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Packages that use Distance | |
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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.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.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 |
de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters | Classes for various typed parameters. |
de.lmu.ifi.dbs.elki.visualization.opticsplot | Code for drawing OPTICS plots |
de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj | Visualizers that do not use a particular projection. |
Uses of Distance in de.lmu.ifi.dbs.elki.algorithm |
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Classes in de.lmu.ifi.dbs.elki.algorithm with type parameters of type Distance | |
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class |
DependencyDerivator<V extends NumberVector<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 |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering with type parameters of type Distance | |
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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 NumberVector<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 | |
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private D |
DBSCAN.epsilon
Holds the value of DBSCAN.EPSILON_PARAM . |
private D |
OPTICS.epsilon
Hold the value of OPTICS.EPSILON_PARAM . |
Fields in de.lmu.ifi.dbs.elki.algorithm.clustering with type parameters of type Distance | |
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private static AssociationID<Distance<?>> |
SLINK.SLINK_LAMBDA
Association ID for SLINK lambda value |
Uses of Distance in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with type parameters of type Distance | |
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class |
SUBCLU<V extends NumberVector<V,?>,D extends Distance<D>>
Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces. |
Uses of Distance in de.lmu.ifi.dbs.elki.algorithm.outlier |
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Classes in de.lmu.ifi.dbs.elki.algorithm.outlier with type parameters of type Distance | |
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class |
AbstractDBOutlier<O extends DatabaseObject,D extends Distance<D>>
Simple distance based outlier detection algorithms. |
class |
DBOutlierDetection<O extends DatabaseObject,D extends Distance<D>>
Simple distanced based outlier detection algorithm. |
class |
DBOutlierScore<O extends DatabaseObject,D extends Distance<D>>
Compute percentage of neighbors in the given neighborhood with size d. |
class |
SOD<V extends NumberVector<V,?>,D extends Distance<D>>
|
Fields in de.lmu.ifi.dbs.elki.algorithm.outlier declared as Distance | |
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private D |
AbstractDBOutlier.d
Holds the value of AbstractDBOutlier.D_PARAM . |
Uses of Distance in de.lmu.ifi.dbs.elki.data |
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Classes in de.lmu.ifi.dbs.elki.data with type parameters of type Distance | |
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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 | |
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private D |
KNNList.infiniteDistance
The infinite distance. |
Uses of Distance in de.lmu.ifi.dbs.elki.database |
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Classes in de.lmu.ifi.dbs.elki.database with type parameters of type Distance | |
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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 | ||
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Database.bulkKNNQueryForID(List<Integer> ids,
int k,
DistanceFunction<O,D> distanceFunction)
Performs k-nearest neighbor queries for the given object IDs. |
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SequentialDatabase.bulkKNNQueryForID(List<Integer> ids,
int k,
DistanceFunction<O,D> distanceFunction)
Retrieves the k nearest neighbors for the query objects. |
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SpatialIndexDatabase.bulkKNNQueryForID(List<Integer> ids,
int k,
DistanceFunction<O,D> distanceFunction)
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MetricalIndexDatabase.bulkKNNQueryForID(List<Integer> ids,
int k,
DistanceFunction<O,T> distanceFunction)
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Database.kNNQueryForID(Integer id,
int k,
DistanceFunction<O,D> distanceFunction)
Performs a k-nearest neighbor query for the given object ID. |
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SequentialDatabase.kNNQueryForID(Integer id,
int k,
DistanceFunction<O,D> distanceFunction)
Retrieves the k nearest neighbors for the query object. |
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SpatialIndexDatabase.kNNQueryForID(Integer id,
int k,
DistanceFunction<O,D> distanceFunction)
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MetricalIndexDatabase.kNNQueryForID(Integer id,
int k,
DistanceFunction<O,T> distanceFunction)
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Database.kNNQueryForObject(O queryObject,
int k,
DistanceFunction<O,D> distanceFunction)
Performs a k-nearest neighbor query for the given object. |
|
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SequentialDatabase.kNNQueryForObject(O queryObject,
int k,
DistanceFunction<O,D> distanceFunction)
Retrieves the k nearest neighbors for the query object. |
|
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SpatialIndexDatabase.kNNQueryForObject(O queryObject,
int k,
DistanceFunction<O,D> distanceFunction)
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MetricalIndexDatabase.kNNQueryForObject(O queryObject,
int k,
DistanceFunction<O,T> distanceFunction)
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Database.rangeQuery(Integer id,
D epsilon,
DistanceFunction<O,D> distanceFunction)
Performs a range query for the given object ID with the given epsilon range and the according distance function. |
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SequentialDatabase.rangeQuery(Integer id,
D epsilon,
DistanceFunction<O,D> distanceFunction)
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SpatialIndexDatabase.rangeQuery(Integer id,
D epsilon,
DistanceFunction<O,D> distanceFunction)
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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. |
|
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SequentialDatabase.rangeQuery(Integer id,
String epsilon,
DistanceFunction<O,D> distanceFunction)
|
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SpatialIndexDatabase.rangeQuery(Integer id,
String epsilon,
DistanceFunction<O,D> distanceFunction)
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MetricalIndexDatabase.rangeQuery(Integer id,
String epsilon,
DistanceFunction<O,T> distanceFunction)
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MetricalIndexDatabase.rangeQuery(Integer id,
T epsilon,
DistanceFunction<O,T> distanceFunction)
