Uses of Class
de.lmu.ifi.dbs.elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor

Packages that use AbstractMaterializeKNNPreprocessor

Package	Description
de.lmu.ifi.dbs.elki.database.query.knn	Prepared queries for k nearest neighbor (kNN) queries
de.lmu.ifi.dbs.elki.index.preprocessed.knn	Indexes providing KNN and rKNN data.
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection	Visualizers for object selection based on 2D projections

Uses of AbstractMaterializeKNNPreprocessor in de.lmu.ifi.dbs.elki.database.query.knn

Fields in de.lmu.ifi.dbs.elki.database.query.knn declared as AbstractMaterializeKNNPreprocessor

Modifier and Type	Field and Description
private AbstractMaterializeKNNPreprocessor<O>	PreprocessorKNNQuery.preprocessor The last preprocessor result

Methods in de.lmu.ifi.dbs.elki.database.query.knn that return AbstractMaterializeKNNPreprocessor

Modifier and Type	Method and Description
AbstractMaterializeKNNPreprocessor<O>	PreprocessorKNNQuery.getPreprocessor() Get the preprocessor instance.

Constructors in de.lmu.ifi.dbs.elki.database.query.knn with parameters of type AbstractMaterializeKNNPreprocessor

Constructor and Description
PreprocessorKNNQuery(Relation<? extends O> relation, AbstractMaterializeKNNPreprocessor<O> preprocessor) Constructor.

Uses of AbstractMaterializeKNNPreprocessor in de.lmu.ifi.dbs.elki.index.preprocessed.knn

Subclasses of AbstractMaterializeKNNPreprocessor in de.lmu.ifi.dbs.elki.index.preprocessed.knn

Modifier and Type	Class and Description
class	CachedDoubleDistanceKNNPreprocessor<O> Preprocessor that loads an existing cached kNN result.
class	KNNJoinMaterializeKNNPreprocessor<V extends NumberVector> Class to materialize the kNN using a spatial join on an R-tree.
class	MaterializeKNNAndRKNNPreprocessor<O> A preprocessor for annotation of the k nearest neighbors and the reverse k nearest neighbors (and their distances) to each database object.
class	MaterializeKNNPreprocessor<O> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
class	MetricalIndexApproximationMaterializeKNNPreprocessor<O extends NumberVector,N extends Node<E>,E extends MTreeEntry> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
class	NNDescent<O> NN-desent (also known as KNNGraph) is an approximate nearest neighbor search algorithm beginning with a random sample, then iteratively refining this sample until.
class	PartitionApproximationMaterializeKNNPreprocessor<O> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
class	RandomSampleKNNPreprocessor<O> Class that computed the kNN only on a random sample.
class	SpacefillingMaterializeKNNPreprocessor<O extends NumberVector> Compute the nearest neighbors approximatively using space filling curves.
class	SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.knn that return AbstractMaterializeKNNPreprocessor

Modifier and Type	Method and Description
abstract AbstractMaterializeKNNPreprocessor<O>	AbstractMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)

Uses of AbstractMaterializeKNNPreprocessor in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection declared as AbstractMaterializeKNNPreprocessor

Modifier and Type	Field and Description
private AbstractMaterializeKNNPreprocessor<? extends NumberVector>	DistanceFunctionVisualization.Instance.result The selection result we work on

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection with parameters of type AbstractMaterializeKNNPreprocessor

Modifier and Type	Method and Description
static double	DistanceFunctionVisualization.getLPNormP(AbstractMaterializeKNNPreprocessor<?> kNN) Get the "p" value of an Lp norm.
static boolean	DistanceFunctionVisualization.isAngularDistance(AbstractMaterializeKNNPreprocessor<?> kNN) Test whether the given preprocessor used an angular distance function