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.data	Basic classes for different data types, database object types and label types.
de.lmu.ifi.dbs.elki.data.cluster	Cluster classes.
de.lmu.ifi.dbs.elki.data.model	Cluster models classes for various algorithms.
de.lmu.ifi.dbs.elki.database	ELKI database layer - loading, storing, indexing and accessing data
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.visualizers	Visualizers for various results
de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d	Visualizers 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

interface

ClusteringAlgorithm<C extends Clustering<? extends Model>,O extends DatabaseObject> Interface for Algorithms that are capable to provide a Clustering as Result.

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

protected Clustering<Model>	SNNClustering.runInTime(Database<O> database) Performs the SNN clustering algorithm on the given database.
protected Clustering<Model>	ByLabelClustering.runInTime(Database<O> database) Run the actual clustering algorithm.
protected Clustering<Model>	DBSCAN.runInTime(Database<O> database) Performs the DBSCAN algorithm on the given database.
protected Clustering<Model>	TrivialAllInOne.runInTime(Database<O> database) Run the actual clustering algorithm.
protected Clustering<Model>	TrivialAllNoise.runInTime(Database<O> database) Run the actual clustering algorithm.
protected Clustering<Model>	ByLabelHierarchicalClustering.runInTime(Database<O> database) Run the actual clustering algorithm.
protected Clustering<Model>	ProjectedDBSCAN.runInTime(Database<V> 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

protected ObjectParameter<ClusteringAlgorithm<Clustering<Model>,V>>	COPAC.PARTITION_ALGORITHM_PARAM Parameter to specify the clustering algorithm to apply to each partition, must extend ClusteringAlgorithm.
private ClusteringAlgorithm<Clustering<Model>,V>	COPAC.partitionAlgorithm Holds the instance of the partitioning algorithm specified by COPAC.PARTITION_ALGORITHM_PARAM.

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

private Clustering<Model>	CASH.doRun(Database<ParameterizationFunction> database, FiniteProgress progress) Runs the CASH algorithm on the specified database, this method is recursively called until only noise is left.
ClusteringAlgorithm<Clustering<Model>,V>	COPAC.getPartitionAlgorithm() Returns the partition algorithm.
protected Clustering<Model>	CASH.runInTime(Database<ParameterizationFunction> database) Performs the CASH algorithm on the given database.
protected Clustering<Model>	ORCLUS.runInTime(Database<V> database) Performs the ORCLUS algorithm on the given database.
protected Clustering<Model>	COPAC.runInTime(Database<V> database) Performs the COPAC algorithm on the given database.
private Clustering<Model>	COPAC.runPartitionAlgorithm(Database<V> database, Map<Integer,List<Integer>> partitionMap) 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, Database<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.

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

private List<Cluster<Model>>	SUBCLU.runDBSCAN(Database<V> database, List<Integer> ids, Subspace<V> subspace) Runs the DBSCAN algorithm on the specified partition of the database in the given subspace.
protected Clustering<Model>	PROCLUS.runInTime(Database<V> database) Performs the PROCLUS algorithm on the given database.

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.data

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

class

Clustering<M extends Model> Result class for clusterings.

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

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

class

Cluster<M extends Model> Generic cluster class, that may or not have hierarchical information.

Fields in de.lmu.ifi.dbs.elki.data.cluster 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<V,?>> 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	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	SubspaceAndMeanModel<V extends FeatureVector<V,?>> Model for Subspace Clusters that additionally stores a mean vector.
class	SubspaceModel<V extends FeatureVector<V,?>> Model for Subspace Clusters.

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

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

<O extends DatabaseObject,R extends Clustering<M>,M extends Model,L extends ClassLabel> Database<O>	LabelsFromClustering.makeDatabaseFromClustering(Database<O> olddb, R clustering, Class<L> classLabel) Retrieve a cloned database that - does not contain noise points - has labels assigned based on the given clustering Useful for e.g. training a classifier based on a clustering.
<O extends DatabaseObject,R extends Clustering<M>,M extends Model> Map<Cluster<M>,Database<O>>	PartitionsFromClustering.makeDatabasesFromClustering(Database<O> olddb, R clustering) Use an existing clustering to partition a database.
<O extends DatabaseObject,R extends Clustering<M>,M extends Model,L extends ClassLabel> Map<L,Database<O>>	PartitionsFromClustering.makeDatabasesFromClustering(Database<O> olddb, R clustering, Class<L> classLabel) Use an existing clustering to partition a database.

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, 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> PairSortedGeneratorInterface	PairCountingFMeasure.getPairGenerator(R clusters) 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.visualizers

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers 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.visualizers.vis2d

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

private Clustering<Model>	BubbleVisualizer.clustering A clustering of the database.
protected Clustering<Model>	ClusteringVisualizer.clustering Clustering to visualize.

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

private Clustering<Model>

KeyVisualizer.clustering The clustering to visualize