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Database.reverseKNNQuery(Integer id,
int k,
DistanceFunction<O,D> distanceFunction)
Performs a reverse k-nearest neighbor query for the given object ID. |
|
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SequentialDatabase.reverseKNNQuery(Integer id,
int k,
DistanceFunction<O,D> distanceFunction)
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SpatialIndexDatabase.reverseKNNQuery(Integer id,
int k,
DistanceFunction<O,D> distanceFunction)
Performs a reverse k-nearest neighbor query for the given object ID. |
|
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MetricalIndexDatabase.reverseKNNQuery(Integer id,
int k,
DistanceFunction<O,T> distanceFunction)
|
Uses of Distance in de.lmu.ifi.dbs.elki.distance |
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Classes in de.lmu.ifi.dbs.elki.distance with type parameters of type Distance | |
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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 | |
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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
Provides an integer distance value. |
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. |
Fields in de.lmu.ifi.dbs.elki.distance declared as Distance | |
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protected D |
AbstractMeasurementFunction.distanceFactory
The distance type |
Methods in de.lmu.ifi.dbs.elki.distance with type parameters of type Distance | ||
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static
|
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
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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 |
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Classes in de.lmu.ifi.dbs.elki.distance.distancefunction with type parameters of type Distance | |
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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. |
interface |
LocalPCAPreprocessorBasedDistanceFunction<O extends NumberVector<O,?>,P extends LocalPCAPreprocessor<O>,D extends Distance<D>>
Interface for local PCA based preprocessors. |
interface |
PreprocessorBasedDistanceFunction<O extends DatabaseObject,P extends Preprocessor<O>,D extends Distance<D>>
Interface to mark preprocessor based distance functions. |
Classes in de.lmu.ifi.dbs.elki.distance.distancefunction that implement Distance | |
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class |
PCACorrelationDistance
The correlation distance is a special Distance that indicates the dissimilarity between correlation connected objects. |
Uses of Distance in de.lmu.ifi.dbs.elki.distance.similarityfunction |
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Classes in de.lmu.ifi.dbs.elki.distance.similarityfunction with type parameters of type Distance | |
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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>>
SharedNearestNeighborSimilarityFunction with a pattern defined to accept Strings that define a non-negative Integer. |
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>>
SharedNearestNeighborSimilarityFunction with a pattern defined to accept Strings that define a non-negative Integer. |
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 |
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Classes in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with type parameters of type Distance | |
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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 |
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Classes in de.lmu.ifi.dbs.elki.evaluation.roc with type parameters of type Distance | |
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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 | ||
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static
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ROC.computeROCAUCDistanceResult(int size,
Cluster<?> clus,
List<DistanceResultPair<D>> nei)
Compute a ROC curves Area-under-curve for a QueryResult and a Cluster. |
|
static
|
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 |
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Classes in de.lmu.ifi.dbs.elki.index.tree with type parameters of type Distance | |
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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 | |
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private D |
DistanceEntry.distance
The distance value belonging to the entry. |
Uses of Distance in de.lmu.ifi.dbs.elki.index.tree.metrical |
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Classes in de.lmu.ifi.dbs.elki.index.tree.metrical with type parameters of type Distance | |
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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 |
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Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants with type parameters of type Distance | |
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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 | |
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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 |
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Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees with type parameters of type Distance | |
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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 |
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Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax with type parameters of type Distance | |
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(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 | |
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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 |
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Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab with type parameters of type Distance | |
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(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 |
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Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree with type parameters of type Distance | |
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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 |
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Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.split with type parameters of type Distance | |
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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 | |
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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 |
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Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.util with type parameters of type Distance | |
