Uses of Interface
de.lmu.ifi.dbs.elki.distance.Distance

Packages that use Distance

de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms Clustering algorithms are supposed to implement the Algorithm-Interface.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.data	Basic classes for different data types, database object types and label types.
de.lmu.ifi.dbs.elki.database	ELKI database layer - loading, storing, indexing and accessing data
de.lmu.ifi.dbs.elki.distance	Distances and (in subpackages) distance functions and similarity functions.
de.lmu.ifi.dbs.elki.distance.distancefunction	Distance functions for use within ELKI.
de.lmu.ifi.dbs.elki.distance.similarityfunction	Similarity functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Kernel functions.
de.lmu.ifi.dbs.elki.evaluation.roc	Evaluation of rankings using ROC AUC (Receiver Operation Characteristics - Area Under Curve)
de.lmu.ifi.dbs.elki.index.tree	Tree-based index structures
de.lmu.ifi.dbs.elki.index.tree.metrical	Tree-based index structures for metrical vector spaces.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants	M-Tree and variants.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees	Metrical index structures based on the concepts of the M-Tree supporting processing of reverse k nearest neighbor queries by using the k-nn distances of the entries.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax	MkMaxTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab	MkTabTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree	MTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.split	Splitting strategies of nodes in an M-Tree (and variants).
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.util	Helper classes for the the M-Tree and it's variants.
de.lmu.ifi.dbs.elki.index.tree.spatial	Tree-based index structures for spatial indexing.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants	R*-Tree and variants.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn	RdKNNTree
de.lmu.ifi.dbs.elki.parser	Parsers for different file formats and data types.
de.lmu.ifi.dbs.elki.preprocessing	Preprocessors used for data preparation in a first step of various algorithms or distance and similarity measures.
de.lmu.ifi.dbs.elki.result	Result types, representation and handling

Uses of Distance in de.lmu.ifi.dbs.elki.algorithm

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

class	DependencyDerivator<V extends RealVector<V,?>,D extends Distance<D>> Dependency derivator computes quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA.
class	DistanceBasedAlgorithm<O extends DatabaseObject,D extends Distance<D>,R extends Result> Provides an abstract algorithm already setting the distance function.
class	KNNDistanceOrder<O extends DatabaseObject,D extends Distance<D>> Provides an order of the kNN-distances for all objects within the database.
class	KNNJoin<V extends NumberVector<V,?>,D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry> Joins in a given spatial database to each object its k-nearest neighbors.

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

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

class	DBSCAN<O extends DatabaseObject,D extends Distance<D>> DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database.
class	DeLiClu<O extends NumberVector<O,?>,D extends Distance<D>> DeLiClu provides the DeLiClu algorithm, a hierarchical algorithm to find density-connected sets in a database.
class	KMeans<D extends Distance<D>,V extends RealVector<V,?>> Provides the k-means algorithm.
class	OPTICS<O extends DatabaseObject,D extends Distance<D>> OPTICS provides the OPTICS algorithm.
class	SLINK<O extends DatabaseObject,D extends Distance<D>> Efficient implementation of the Single-Link Algorithm SLINK of R.
class	SNNClustering<O extends DatabaseObject,D extends Distance<D>> Shared nearest neighbor clustering.

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

private static AssociationID<Distance<?>>

SLINK.SLINK_LAMBDA Association ID for SLINK lambda value

Uses of Distance in de.lmu.ifi.dbs.elki.algorithm.outlier

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier with type parameters of type Distance

class

SOD<V extends RealVector<V,Double>,D extends Distance<D>>

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

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

class

KNNList<D extends Distance<D>> A wrapper class for storing the k most similar comparable objects.

Fields in de.lmu.ifi.dbs.elki.data declared as Distance

private D

KNNList.infiniteDistance The infinite distance.

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

Classes in de.lmu.ifi.dbs.elki.database with type parameters of type Distance

class	DistanceResultPair<D extends Distance<D>> Class that consists of a pair (distance, object ID) commonly returned for kNN and range queries.
class	MetricalIndexDatabase<O extends DatabaseObject,D extends Distance<D>,N extends MetricalNode<N,E>,E extends MTreeEntry<D>> MetricalIndexDatabase is a database implementation which is supported by a metrical index structure.

