Uses of Interface
de.lmu.ifi.dbs.elki.data.model.Model

Packages that use Model

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.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan	Generalized DBSCAN Generalized DBSCAN is an abstraction of the original DBSCAN idea, that allows the use of arbitrary "neighborhood" and "core point" predicates.
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.parallel	Parallel versions of Generalized DBSCAN.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction	Extraction of partitional clusterings from hierarchical results.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations
de.lmu.ifi.dbs.elki.algorithm.clustering.meta	Meta clustering algorithms, that get their result from other clusterings or external sources.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Axis-parallel subspace clustering algorithms The clustering algorithms in this package are instances of both, projected clustering algorithms or subspace clustering algorithms according to the classical but somewhat obsolete classification schema of clustering algorithms for axis-parallel subspaces.
de.lmu.ifi.dbs.elki.algorithm.clustering.trivial	Trivial clustering algorithms: all in one, no clusters, label clusterings These methods are mostly useful for providing a reference result in evaluation.
de.lmu.ifi.dbs.elki.algorithm.outlier.trivial	Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
de.lmu.ifi.dbs.elki.data	Basic classes for different data types, database object types and label types
de.lmu.ifi.dbs.elki.data.model	Cluster models classes for various algorithms
de.lmu.ifi.dbs.elki.data.synthetic.bymodel	Generator using a distribution model specified in an XML configuration file GeneratorXMLSpec is a standalone application that loads an XML specification file and generates a synthetic data set according to the specifications given.
de.lmu.ifi.dbs.elki.datasource.parser	Parsers for different file formats and data types The general use-case for any parser is to create objects out of an InputStream (e.g. by reading a data file).
de.lmu.ifi.dbs.elki.evaluation.clustering	Evaluation of clustering results
de.lmu.ifi.dbs.elki.evaluation.outlier	Evaluate an outlier score using a misclassification based cost model
de.lmu.ifi.dbs.elki.result	Result types, representation and handling
de.lmu.ifi.dbs.elki.result.textwriter	Text serialization (CSV, Gnuplot, Console, ...)
de.lmu.ifi.dbs.elki.visualization	Visualization package of ELKI
de.lmu.ifi.dbs.elki.visualization.opticsplot	Code for drawing OPTICS plots
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster	Visualizers for clustering results based on 2D projections
de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj	Visualizers that do not use a particular projection

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

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

Modifier and Type	Interface and 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 de.lmu.ifi.dbs.elki.algorithm.clustering that return types with arguments of type Model

Modifier and Type	Method and Description
Clustering<Model>	SNNClustering.run(Database database, Relation<O> relation) Perform SNN clustering
Clustering<Model>	DBSCAN.run(Relation<O> relation) Performs the DBSCAN algorithm on the given database.
Clustering<Model>	GriDBSCAN.run(Relation<V> relation) Performs the DBSCAN algorithm on the given database.
Clustering<Model>	GriDBSCAN.Instance.run(Relation<V> relation) Performs the DBSCAN algorithm on the given database.

Uses of Model in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

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

Modifier and Type	Method and Description
private Clustering<Model>	CASH.doRun(Relation<ParameterizationFunction> relation, FiniteProgress progress) Runs the CASH algorithm on the specified database, this method is recursively called until only noise is left.
Clustering<Model>	LMCLUS.run(Database database, Relation<NumberVector> relation) The main LMCLUS (Linear manifold clustering algorithm) is processed in this method.
Clustering<Model>	ORCLUS.run(Database database, Relation<V> relation) Performs the ORCLUS algorithm on the given database.
Clustering<Model>	CASH.run(Relation<V> rel) Run CASH on the relation.

