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

Packages that use Distance

de.lmu.ifi.dbs.elki.algorithm	Package to collect algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering	Package collects clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.result	Package to collect result classes for the results of algorithms.
de.lmu.ifi.dbs.elki.algorithm.result.clustering	Package to collect result classes for the results of clustering algorithms.
de.lmu.ifi.dbs.elki.database	Package collects variants of databases and related classes.
de.lmu.ifi.dbs.elki.distance	Package collects distances and - in its subpackages - distance and similarity functions.
de.lmu.ifi.dbs.elki.distance.distancefunction	Package collects distance functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction	Package collects similarity functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Package collects kernel functions.
de.lmu.ifi.dbs.elki.index.tree	Package collects variants of tree-based index structures.
de.lmu.ifi.dbs.elki.index.tree.metrical	Package collects metrical tree-based index structures.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants	Package collects variants of the M-Tree.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkmax	Package collects classes for the MkMaxTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktab	Package collects classes for the MkTabTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree	Package collects classes for the MTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.util	Package collects helper classes for the variants of the M-Tree.
de.lmu.ifi.dbs.elki.index.tree.spatial	Package collects spatial tree-based index structures.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants	Package collects variants of the R*-Tree.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn	Package collects classes for the RdKNNTree
de.lmu.ifi.dbs.elki.parser	Package collects parser for different file formats and data types.
de.lmu.ifi.dbs.elki.preprocessing	Package collects preprocessors used for data preparation in a first step of various algorithms or distance measures.
de.lmu.ifi.dbs.elki.utilities	Package collects various classes and methods of global utility.

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 quantitativly linear dependencies among attributes of a given dataset based on a linear correlation PCA.
class	DistanceBasedAlgorithm<O extends DatabaseObject,D extends Distance<D>> 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 hierachical 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 declared as Distance

(package private) D

SLINK.SLinkDistance.distance

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

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

class	KNNDistanceOrderResult<O extends DatabaseObject,D extends Distance<D>>
class	KNNJoinResult<O extends DatabaseObject,D extends Distance<D>> Provides the result of a kNN-Join.
class	PointerRepresentation<O extends DatabaseObject,D extends Distance<D>> Provides the result of the single link algorithm SLINK.

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

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

class	ClusterOrder<O extends DatabaseObject,D extends Distance<D>> A class representing the cluster order of the OPTICS algorithm.
class	ClusterOrderEntry<D extends Distance<D>> Provides an entry in a cluster order.
class	HierarchicalAxesParallelCorrelationClusters<V extends RealVector<V,?>,D extends Distance<D>> Provides a result of a clustering algorithm that computes hierarchical axes parallel correlation clusters from a cluster order.

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

private D	ClusterOrder.maxReachability The maximum reachability in this cluster order.
private D	ClusterOrderEntry.reachability The reachability of the entry.

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	DistanceCache<D extends Distance<D>> Provides a cache for distances between database objects.
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<QueryResult<D>>>	SequentialDatabase.bulkKNNQueryForID(List<Integer> ids, int k, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<List<QueryResult<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<QueryResult<D>>>	InvertedListDatabase.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<QueryResult<D>>>	SpatialIndexDatabase.bulkKNNQueryForID(List<Integer> ids, int k, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<List<QueryResult<D>>>	MetricalIndexDatabase.bulkKNNQueryForID(List<Integer> ids, int k, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<QueryResult<D>>	SequentialDatabase.kNNQueryForID(Integer id, int k, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<QueryResult<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<QueryResult<D>>	InvertedListDatabase.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<QueryResult<D>>	SpatialIndexDatabase.kNNQueryForID(Integer id, int k, DistanceFunction<O,D> distanceFunction)
<T extends Distance<T>> List<QueryResult<T>>	MetricalIndexDatabase.kNNQueryForID(Integer id, int k, DistanceFunction<O,T> distanceFunction)
<D extends Distance<D>> List<QueryResult<D>>	SequentialDatabase.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<QueryResult<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<QueryResult<D>>	InvertedListDatabase.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,D> distanceFunction) Performs a k-nearest neighbor query for the given object.
<D extends Distance<D>> List<QueryResult<D>>	SpatialIndexDatabase.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,D> distanceFunction)
<T extends Distance<T>> List<QueryResult<T>>	MetricalIndexDatabase.kNNQueryForObject(O queryObject, int k, DistanceFunction<O,T> distanceFunction)
<D extends Distance<D>> List<QueryResult<D>>	SequentialDatabase.rangeQuery(Integer id, String epsilon, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<QueryResult<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<QueryResult<D>>	InvertedListDatabase.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<QueryResult<D>>	SpatialIndexDatabase.rangeQuery(Integer id, String epsilon, DistanceFunction<O,D> distanceFunction)
<T extends Distance<T>> List<QueryResult<T>>	MetricalIndexDatabase.rangeQuery(Integer id, String epsilon, DistanceFunction<O,T> distanceFunction)
<D extends Distance<D>> List<QueryResult<D>>	SequentialDatabase.reverseKNNQuery(Integer id, int k, DistanceFunction<O,D> distanceFunction)
<D extends Distance<D>> List<QueryResult<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<QueryResult<D>>	InvertedListDatabase.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<QueryResult<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<QueryResult<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 Measurement Function 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.