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

<D extends Distance<D>> List<List<DistanceResultPair<D>>>	Database.bulkKNNQueryForID(List<Integer> ids, int k, DistanceFunction<O,D> distanceFunction) Performs k-nearest neighbor queries for the given object IDs.
<D extends Distance<D>> List<List<DistanceResultPair<D>>>	SequentialDatabase.bulkKNNQueryForID(List<Integer> ids, int k, DistanceFunction<O,D> distanceFunction) Retrieves the k nearest neighbors for the query objects.
<D extends Distance<D>> List<List<DistanceResultPair<D>>>	SpatialIndexDatabase.bulkKNNQueryForID(List<Integer> ids, int k, DistanceFunction<O,D> distanceFunction)
<T extends Distance<T>> List<List<DistanceResultPair<T>>>	MetricalIndexDatabase.bulkKNNQueryForID(List<Integer> ids, int k, DistanceFunction<O,T> distanceFunction)
<D extends Distance<D>> List<DistanceResultPair<D>>	Database.kNNQueryForID(Integer id, int k, DistanceFunction<O,D> distanceFunction) Performs a k-nearest neighbor query for the given object ID.
<D extends Distance<D>> List<DistanceResultPair<D>>	SequentialDatabase.kNNQueryForID(Integer id, int k, DistanceFunction<O,D> distanceFunction) Retrieves the k nearest neighbors for the query object.
<D extends Distance<D>> List<DistanceResultPair<D>>	SpatialIndexDatabase.kNNQueryForID(Integer id, int k, DistanceFunction<O,D> distanceFunction)
<T extends Distance<T>> List<DistanceResultPair<T>>	MetricalIndexDatabase.kNNQueryForID(Integer id, int k, DistanceFunction<O,T> distanceFunction)
<D extends Distance<D>> List<DistanceResultPair<D>>	Database.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,D> distanceFunction) Performs a k-nearest neighbor query for the given object.
<D extends Distance<D>> List<DistanceResultPair<D>>	SequentialDatabase.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,D> distanceFunction) Retrieves the k nearest neighbors for the query object.
<D extends Distance<D>> List<DistanceResultPair<D>>	SpatialIndexDatabase.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,D> distanceFunction)
<T extends Distance<T>> List<DistanceResultPair<T>>	MetricalIndexDatabase.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,T> distanceFunction)
<D extends Distance<D>> List<DistanceResultPair<D>>	Database.rangeQuery(Integer id, String epsilon, DistanceFunction<O,D> distanceFunction) Performs a range query for the given object ID with the given epsilon range and the according distance function.
<D extends Distance<D>> List<DistanceResultPair<D>>	SequentialDatabase.rangeQuery(Integer id, String epsilon, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<DistanceResultPair<D>>	SpatialIndexDatabase.rangeQuery(Integer id, String epsilon, DistanceFunction<O,D> distanceFunction)
<T extends Distance<T>> List<DistanceResultPair<T>>	MetricalIndexDatabase.rangeQuery(Integer id, String epsilon, DistanceFunction<O,T> distanceFunction)
<D extends Distance<D>> List<DistanceResultPair<D>>	Database.reverseKNNQuery(Integer id, int k, DistanceFunction<O,D> distanceFunction) Performs a reverse k-nearest neighbor query for the given object ID.
<D extends Distance<D>> List<DistanceResultPair<D>>	SequentialDatabase.reverseKNNQuery(Integer id, int k, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<DistanceResultPair<D>>	SpatialIndexDatabase.reverseKNNQuery(Integer id, int k, DistanceFunction<O,D> distanceFunction) Performs a reverse k-nearest neighbor query for the given object ID.
<T extends Distance<T>> List<DistanceResultPair<T>>	MetricalIndexDatabase.reverseKNNQuery(Integer id, int k, DistanceFunction<O,T> distanceFunction)