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with type arguments of type Model

Modifier and Type	Method and Description
private java.util.List<java.util.List<Cluster<CorrelationModel>>>	ERiC.extractCorrelationClusters(Clustering<Model> dbscanResult, Relation<V> 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 de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan

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

Modifier and Type	Method and Description
Clustering<Model>	GeneralizedDBSCAN.Instance.run() Run the actual GDBSCAN algorithm.
Clustering<Model>	GeneralizedDBSCAN.run(Database database)
Clustering<Model>	LSDBC.run(Database database, Relation<O> relation) Run the LSDBC algorithm

Uses of Model in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.parallel

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

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

Uses of Model in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction

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

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

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

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

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

Uses of Model in de.lmu.ifi.dbs.elki.algorithm.clustering.meta

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

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

Uses of Model in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

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

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

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with type arguments of type Model

Modifier and Type	Method and 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 de.lmu.ifi.dbs.elki.algorithm.clustering.trivial

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

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

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial with type arguments of type Model

Modifier and Type	Method and Description
Clustering<Model>	ByModelClustering.run(Relation<Model> relation) Run the actual clustering algorithm.

Uses of Model in de.lmu.ifi.dbs.elki.algorithm.outlier.trivial

Method parameters in de.lmu.ifi.dbs.elki.algorithm.outlier.trivial with type arguments of type Model

Modifier and Type	Method and Description
OutlierResult	TrivialGeneratedOutlier.run(Relation<Model> models, Relation<NumberVector> vecs, Relation<?> labels) Run the algorithm

Uses of Model in de.lmu.ifi.dbs.elki.data

Classes in de.lmu.ifi.dbs.elki.data with type parameters of type Model

Modifier and Type	Class and 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 de.lmu.ifi.dbs.elki.data declared as Model

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

Methods in de.lmu.ifi.dbs.elki.data that return types with arguments of type Model

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

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

Subinterfaces of Model in de.lmu.ifi.dbs.elki.data.model

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

Classes in de.lmu.ifi.dbs.elki.data.model that implement Model

Modifier and Type	Class and Description
class	AbstractModel Abstract base class for Cluster Models.
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	CorrelationAnalysisSolution<V extends NumberVector> A solution of correlation analysis is a matrix of equations describing the dependencies.
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	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	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 de.lmu.ifi.dbs.elki.data.model with type parameters of type Model

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

Methods in de.lmu.ifi.dbs.elki.data.model with parameters of type Model

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

Uses of Model in de.lmu.ifi.dbs.elki.data.synthetic.bymodel

Methods in de.lmu.ifi.dbs.elki.data.synthetic.bymodel that return Model

Modifier and Type	Method and Description
Model	GeneratorStatic.makeModel()
Model	GeneratorSingleCluster.makeModel() Make a cluster model for this cluster.
Model	GeneratorInterface.makeModel() Make a cluster model for this cluster.

Methods in de.lmu.ifi.dbs.elki.data.synthetic.bymodel with parameters of type Model

Modifier and Type	Method and Description
private void	GeneratorMain.initLabelsAndModels(java.util.ArrayList<GeneratorInterface> generators, ClassLabel[] labels, Model[] models, java.util.regex.Pattern reassign) Initialize cluster labels and models.

Uses of Model in de.lmu.ifi.dbs.elki.datasource.parser

Fields in de.lmu.ifi.dbs.elki.datasource.parser with type parameters of type Model

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

Uses of Model in de.lmu.ifi.dbs.elki.evaluation.clustering

Methods in de.lmu.ifi.dbs.elki.evaluation.clustering with type parameters of type Model

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

Uses of Model in de.lmu.ifi.dbs.elki.evaluation.outlier

Methods in de.lmu.ifi.dbs.elki.evaluation.outlier that return types with arguments of type Model

Modifier and Type	Method and Description
private Clustering<Model>	OutlierThresholdClustering.split(OutlierResult or)

Uses of Model in de.lmu.ifi.dbs.elki.result

Method parameters in de.lmu.ifi.dbs.elki.result with type arguments of type Model

Modifier and Type	Method and Description
private DoubleObjPair<Polygon>	KMLOutputHandler.buildHullsRecursively(Cluster<Model> clu, Hierarchy<Cluster<Model>> hier, java.util.Map<java.lang.Object,DoubleObjPair<Polygon>> hulls, Relation<? extends NumberVector> coords) Recursively step through the clusters to build the hulls.
private DoubleObjPair<Polygon>	KMLOutputHandler.buildHullsRecursively(Cluster<Model> clu, Hierarchy<Cluster<Model>> hier, java.util.Map<java.lang.Object,DoubleObjPair<Polygon>> hulls, Relation<? extends NumberVector> coords) Recursively step through the clusters to build the hulls.
private void	KMLOutputHandler.writeClusteringResult(javax.xml.stream.XMLStreamWriter xmlw, Clustering<Model> clustering, Database database)

