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.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan	Generalized DBSCAN.
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.
de.lmu.ifi.dbs.elki.data.type	Data type information, also used for type restrictions.
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.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.
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	Class and Description
class	AbstractProjectedDBSCAN<R extends Clustering<Model>,V extends NumberVector<?>> Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
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>	AbstractProjectedDBSCAN.run(Database database, Relation<V> relation) Run the algorithm
Clustering<Model>	DBSCAN.run(Relation<O> relation) Performs the DBSCAN algorithm on the given database.

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

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

Modifier and Type	Field and Description
protected Class<? extends ClusteringAlgorithm<Clustering<Model>>>	COPAC.Parameterizer.algC
private Class<? extends ClusteringAlgorithm<Clustering<Model>>>	COPAC.partitionAlgorithm Get the algorithm to run on each partition.

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.
ClusteringAlgorithm<Clustering<Model>>	COPAC.getPartitionAlgorithm(DistanceQuery<V,D> query) Returns the partition algorithm.
Clustering<Model>	LMCLUS.run(Database database, Relation<NumberVector<?>> relation) The main LMCLUS (Linear manifold clustering algorithm) is processed in this method.
Clustering<Model>	CASH.run(Database database, Relation<V> vrel) Run CASH on the relation.
Clustering<Model>	ORCLUS.run(Database database, Relation<V> relation) Performs the ORCLUS algorithm on the given database.
Clustering<Model>	COPAC.run(Relation<V> relation) Performs the COPAC algorithm on the given database.
private Clustering<Model>	COPAC.runPartitionAlgorithm(Relation<V> relation, Map<Integer,DBIDs> partitionMap, DistanceQuery<V,D> query) Runs the partition algorithm and creates the result.

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

Modifier and Type	Method and Description
private List<List<Cluster<CorrelationModel<V>>>>	ERiC.extractCorrelationClusters(Clustering<Model> copacResult, Relation<V> database, int dimensionality) 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.

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

Constructor and Description
COPAC(FilteredLocalPCABasedDistanceFunction<V,?,D> partitionDistanceFunction, Class<? extends ClusteringAlgorithm<Clustering<Model>>> partitionAlgorithm, Collection<Pair<OptionID,Object>> partitionAlgorithmParameters) Constructor.

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)

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 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(List<Subspace> subspaces, Subspace candidate, TreeMap<Subspace,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.

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

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

Modifier and Type	Class and Description
class	BaseModel 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<V extends FeatureVector<?>> Cluster model using a filtered PCA result and an centroid.
class	DendrogramModel<D extends Distance<D>> Model for dendrograms, provides the distance to the child cluster.
class	DimensionModel Cluster model just providing a cluster dimensionality.
class	EMModel<V extends FeatureVector<?>> Cluster model of an EM cluster, providing a mean and a full covariance Matrix.
class	KMeansModel<V extends NumberVector<?>> 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<V extends FeatureVector<?>> 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	SubspaceModel<V extends FeatureVector<?>> Model for Subspace Clusters.

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

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

Modifier and Type	Class and Description
class	GeneratorSingleCluster Class to generate a single cluster according to a model as well as getting the density of a given model at that point (to evaluate generated points according to the same model)

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

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

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

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

Modifier and Type	Field and Description
static SimpleTypeInformation<Model>	TypeUtil.MODEL Cluster model type.

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

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

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

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, List<Relation<?>> ra, NamingScheme naming)
private void	TextWriter.writeClusterResult(Database db, StreamFactory streamOpener, Clustering<Model> clustering, Cluster<Model> clus, 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 <D extends Distance<D>> Clustering<Model>	OPTICSCut.makeOPTICSCut(ClusterOrderResult<D> co, OPTICSDistanceAdapter<D> adapter, double epsilon) Compute an OPTICS cut clustering

Uses of Model 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 Model

Modifier and Type	Field and Description
private Clustering<Model>	ClusterOutlineVisualization.Instance.clustering The result we visualize

Uses of Model 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 Model

Modifier and Type	Field and Description
(package private) Clustering<Model>	ClusterMeanVisualization.Instance.clustering Clustering to visualize.
(package private) Clustering<Model>	VoronoiVisualization.Instance.clustering The result we work on.

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(ArrayList<Vector> hull, Hierarchy<Cluster<Model>> hier, Cluster<Model> clus) Recursively add a cluster and its children.
private double	ClusterHullVisualization.Instance.addRecursively(ArrayList<Vector> 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, Map<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, Map<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

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

Modifier and Type	Field and Description
private Clustering<Model>	KeyVisualization.Instance.clustering Clustering to display

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

Modifier and Type	Method and Description
private static <M extends Model> int[]	KeyVisualization.findDepth(Clustering<M> c)
private static <M extends Model> void	KeyVisualization.findDepth(Hierarchy<Cluster<M>> hier, Cluster<M> cluster, int[] size)

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, gnu.trove.map.TObjectIntMap<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, gnu.trove.map.TObjectIntMap<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, gnu.trove.map.TObjectIntMap<Cluster<Model>> cnum, Hierarchy<Cluster<Model>> hier)