Uses of Interface
elki.data.model.Model

Packages that use Model

Package	Description
elki.clustering	Clustering algorithms.
elki.clustering.correlation	Correlation clustering algorithms.
elki.clustering.dbscan	DBSCAN and its generalizations.
elki.clustering.dbscan.parallel	Parallel versions of Generalized DBSCAN.
elki.clustering.em.models
elki.clustering.hierarchical.extraction	Extraction of partitional clusterings from hierarchical results.
elki.clustering.kmeans	K-means clustering and variations.
elki.clustering.meta	Meta clustering algorithms, that get their result from other clusterings or external sources.
elki.clustering.subspace	Axis-parallel subspace clustering algorithms.
elki.clustering.trivial	Trivial clustering algorithms: all in one, no clusters, label clusterings.
elki.data
elki.data.model	Cluster models classes for various algorithms.
elki.datasource.parser
elki.evaluation.clustering	Evaluation of clustering results.

Uses of Model in elki.clustering

Classes in elki.clustering with type parameters of type Model

Modifier and Type	Interface	Description
interface	ClusteringAlgorithm<C extends Clustering<? extends Model>>	Interface for Algorithms that are capable to provide a Clustering as Result. in general, clustering algorithms are supposed to implement the Algorithm-Interface.

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

Modifier and Type	Method	Description
Clustering<Model>	SNNClustering.run(elki.database.relation.Relation<O> relation)	Perform SNN clustering

Uses of Model in elki.clustering.correlation

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

Modifier and Type	Method	Description
Clustering<Model>	LMCLUS.run(elki.database.relation.Relation<? extends elki.data.NumberVector> relation)	The main LMCLUS (Linear manifold clustering algorithm) is processed in this method.
Clustering<Model>	ORCLUS.run(elki.database.relation.Relation<? extends elki.data.NumberVector> relation)	Performs the ORCLUS algorithm on the given database.

Method parameters in elki.clustering.correlation with type arguments of type Model

Modifier and Type	Method	Description
private java.util.List<java.util.List<Cluster<CorrelationModel>>>	ERiC.extractCorrelationClusters(Clustering<Model> dbscanResult, elki.database.relation.Relation<? extends elki.data.NumberVector> relation, int dimensionality, ERiCNeighborPredicate.Instance npred)	Extracts the correlation clusters and noise from the copac result and returns a mapping of correlation dimension to maps of clusters within this correlation dimension.

Uses of Model in elki.clustering.dbscan

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

Modifier and Type	Method	Description
Clustering<Model>	GeneralizedDBSCAN.autorun(elki.database.Database database)
protected Clustering<Model>	GriDBSCAN.Instance.buildResult(elki.database.ids.DBIDs ids, int clusterid)	Assemble the clustering result.
Clustering<Model>	DBSCAN.run(elki.database.relation.Relation<O> relation)	Performs the DBSCAN algorithm on the given database.
Clustering<Model>	GeneralizedDBSCAN.Instance.run()	Run the actual GDBSCAN algorithm.
Clustering<Model>	GriDBSCAN.Instance.run(elki.database.relation.Relation<V> relation)	Performs the DBSCAN algorithm on the given database.
Clustering<Model>	GriDBSCAN.run(elki.database.relation.Relation<V> relation)	Performs the DBSCAN algorithm on the given database.
Clustering<Model>	LSDBC.run(elki.database.relation.Relation<O> relation)	Run the LSDBC algorithm

Uses of Model in elki.clustering.dbscan.parallel

Methods in elki.clustering.dbscan.parallel that return types with arguments of type Model

Modifier and Type	Method	Description
Clustering<Model>	ParallelGeneralizedDBSCAN.autorun(elki.database.Database database)
Clustering<Model>	ParallelGeneralizedDBSCAN.Instance.run()	Run the parallel GDBSCAN algorithm.

Uses of Model in elki.clustering.em.models

Classes in elki.clustering.em.models with type parameters of type Model

Modifier and Type	Interface	Description
interface	EMClusterModel<O,M extends Model>	Models usable in EM clustering.
interface	EMClusterModelFactory<O,M extends Model>	Factory for initializing the EM models.

Uses of Model in elki.clustering.hierarchical.extraction

Methods in elki.clustering.hierarchical.extraction that return types with arguments of type Model

Modifier and Type	Method	Description
Clustering<Model>	ClustersWithNoiseExtraction.autorun(elki.database.Database database)
Clustering<Model>	ClustersWithNoiseExtraction.Instance.run()	Extract all clusters from the pi-lambda-representation.
Clustering<Model>	ClustersWithNoiseExtraction.run(ClusterMergeHistory merges)	Process an existing result.

Uses of Model in elki.clustering.kmeans

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

Modifier and Type	Class	Description
class	AbstractKMeans<V extends elki.data.NumberVector,M extends Model>	Abstract base class for k-means implementations.
interface	KMeans<V extends elki.data.NumberVector,M extends Model>	Some constants and options shared among kmeans family algorithms.

Uses of Model in elki.clustering.meta

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

Modifier and Type	Method	Description
Clustering<? extends Model>	ExternalClustering.autorun(elki.database.Database database)	Run the algorithm.

