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

Packages that use Model

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.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.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.evaluation.paircounting	Evaluation of clustering results via pair counting.
de.lmu.ifi.dbs.elki.result	Result types, representation and handling
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.vis1d	Visualizers based on 1D projections.
de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d	Visualizers based on 2D projections.

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

class	AbstractProjectedClustering<R extends Clustering<Model>,V extends NumberVector<V,?>> Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.
class	AbstractProjectedDBSCAN<R extends Clustering<Model>,V extends NumberVector<V,?>> 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

private Clustering<Model>	SLINK.extractClusters_erich(DBIDs ids, DataStore<DBID> pi, DataStore<D> lambda, int minclusters) Extract all clusters from the pi-lambda-representation.
Clustering<Model>	SNNClustering.run(Database database, Relation<O> relation) Perform SNN clustering
Clustering<Model>	DBSCAN.run(Database database, Relation<O> relation) Performs the DBSCAN algorithm on the given database.
Clustering<Model>	AbstractProjectedDBSCAN.run(Database database, Relation<V> relation)

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

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

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>	CASH.run(Database database, Relation<ParameterizationFunction> relation) 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

private SortedMap<Integer,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

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.subspace

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

Clustering<Model>	PROCLUS.run(Database database, Relation<V> relation) Performs the PROCLUS algorithm on the given database.
private List<Cluster<Model>>	SUBCLU.runDBSCAN(Relation<V> relation, DBIDs ids, Subspace<V> 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

private Subspace<V>

SUBCLU.bestSubspace(List<Subspace<V>> subspaces, Subspace<V> candidate, TreeMap<Subspace<V>,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

Clustering<Model>	ByLabelClustering.run(Database database)
Clustering<Model>	ByLabelHierarchicalClustering.run(Database database)
Clustering<Model>	TrivialAllNoise.run(Relation<?> relation)
Clustering<Model>	ByLabelClustering.run(Relation<?> relation) Run the actual clustering algorithm.
Clustering<Model>	ByLabelHierarchicalClustering.run(Relation<?> relation) Run the actual clustering algorithm.
Clustering<Model>	TrivialAllInOne.run(Relation<?> relation)

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

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

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

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

class	BaseModel Abstract base class for Cluster Models.
class	Bicluster<V extends FeatureVector<?,?>> Wrapper class to provide the basic properties of a bicluster.
class	BiclusterWithInverted<V extends FeatureVector<V,?>> This code was factored out of the Bicluster class, since not all biclusters have inverted rows.
class	ClusterModel Generic cluster model.
class	CorrelationAnalysisSolution<V extends NumberVector<V,?>> A solution of correlation analysis is a matrix of equations describing the dependencies.
class	CorrelationModel<V extends FeatureVector<V,?>> 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<V,?>> Cluster model of an EM cluster, providing a mean and a full covariance Matrix.
class	LinearEquationModel Cluster model containing a linear equation system for the cluster.
class	MeanModel<V extends FeatureVector<V,?>> Cluster model that stores a mean for the cluster.
class	OPTICSModel Model for an OPTICS cluster
class	SubspaceModel<V extends FeatureVector<V,?>> Model for Subspace Clusters.

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

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

static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2, boolean noiseSpecial, boolean hierarchicalSpecial) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2, boolean noiseSpecial, boolean hierarchicalSpecial) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2, double beta) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2, double beta) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2, double beta, boolean noiseSpecial, boolean hierarchicalSpecial) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> double	PairCountingFMeasure.compareClusterings(R result1, S result2, double beta, boolean noiseSpecial, boolean hierarchicalSpecial) Compare two clustering results.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> Triple<Integer,Integer,Integer>	PairCountingFMeasure.countPairs(R result1, S result2) Compare two sets of generated pairs.
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> Triple<Integer,Integer,Integer>	PairCountingFMeasure.countPairs(R result1, S result2) Compare two sets of generated pairs.
static <R extends Clustering<M>,M extends Model> PairSortedGeneratorInterface	PairCountingFMeasure.getPairGenerator(R clusters, boolean noiseSpecial, boolean hierarchicalSpecial) Get a pair generator for the given Clustering

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

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.visualization

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

private Clustering<Model>	VisualizerContext.generateDefaultClustering() Generate a default (fallback) clustering.
Clustering<Model>	VisualizerContext.getOrCreateDefaultClustering() Convenience method to get the clustering to use, and fall back to a default "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

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.vis1d

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.vis1d with type parameters of type Model

private Clustering<Model>

P1DHistogramVisualizer.clustering The clustering we visualize

Uses of Model in de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d with type parameters of type Model

private Clustering<Model>	ClusteringVisualization.clustering The result we visualize
(package private) Clustering<Model>	ClusterConvexHullVisualization.clustering The result we work on

Method parameters in de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d with type arguments of type Model

private void

BubbleVisualization.setupCSS(SVGPlot svgp, Clustering<? extends Model> clustering) Registers the Bubble-CSS-Class at a SVGPlot.