Uses of Distance in de.lmu.ifi.dbs.elki.distance

Classes in de.lmu.ifi.dbs.elki.distance with type parameters of type Distance

class	AbstractMeasurementFunction<O extends DatabaseObject,D extends Distance<D>> Abstract implementation of interface MeasurementFunction that provides some methods valid for any extending class.
interface	Distance<D extends Distance<D>> The interface Distance defines the requirements of any instance class.
interface	MeasurementFunction<O extends DatabaseObject,D extends Distance<D>> Interface Measurement describes the requirements of any measurement function (e.g. distance function or similarity function), that provides a measurement for comparing database objects.
interface	PreprocessorBasedMeasurementFunction<O extends DatabaseObject,P extends Preprocessor<O>,D extends Distance<D>> Describes the requirements of any measurement function (e.g. distance function or similarity function) needing a preprocessor running on a database.

Classes in de.lmu.ifi.dbs.elki.distance that implement Distance

class	AbstractDistance<D extends AbstractDistance<D>> An abstract distance implements equals conveniently for any extending class.
class	BitDistance Provides a Distance for a bit-valued distance.
class	CorrelationDistance<D extends CorrelationDistance<D>> The correlation distance is a special Distance that indicates the dissimilarity between correlation connected objects.
class	DoubleDistance Provides a Distance for a double-valued distance.
class	FloatDistance Provides a Distance for a float-valued distance.
class	IntegerDistance
class	NumberDistance<D extends NumberDistance<D,N>,N extends Number> Provides a Distance for a number-valued distance.
class	PreferenceVectorBasedCorrelationDistance A PreferenceVectorBasedCorrelationDistance holds additionally to the CorrelationDistance the common preference vector of the two objects defining the distance.
class	SubspaceDistance The subspace distance is a special distance that indicates the dissimilarity between subspaces of equal dimensionality.

Methods in de.lmu.ifi.dbs.elki.distance with type parameters of type Distance

static <D extends Distance<D>> D	DistanceUtil.max(D d1, D d2) Returns the maximum of the given Distances or the first, if none is greater than the other one.
static <D extends Distance<D>> D	DistanceUtil.min(D d1, D d2) Returns the minimum of the given Distances or the first, if none is less than the other one.

Uses of Distance in de.lmu.ifi.dbs.elki.distance.distancefunction

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction with type parameters of type Distance

class	AbstractDistanceFunction<O extends DatabaseObject,D extends Distance<D>> AbstractDistanceFunction provides some methods valid for any extending class.
class	AbstractPreprocessorBasedDistanceFunction<O extends DatabaseObject,P extends Preprocessor<O>,D extends Distance<D>> Abstract super class for distance functions needing a preprocessor.
interface	DistanceFunction<O extends DatabaseObject,D extends Distance<D>> Interface DistanceFunction describes the requirements of any distance function.
class	RepresentationSelectingDistanceFunction<O extends DatabaseObject,M extends MultiRepresentedObject<O>,D extends Distance<D>> Distance function for multirepresented objects that selects one representation and computes the distances only within the selected representation.

Uses of Distance in de.lmu.ifi.dbs.elki.distance.similarityfunction

Classes in de.lmu.ifi.dbs.elki.distance.similarityfunction with type parameters of type Distance

class	AbstractPreprocessorBasedSimilarityFunction<O extends DatabaseObject,P extends Preprocessor<O>,D extends Distance<D>> Abstract super class for distance functions needing a preprocessor.
class	AbstractSimilarityFunction<O extends DatabaseObject,D extends Distance<D>>
class	FractionalSharedNearestNeighborSimilarityFunction<O extends DatabaseObject,D extends Distance<D>>
interface	NormalizedSimilarityFunction<O extends DatabaseObject,D extends Distance<D>> Marker interface to signal that the similarity function is normalized to produce values in the range of [0:1].
class	SharedNearestNeighborSimilarityFunction<O extends DatabaseObject,D extends Distance<D>>
interface	SimilarityFunction<O extends DatabaseObject,D extends Distance<D>> Interface SimilarityFunction describes the requirements of any similarity function.

Uses of Distance in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

Classes in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with type parameters of type Distance

class	AbstractKernelFunction<O extends DatabaseObject,D extends Distance<D>> AbstractKernelFunction provides some methods valid for any extending class.
interface	KernelFunction<O extends DatabaseObject,D extends Distance<D>> Interface Kernel describes the requirements of any kernel function.

