Uses of Class
elki.data.model.MeanModel

Packages that use MeanModel

Package	Description
elki.clustering	Clustering algorithms.
elki.clustering.em	Expectation-Maximization clustering algorithm for Gaussian Mixture Modeling (GMM).
elki.clustering.hierarchical.birch	BIRCH clustering.
elki.clustering.kmeans	K-means clustering and variations.
elki.clustering.kmeans.initialization	Initialization strategies for k-means.
elki.clustering.kmeans.quality	Quality measures for k-Means results.
elki.data.model	Cluster models classes for various algorithms.

Uses of MeanModel in elki.clustering

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

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

Uses of MeanModel in elki.clustering.em

Classes in elki.clustering.em with type parameters of type MeanModel

Modifier and Type	Class	Description
class	EM<O,M extends MeanModel>	Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.
static class	EM.Par<O,M extends MeanModel>	Parameterization class.

Uses of MeanModel in elki.clustering.hierarchical.birch

Methods in elki.clustering.hierarchical.birch that return types with arguments of type MeanModel

Modifier and Type	Method	Description
Clustering<MeanModel>	BIRCHLeafClustering.run(elki.database.relation.Relation<elki.data.NumberVector> relation)	Run the clustering algorithm.

Uses of MeanModel in elki.clustering.kmeans

Classes in elki.clustering.kmeans with type parameters of type MeanModel

Modifier and Type	Class	Description
class	BestOfMultipleKMeans<V extends elki.data.NumberVector,M extends MeanModel>	Run K-Means multiple times, and keep the best run.
static class	BestOfMultipleKMeans.Par<V extends elki.data.NumberVector,M extends MeanModel>	Parameterization class.
class	BisectingKMeans<V extends elki.data.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	BisectingKMeans.Par<V extends elki.data.NumberVector,M extends MeanModel>	Parameterization class.
class	GMeans<V extends elki.data.NumberVector,M extends MeanModel>	G-Means extends K-Means and estimates the number of centers with Anderson Darling Test. Implemented as specialization of XMeans.
static class	GMeans.Par<V extends elki.data.NumberVector,M extends MeanModel>	Parameterization class.
class	XMeans<V extends elki.data.NumberVector,M extends MeanModel>	X-means: Extending K-means with Efficient Estimation on the Number of Clusters.
static class	XMeans.Par<V extends elki.data.NumberVector,M extends MeanModel>	Parameterization class.

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

Modifier and Type	Method	Description
protected Clustering<MeanModel>	KMediansLloyd.Instance.buildMediansResult()
Clustering<MeanModel>	FuzzyCMeans.run(elki.database.relation.Relation<V> relation)	Runs Fuzzy C Means clustering on the given Relation
Clustering<MeanModel>	KMediansLloyd.run(elki.database.relation.Relation<V> relation)

Method parameters in elki.clustering.kmeans with type arguments of type MeanModel

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

Uses of MeanModel in elki.clustering.kmeans.initialization

Method parameters in elki.clustering.kmeans.initialization with type arguments of type MeanModel

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

Uses of MeanModel in elki.clustering.kmeans.quality

Method parameters in elki.clustering.kmeans.quality with type arguments of type MeanModel

Modifier and Type	Method	Description
static double	AbstractKMeansQualityMeasure.logLikelihood(elki.database.relation.Relation<? extends elki.data.NumberVector> relation, Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
static double	BayesianInformationCriterionXMeans.logLikelihoodXMeans(elki.database.relation.Relation<? extends elki.data.NumberVector> relation, Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
static double	BayesianInformationCriterionZhao.logLikelihoodZhao(elki.database.relation.Relation<? extends elki.data.NumberVector> relation, Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
static int	AbstractKMeansQualityMeasure.numberOfFreeParameters(elki.database.relation.Relation<? extends elki.data.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 elki.data.NumberVector> double	AkaikeInformationCriterion.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)
<V extends elki.data.NumberVector> double	AkaikeInformationCriterionXMeans.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)
<V extends elki.data.NumberVector> double	BayesianInformationCriterion.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)
<V extends elki.data.NumberVector> double	BayesianInformationCriterionXMeans.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)
<V extends elki.data.NumberVector> double	BayesianInformationCriterionZhao.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)
<V extends O> double	KMeansQualityMeasure.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)	Calculates and returns the quality measure.
<V extends elki.data.NumberVector> double	WithinClusterMeanDistance.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)
<V extends elki.data.NumberVector> double	WithinClusterVariance.quality(Clustering<? extends MeanModel> clustering, elki.distance.NumberVectorDistance<? super V> distance, elki.database.relation.Relation<V> relation)
static double	AbstractKMeansQualityMeasure.varianceContributionOfCluster(Cluster<? extends MeanModel> cluster, elki.distance.NumberVectorDistance<?> distance, elki.database.relation.Relation<? extends elki.data.NumberVector> relation)	Variance contribution of a single cluster.

Uses of MeanModel in elki.data.model

Subclasses of MeanModel in elki.data.model

Modifier and Type	Class	Description
class	EMModel	Cluster model of an EM cluster, providing a mean and a full covariance Matrix.
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.