustering<? extends MeanModel> clustering) Compute the number of points in a given set of clusters (which may be less than the complete data set for X-means!)
<V extends NumberVector> double	BayesianInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterionZhao.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	WithinClusterVarianceQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	AkaikeInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	WithinClusterMeanDistanceQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends O> double	KMeansQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation) Calculates and returns the quality measure.
static <V extends NumberVector> double	AbstractKMeansQualityMeasure.varianceOfCluster(Cluster<? extends MeanModel> cluster, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation) Variance contribution of a single cluster.

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

Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering with type parameters of type MeanModel

Modifier and Type	Field and Description
protected ClusteringAlgorithm<Clustering<MeanModel>>	CBLOF.clusteringAlgorithm The clustering algorithm to use.
protected ClusteringAlgorithm<Clustering<MeanModel>>	CBLOF.Parameterizer.clusteringAlgorithm The clustering algorithm to use.

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

Modifier and Type	Method and Description
private void	CBLOF.computeCBLOFs(Relation<O> relation, NumberVectorDistanceFunction<? super O> distance, WritableDoubleDataStore cblofs, DoubleMinMax cblofMinMax, java.util.List<? extends Cluster<MeanModel>> largeClusters, java.util.List<? extends Cluster<MeanModel>> smallClusters) Compute the CBLOF scores for all the data.
private void	CBLOF.computeCBLOFs(Relation<O> relation, NumberVectorDistanceFunction<? super O> distance, WritableDoubleDataStore cblofs, DoubleMinMax cblofMinMax, java.util.List<? extends Cluster<MeanModel>> largeClusters, java.util.List<? extends Cluster<MeanModel>> smallClusters) Compute the CBLOF scores for all the data.
private double	CBLOF.computeLargeClusterCBLOF(O obj, NumberVectorDistanceFunction<? super O> distanceQuery, NumberVector clusterMean, Cluster<MeanModel> cluster)
private double	CBLOF.computeSmallClusterCBLOF(O obj, NumberVectorDistanceFunction<? super O> distance, java.util.List<NumberVector> largeClusterMeans, Cluster<MeanModel> cluster)
private int	CBLOF.getClusterBoundary(Relation<O> relation, java.util.List<? extends Cluster<MeanModel>> clusters) Compute the boundary index separating the large cluster from the small cluster.

Constructor parameters in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering with type arguments of type MeanModel

Constructor and Description
CBLOF(NumberVectorDistanceFunction<? super O> distanceFunction, ClusteringAlgorithm<Clustering<MeanModel>> clusteringAlgorithm, double alpha, double beta) Constructor.

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 Cluster model of an EM cluster, providing a mean and a full covariance Matrix.
class	GeneratorModel Cluster model for synthetically generated data.
class	KMeansModel Trivial subclass of the MeanModel that indicates the clustering to be produced by k-means (so the Voronoi cell visualization is sensible).
class	SubspaceModel Model for Subspace Clusters.

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>	SameSizeKMeansAlgorithm.run(Database database, Relation<V> relation) Run k-means with cluster size constraints.