Uses of Distance in de.lmu.ifi.dbs.elki.evaluation.roc

Classes in de.lmu.ifi.dbs.elki.evaluation.roc with type parameters of type Distance

static class

ROC.DistanceResultAdapter<D extends Distance<D>> This adapter can be used for an arbitrary collection of Integers, and uses that id1.compareTo(id2) !

Methods in de.lmu.ifi.dbs.elki.evaluation.roc with type parameters of type Distance

static <D extends Distance<D>> double	ROC.computeROCAUCDistanceResult(int size, Cluster<?> clus, List<DistanceResultPair<D>> nei) Compute a ROC curves Area-under-curve for a QueryResult and a Cluster.
static <D extends Distance<D>> double	ROC.computeROCAUCDistanceResult(int size, Collection<Integer> ids, List<DistanceResultPair<D>> nei) Compute a ROC curves Area-under-curve for a QueryResult and a Cluster.

Uses of Distance in de.lmu.ifi.dbs.elki.index.tree

Classes in de.lmu.ifi.dbs.elki.index.tree with type parameters of type Distance

class

DistanceEntry<D extends Distance<D>,E extends Entry> Helper class: encapsulates an entry in an Index and a distance value belonging to this entry.

Fields in de.lmu.ifi.dbs.elki.index.tree declared as Distance

private D

DistanceEntry.distance The distance value belonging to the entry.

Uses of Distance in de.lmu.ifi.dbs.elki.index.tree.metrical

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical with type parameters of type Distance

class

MetricalIndex<O extends DatabaseObject,D extends Distance<D>,N extends MetricalNode<N,E>,E extends MetricalEntry> Abstract super class for all metrical index classes.

Uses of Distance in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants with type parameters of type Distance

class	AbstractMTree<O extends DatabaseObject,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>> Abstract super class for all M-Tree variants.
class	AbstractMTreeNode<O extends DatabaseObject,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>> Abstract super class for nodes in M-Tree variants.
class	MTreeDirectoryEntry<D extends Distance<D>> Represents an entry in a directory node of an M-Tree.
interface	MTreeEntry<D extends Distance<D>> Defines the requirements for an entry in an M-Tree node.
class	MTreeLeafEntry<D extends Distance<D>> Represents an entry in a leaf node of an M-Tree.

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants declared as Distance

private D	MTreeDirectoryEntry.coveringRadius The covering radius of the entry.
private D	MTreeLeafEntry.parentDistance The distance from the underlying data object to its parent's routing object.
private D	MTreeDirectoryEntry.parentDistance The distance from the routing object of this entry to its parent's routing object.

Uses of Distance in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees with type parameters of type Distance

class

AbstractMkTree<O extends DatabaseObject,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>> Abstract class for all M-Tree variants supporting processing of reverse k-nearest neighbor queries by using the k-nn distances of the entries, where k is less than or equal to the specified parameter AbstractMkTree.K_MAX_PARAM.

Uses of Distance in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax with type parameters of type Distance

(package private) class	MkMaxDirectoryEntry<D extends Distance<D>> Represents an entry in a directory node of an MkMaxTree.
(package private) interface	MkMaxEntry<D extends Distance<D>> Defines the requirements for an entry in an MkMaxTreeNode.
(package private) class	MkMaxLeafEntry<D extends Distance<D>> Represents an entry in a leaf node of an MkMaxTree.
class	MkMaxTree<O extends DatabaseObject,D extends Distance<D>> MkMaxTree is a metrical index structure based on the concepts of the M-Tree supporting efficient processing of reverse k nearest neighbor queries for parameter k <= k_max.
(package private) class	MkMaxTreeNode<O extends DatabaseObject,D extends Distance<D>> Represents a node in an MkMaxTree.

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax declared as Distance

private D	MkMaxDirectoryEntry.knnDistance The aggregated k-nearest neighbor distance of the underlying MkMax-Tree node.
private D	MkMaxLeafEntry.knnDistance The k-nearest neighbor distance of the underlying data object.