Uses of Model in de.lmu.ifi.dbs.elki.result.textwriter

Method parameters in de.lmu.ifi.dbs.elki.result.textwriter with type arguments of type Model

Modifier and Type	Method and Description
private void	TextWriter.writeClusterResult(Database db, StreamFactory streamOpener, Clustering<Model> clustering, Cluster<Model> clus, java.util.List<Relation<?>> ra, NamingScheme naming)
private void	TextWriter.writeClusterResult(Database db, StreamFactory streamOpener, Clustering<Model> clustering, Cluster<Model> clus, java.util.List<Relation<?>> ra, NamingScheme naming)

Uses of Model in de.lmu.ifi.dbs.elki.visualization

Methods in de.lmu.ifi.dbs.elki.visualization that return types with arguments of type Model

Modifier and Type	Method and Description
private Clustering<Model>	VisualizerContext.generateDefaultClustering() Generate a default (fallback) clustering.

Uses of Model in de.lmu.ifi.dbs.elki.visualization.opticsplot

Methods in de.lmu.ifi.dbs.elki.visualization.opticsplot that return types with arguments of type Model

Modifier and Type	Method and Description
static <E extends ClusterOrder> Clustering<Model>	OPTICSCut.makeOPTICSCut(E co, double epsilon) Compute an OPTICS cut clustering

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

Method parameters in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster with type arguments of type Model

Modifier and Type	Method and Description
private double	ClusterHullVisualization.Instance.addRecursively(java.util.ArrayList<double[]> hull, Hierarchy<Cluster<Model>> hier, Cluster<Model> clus) Recursively add a cluster and its children.
private double	ClusterHullVisualization.Instance.addRecursively(java.util.ArrayList<double[]> hull, Hierarchy<Cluster<Model>> hier, Cluster<Model> clus) Recursively add a cluster and its children.
private DoubleObjPair<Polygon>	ClusterHullVisualization.Instance.buildHullsRecursively(Cluster<Model> clu, Hierarchy<Cluster<Model>> hier, java.util.Map<java.lang.Object,DoubleObjPair<Polygon>> hulls) Recursively step through the clusters to build the hulls.
private DoubleObjPair<Polygon>	ClusterHullVisualization.Instance.buildHullsRecursively(Cluster<Model> clu, Hierarchy<Cluster<Model>> hier, java.util.Map<java.lang.Object,DoubleObjPair<Polygon>> hulls) Recursively step through the clusters to build the hulls.

Uses of Model in de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj with type parameters of type Model

Modifier and Type	Method and Description
protected static <M extends Model> int[]	KeyVisualization.findDepth(Clustering<M> c) Compute the size of the clustering.
private static <M extends Model> void	KeyVisualization.findDepth(Hierarchy<Cluster<M>> hier, Cluster<M> cluster, int[] size) Recursive depth computation.

Method parameters in de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj with type arguments of type Model

Modifier and Type	Method and Description
private double	KeyVisualization.Instance.drawHierarchy(SVGPlot svgp, MarkerLibrary ml, DoubleDoublePair size, DoubleDoublePair pos, int depth, Cluster<Model> cluster, it.unimi.dsi.fastutil.objects.Object2IntOpenHashMap<Cluster<Model>> cnum, Hierarchy<Cluster<Model>> hier)
private double	KeyVisualization.Instance.drawHierarchy(SVGPlot svgp, MarkerLibrary ml, DoubleDoublePair size, DoubleDoublePair pos, int depth, Cluster<Model> cluster, it.unimi.dsi.fastutil.objects.Object2IntOpenHashMap<Cluster<Model>> cnum, Hierarchy<Cluster<Model>> hier)
private double	KeyVisualization.Instance.drawHierarchy(SVGPlot svgp, MarkerLibrary ml, DoubleDoublePair size, DoubleDoublePair pos, int depth, Cluster<Model> cluster, it.unimi.dsi.fastutil.objects.Object2IntOpenHashMap<Cluster<Model>> cnum, Hierarchy<Cluster<Model>> hier)