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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 |
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Classes in de.lmu.ifi.dbs.elki.index.tree.spatial with type parameters of type Distance | |
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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
|
SpatialIndex.bulkKNNQueryForIDs(List<Integer> ids,
int k,
SpatialDistanceFunction<O,D> distanceFunction)
Performs a bulk k-nearest neighbor query for the given object IDs. |
|
abstract
|
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
|
SpatialIndex.rangeQuery(O obj,
D epsilon,
SpatialDistanceFunction<O,D> distanceFunction)
Performs a range query for the given object with the given epsilon range and the according distance function. |
|
abstract
|
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
|
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 |
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Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants with type parameters of type Distance | ||
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protected
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AbstractRStarTree.batchNN(N node,
SpatialDistanceFunction<O,D> distanceFunction,
Map<Integer,KNNList<D>> knnLists)
Performs a batch knn query. |
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AbstractRStarTree.bulkKNNQueryForIDs(List<Integer> ids,
int k,
SpatialDistanceFunction<O,D> distanceFunction)
Performs a bulk k-nearest neighbor query for the given object IDs. |
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protected
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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. |
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protected
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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. |
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protected
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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. |
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protected
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AbstractRStarTreeNode.initReInsert(int start,
DistanceEntry<D,E>[] reInsertEntries)
Initializes a reinsert operation. |
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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. |
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AbstractRStarTree.rangeQuery(O object,
D epsilon,
SpatialDistanceFunction<O,D> distanceFunction)
Performs a range query for the given spatial object with the given epsilon range and the according distance function. |
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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. |
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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 |
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Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn with type parameters of type Distance | ||
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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 |
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Classes in de.lmu.ifi.dbs.elki.parser with type parameters of type Distance | |
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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 |
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Classes in de.lmu.ifi.dbs.elki.preprocessing with type parameters of type Distance | |
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class |
FourCPreprocessor<D extends Distance<D>,V extends NumberVector<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 NumberVector<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 FeatureVector<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. |
Fields in de.lmu.ifi.dbs.elki.preprocessing declared as Distance | |
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private D |
ProjectedDBSCANPreprocessor.epsilon
Contains the value of parameter epsilon; |
Uses of Distance in de.lmu.ifi.dbs.elki.result |
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Classes in de.lmu.ifi.dbs.elki.result with type parameters of type Distance | |
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class |
ClusterOrderEntry<D extends Distance<?>>
Provides an entry in a cluster order. |
class |
ClusterOrderResult<D extends Distance<?>>
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 | |
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private D |
ClusterOrderEntry.reachability
The reachability of the entry. |
Fields in de.lmu.ifi.dbs.elki.result with type parameters of type Distance | |
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static AssociationID<? extends Distance<?>> |
ClusterOrderResult.REACHABILITY_ID
Association ID for reachability distance. |
Uses of Distance in de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters |
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Classes in de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters with type parameters of type Distance | |
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class |
DistanceParameter<D extends Distance<D>>
Parameter class for a parameter specifying a double value. |
Fields in de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters declared as Distance | |
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(package private) D |
DistanceParameter.dist
Distance type |
Uses of Distance in de.lmu.ifi.dbs.elki.visualization.opticsplot |
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Classes in de.lmu.ifi.dbs.elki.visualization.opticsplot with type parameters of type Distance | |
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interface |
OPTICSDistanceAdapter<D extends Distance<?>>
Interface to map ClusterOrderEntries to double values to use in the OPTICS plot. |
class |
OPTICSPlot<D extends Distance<?>>
Class to produce an OPTICS plot image. |
Uses of Distance in de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj |
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Classes in de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj with type parameters of type Distance | |
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class |
OPTICSPlotVisualizer<D extends Distance<?>>
Visualize an OPTICS result by constructing an OPTICS plot for it. |
Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj with type parameters of type Distance | ||
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static
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OPTICSPlotVisualizer.canPlot(ClusterOrderResult<D> co)
Test whether we have an adapter for this cluster orders distance. |
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private static
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OPTICSPlotVisualizer.getAdapterForDistance(ClusterOrderResult<D> co)
Try to find a distance adapter. |
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