Uses of Distance in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab with type parameters of type Distance

(package private) class	MkTabDirectoryEntry<D extends Distance<D>> Represents an entry in a directory node of a MkTab-Tree.
(package private) interface	MkTabEntry<D extends Distance<D>> Defines the requirements for an entry in an MkCop-Tree node.
(package private) class	MkTabLeafEntry<D extends Distance<D>> Represents an entry in a leaf node of a MkTab-Tree.
class	MkTabTree<O extends DatabaseObject,D extends Distance<D>> MkTabTree is a metrical index structure based on the concepts of the M-Tree supporting efficient processing of reverse k nearest neighbor queries for parameter k < kmax.
(package private) class	MkTabTreeNode<O extends DatabaseObject,D extends Distance<D>> Represents a node in a MkMax-Tree.

Uses of Distance in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree with type parameters of type Distance

class	MTree<O extends DatabaseObject,D extends Distance<D>> MTree is a metrical index structure based on the concepts of the M-Tree.
class	MTreeNode<O extends DatabaseObject,D extends Distance<D>> Represents a node in an M-Tree.

Uses of Distance in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.split

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.split with type parameters of type Distance

class	Assignments<D extends Distance<D>,E extends MTreeEntry<D>> Encapsulates the attributes of an assignment during a split.
class	MLBDistSplit<O extends DatabaseObject,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>> Encapsulates the required methods for a split of a node in an M-Tree.
class	MRadSplit<O extends DatabaseObject,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>> Encapsulates the required methods for a split of a node in an M-Tree.
class	MTreeSplit<O extends DatabaseObject,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>> Abstract super class for splitting a node in an M-Tree.

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.split declared as Distance

private D	Assignments.firstCoveringRadius The first covering radius.
private D	Assignments.secondCoveringRadius The second covering radius.

Uses of Distance in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.util

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.util with type parameters of type Distance

class

PQNode<D extends Distance<D>> Encapsulates the attributes for a object that can be stored in a heap.

Uses of Distance in de.lmu.ifi.dbs.elki.index.tree.spatial

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial with type parameters of type Distance

interface

SpatialDistanceFunction<V extends FeatureVector<V,?>,D extends Distance<D>> Defines the requirements for a distance function that can used in spatial index to measure the dissimilarity between spatial data objects.

Methods in de.lmu.ifi.dbs.elki.index.tree.spatial with type parameters of type Distance

abstract <D extends Distance<D>> List<List<DistanceResultPair<D>>>	SpatialIndex.bulkKNNQueryForIDs(List<Integer> ids, int k, SpatialDistanceFunction<O,D> distanceFunction) Performs a bulk k-nearest neighbor query for the given object IDs.
abstract <D extends Distance<D>> List<DistanceResultPair<D>>	SpatialIndex.kNNQuery(O obj, int k, SpatialDistanceFunction<O,D> distanceFunction) Performs a k-nearest neighbor query for the given object with the given parameter k and the according distance function.
abstract <D extends Distance<D>> List<DistanceResultPair<D>>	SpatialIndex.rangeQuery(O obj, String epsilon, SpatialDistanceFunction<O,D> distanceFunction) Performs a range query for the given object with the given epsilon range and the according distance function.
abstract <D extends Distance<D>> List<DistanceResultPair<D>>	SpatialIndex.reverseKNNQuery(O object, int k, SpatialDistanceFunction<O,D> distanceFunction) Performs a reverse k-nearest neighbor query for the given object ID.

Uses of Distance in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants

Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants with type parameters of type Distance