Uses of Model in elki.clustering.subspace

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

Modifier and Type	Method	Description
private java.util.List<Cluster<Model>>	SUBCLU.runDBSCAN(elki.database.relation.Relation<V> relation, elki.database.ids.DBIDs ids, Subspace subspace)	Runs the DBSCAN algorithm on the specified partition of the database in the given subspace.

Method parameters in elki.clustering.subspace with type arguments of type Model

Modifier and Type	Method	Description
private Subspace	SUBCLU.bestSubspace(java.util.List<Subspace> subspaces, Subspace candidate, java.util.TreeMap<Subspace,java.util.List<Cluster<Model>>> clusterMap)	Determines the d-dimensional subspace of the (d+1) -dimensional candidate with minimal number of objects in the cluster.

Uses of Model in elki.clustering.trivial

Classes in elki.clustering.trivial with type parameters of type Model

Modifier and Type	Class	Description
class	ReferenceClustering<M extends Model>	Reference clustering.

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

Modifier and Type	Method	Description
Clustering<Model>	ByLabelClustering.autorun(elki.database.Database database)
Clustering<Model>	ByLabelHierarchicalClustering.autorun(elki.database.Database database)
Clustering<Model>	ByLabelOrAllInOneClustering.autorun(elki.database.Database database)
Clustering<Model>	ByLabelClustering.run(elki.database.relation.Relation<?> relation)	Run the actual clustering algorithm.
Clustering<Model>	ByLabelHierarchicalClustering.run(elki.database.relation.Relation<?> relation)	Run the actual clustering algorithm.
Clustering<Model>	TrivialAllInOne.run(elki.database.relation.Relation<?> relation)	Perform trivial clustering.
Clustering<Model>	TrivialAllNoise.run(elki.database.relation.Relation<?> relation)	Run the trivial clustering algorithm.

Uses of Model in elki.data

Classes in elki.data with type parameters of type Model

Modifier and Type	Class	Description
class	Cluster<M extends Model>	Generic cluster class, that may or not have hierarchical information.
class	Clustering<M extends Model>	Result class for clusterings.

Fields in elki.data declared as Model

Modifier and Type	Field	Description
private M	Cluster.model	Cluster model.

Methods in elki.data that return types with arguments of type Model

Modifier and Type	Method	Description
static java.util.List<Clustering<? extends Model>>	Clustering.getClusteringResults(java.lang.Object r)	Collect all clustering results from a Result

Uses of Model in elki.data.model

Subinterfaces of Model in elki.data.model

Modifier and Type	Interface	Description
interface	PrototypeModel<V>	Cluster model that stores a prototype for each cluster.

Classes in elki.data.model that implement Model

Modifier and Type	Class	Description
class	BiclusterModel	Wrapper class to provide the basic properties of a Bicluster.
class	BiclusterWithInversionsModel	This code was factored out of the Bicluster class, since not all biclusters have inverted rows.
class	ClusterModel	Generic cluster model.
class	CoreObjectsModel	Cluster model using "core" objects.
class	CorrelationModel	Cluster model using a filtered PCA result and an centroid.
class	DendrogramModel	Model for dendrograms, provides the height of this subtree.
class	DimensionModel	Cluster model additionally providing a cluster dimensionality.
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	LinearEquationModel	Cluster model containing a linear equation system for the cluster.
class	MeanModel	Cluster model that stores a mean for the cluster.
class	MedoidModel	Cluster model that stores a mean for the cluster.
class	OPTICSModel	Model for an OPTICS cluster
class	PrototypeDendrogramModel	Hierarchical cluster, with prototype.
class	SimplePrototypeModel<V>	Cluster model that stores a prototype for each cluster.
class	SubspaceModel	Model for Subspace Clusters.

Fields in elki.data.model with type parameters of type Model

Modifier and Type	Field	Description
static elki.data.type.SimpleTypeInformation<Model>	Model.TYPE	Type information, for relation selection.

Methods in elki.data.model with parameters of type Model

Modifier and Type	Method	Description
static elki.data.NumberVector	ModelUtil.getPrototype(Model model, elki.database.relation.Relation<? extends elki.data.NumberVector> relation)	Get the representative vector for a cluster model.
static <V extends elki.data.NumberVector> V	ModelUtil.getPrototype(Model model, elki.database.relation.Relation<? extends V> relation, elki.data.NumberVector.Factory<V> factory)	Get (and convert!)
static elki.data.NumberVector	ModelUtil.getPrototypeOrCentroid(Model model, elki.database.relation.Relation<? extends elki.data.NumberVector> relation, elki.database.ids.DBIDs ids)	Get the representative vector for a cluster model, or compute the centroid.
static <V extends elki.data.NumberVector> V	ModelUtil.getPrototypeOrCentroid(Model model, elki.database.relation.Relation<? extends V> relation, elki.database.ids.DBIDs ids, elki.data.NumberVector.Factory<V> factory)	Get the representative vector for a cluster model, or compute the centroid.

Uses of Model in elki.datasource.parser

Fields in elki.datasource.parser with type parameters of type Model

Modifier and Type	Field	Description
(package private) Clustering<Model>	ClusteringVectorParser.curclu	Current clustering.

Uses of Model in elki.evaluation.clustering

Methods in elki.evaluation.clustering with type parameters of type Model

Modifier and Type	Method	Description
static <C extends Model> void	LogClusterSizes.logClusterSizes(Clustering<C> c)	Log the cluster sizes of a clustering.