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

(package private) class	AbstractDistance<D extends AbstractDistance<D>> An abstract distance implements equals conveniently for any extending class.
class	BitDistance TODO arthur comment
class	CorrelationDistance<D extends CorrelationDistance<D>> The CorrelationDistance 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>> 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<D extends SubspaceDistance<D>> The SubspaceDistance is a special distance that indicates the dissimilarity between subspaces of equal dimensionality.

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	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.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>> Represents a node in an AbstractM-Tree.
class	MLBDistSplit<O extends DatabaseObject,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>> Encapsulates the required parameters 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 parameters for a split of a node in an M-Tree.
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.
class	MTreeSplit<O extends DatabaseObject,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>> Encapsulates the required parameters for a split of a node in a 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	MTreeDirectoryEntry.parentDistance The distance from the routing object of this entry to its parent's routing object.
private D	MTreeLeafEntry.parentDistance The distance from the underlying data object to its parent's routing object.

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

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

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

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

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

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

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.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>> 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 < 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.util

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.util 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	PQNode<D extends Distance<D>> Encapsulates the attributes for a object that can be stored in a heap.

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.util 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.spatial

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

interface

SpatialDistanceFunction<O extends FeatureVector<O,?>,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<QueryResult<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<QueryResult<D>>	SpatialIndex.kNNQuery(O obj, int k, DistanceFunction<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<QueryResult<D>>	SpatialIndex.rangeQuery(O obj, String epsilon, DistanceFunction<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<QueryResult<D>>	SpatialIndex.reverseKNNQuery(O object, int k, DistanceFunction<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<QueryResult<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, DistanceFunction<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<QueryResult<D>>	AbstractRStarTree.kNNQuery(O object, int k, DistanceFunction<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<QueryResult<D>>	AbstractRStarTree.rangeQuery(O object, String epsilon, DistanceFunction<O,D> distanceFunction) Performs a range query for the given spatial objec with the given epsilon range and the according distance function.
<D extends Distance<D>> List<QueryResult<D>>	AbstractRStarTree.reverseKNNQuery(O object, int k, DistanceFunction<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<QueryResult<T>>

RdKNNTree.reverseKNNQuery(O object, int k, DistanceFunction<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	KernelFourCPreprocessor<D extends Distance<D>,V extends RealVector<V,?>> Preprocessor for kernel 4C local dimensionality, neighbor objects and strong eigenvector matrix assignment to objects of a certain database.
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 alghorithm.
class	SharedNearestNeighborsPreprocessor<O extends DatabaseObject,D extends Distance<D>> A preprocessor for annotation of the ids of nearest neighbors to each database object.

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

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

class	KNNList<D extends Distance<D>> A wrapper class for storing the k most similar comparable objects.
class	QueryResult<D extends Distance<D>> QueryResult holds the id of a database object and its distance to a special query object.

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

private D	QueryResult.distance The distance of the underlying database object to the query object.
private D	KNNList.infiniteDistance The infinite distance.

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

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