protected <D extends Distance<D>> void	AbstractRStarTree.batchNN(N node, SpatialDistanceFunction<O,D> distanceFunction, Map<Integer,KNNList<D>> knnLists) Performs a batch knn query.
<D extends Distance<D>> List<List<DistanceResultPair<D>>>	AbstractRStarTree.bulkKNNQueryForIDs(List<Integer> ids, int k, SpatialDistanceFunction<O,D> distanceFunction) Performs a bulk k-nearest neighbor query for the given object IDs.
protected <D extends Distance<D>> void	AbstractRStarTree.doKNNQuery(Object object, SpatialDistanceFunction<O,D> distanceFunction, KNNList<D> knnList) Performs a k-nearest neighbor query for the given NumberVector with the given parameter k and the according distance function.
protected <D extends Distance<D>> List<DistanceEntry<D,E>>	AbstractRStarTree.getSortedEntries(N node, Collection<Integer> ids, SpatialDistanceFunction<O,D> distanceFunction) Sorts the entries of the specified node according to their minimum distance to the specified objects.
protected <D extends Distance<D>> List<DistanceEntry<D,E>>	AbstractRStarTree.getSortedEntries(N node, Integer q, SpatialDistanceFunction<O,D> distanceFunction) Sorts the entries of the specified node according to their minimum distance to the specified object.
protected <D extends Distance<D>> void	AbstractRStarTreeNode.initReInsert(int start, DistanceEntry<D,E>[] reInsertEntries) Initializes a reinsert operation.
<D extends Distance<D>> List<DistanceResultPair<D>>	AbstractRStarTree.kNNQuery(O object, int k, SpatialDistanceFunction<O,D> distanceFunction) Performs a k-nearest neighbor query for the given NumberVector with the given parameter k and the according distance function.
<D extends Distance<D>> List<DistanceResultPair<D>>	AbstractRStarTree.rangeQuery(O object, String epsilon, SpatialDistanceFunction<O,D> distanceFunction) Performs a range query for the given spatial object with the given epsilon range and the according distance function.
<D extends Distance<D>> List<DistanceResultPair<D>>	AbstractRStarTree.reverseKNNQuery(O object, int k, SpatialDistanceFunction<O,D> distanceFunction) Performs a reverse k-nearest neighbor query for the given object ID.

Uses of Distance in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn

Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn with type parameters of type Distance

<T extends Distance<T>> List<DistanceResultPair<T>>

RdKNNTree.reverseKNNQuery(O object, int k, SpatialDistanceFunction<O,T> distanceFunction) Performs a reverse k-nearest neighbor query for the given object ID.

Uses of Distance in de.lmu.ifi.dbs.elki.parser

Classes in de.lmu.ifi.dbs.elki.parser with type parameters of type Distance

interface	DistanceParser<O extends DatabaseObject,D extends Distance<D>> A DistanceParser shall provide a DistanceParsingResult by parsing an InputStream.
class	DistanceParsingResult<O extends DatabaseObject,D extends Distance<D>> Provides a list of database objects and labels associated with these objects and a cache of precomputed distances between the database objects.

Uses of Distance in de.lmu.ifi.dbs.elki.preprocessing

Classes in de.lmu.ifi.dbs.elki.preprocessing with type parameters of type Distance

class	FourCPreprocessor<D extends Distance<D>,V extends RealVector<V,?>> Preprocessor for 4C local dimensionality and locally weighted matrix assignment to objects of a certain database.
class	MaterializeKNNPreprocessor<O extends DatabaseObject,D extends Distance<D>> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
class	PreDeConPreprocessor<D extends Distance<D>,V extends RealVector<V,?>> Preprocessor for PreDeCon local dimensionality and locally weighted matrix assignment to objects of a certain database.
class	ProjectedDBSCANPreprocessor<D extends Distance<D>,V extends RealVector<V,?>> Abstract superclass for preprocessor of algorithms extending the ProjectedDBSCAN algorithm.
class	SharedNearestNeighborsPreprocessor<O extends DatabaseObject,D extends Distance<D>> A preprocessor for annotation of the ids of nearest neighbors to each database object.
class	SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector<O,?>,D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.

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

Classes in de.lmu.ifi.dbs.elki.result with type parameters of type Distance

class	ClusterOrderEntry<D extends Distance<D>> Provides an entry in a cluster order.
class	ClusterOrderResult<D extends Distance<D>> Class to store the result of an ordering clustering algorithm such as OPTICS.
class	KNNDistanceOrderResult<D extends Distance<D>> Wraps a list containing the knn distances.

Fields in de.lmu.ifi.dbs.elki.result declared as Distance

private D

ClusterOrderEntry.reachability The reachability of the entry.