Modifier and Type | Class and Description |
---|---|
class |
AbstractDistanceBasedAlgorithm<O,D extends Distance<D>,R extends Result>
Provides an abstract algorithm already setting the distance function.
|
static class |
AbstractDistanceBasedAlgorithm.Parameterizer<O,D extends Distance<D>>
Parameterization helper class.
|
class |
AbstractPrimitiveDistanceBasedAlgorithm<O,D extends Distance<?>,R extends Result>
Provides an abstract algorithm already setting the distance function.
|
static class |
AbstractPrimitiveDistanceBasedAlgorithm.Parameterizer<O,D extends Distance<D>>
Parameterization helper class.
|
class |
DependencyDerivator<V extends NumberVector<?>,D extends Distance<D>>
Dependency derivator computes quantitatively linear dependencies among
attributes of a given dataset based on a linear correlation PCA.
|
static class |
DependencyDerivator.Parameterizer<V extends NumberVector<?>,D extends Distance<D>>
Parameterization class.
|
class |
KNNDistanceOrder<O,D extends Distance<D>>
Provides an order of the kNN-distances for all objects within the database.
|
static class |
KNNDistanceOrder.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
class |
KNNJoin<V extends NumberVector<?>,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.
|
static class |
KNNJoin.Parameterizer<V extends NumberVector<?>,D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry>
Parameterization class.
|
Modifier and Type | Field and Description |
---|---|
(package private) D |
KNNJoin.Task.mindist
Minimum distance.
|
Modifier and Type | Class and Description |
---|---|
class |
KNNBenchmarkAlgorithm<O,D extends Distance<D>>
Benchmarking algorithm that computes the k nearest neighbors for each query
point.
|
static class |
KNNBenchmarkAlgorithm.Parameterizer<O,D extends Distance<D>>
Parameterization class
|
Modifier and Type | Class and Description |
---|---|
static class |
AbstractProjectedDBSCAN.Parameterizer<V extends NumberVector<?>,D extends Distance<D>>
Parameterization class.
|
class |
DBSCAN<O,D extends Distance<D>>
DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected
sets in a database.
|
static class |
DBSCAN.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
class |
DeLiClu<NV extends NumberVector<?>,D extends Distance<D>>
DeLiClu provides the DeLiClu algorithm, a hierarchical algorithm to find
density-connected sets in a database.
|
static class |
DeLiClu.Parameterizer<NV extends NumberVector<?>,D extends Distance<D>>
Parameterization class.
|
class |
OPTICS<O,D extends Distance<D>>
OPTICS provides the OPTICS algorithm.
|
static class |
OPTICS.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
interface |
OPTICSTypeAlgorithm<D extends Distance<D>>
Interface for OPTICS type algorithms, that can be analysed by OPTICS Xi etc.
|
class |
SLINK<O,D extends Distance<D>>
Implementation of the efficient Single-Link Algorithm SLINK of R.
|
private static class |
SLINK.CompareByLambda<D extends Distance<D>>
Order a DBID collection by the lambda value.
|
static class |
SLINK.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
Modifier and Type | Field and Description |
---|---|
(package private) D |
DeLiClu.SpatialObjectPair.distance
The current distance.
|
private D |
OPTICS.epsilon
Hold the value of
OPTICS.EPSILON_ID . |
protected D |
OPTICS.Parameterizer.epsilon |
private D |
DBSCAN.epsilon
Holds the value of
DBSCAN.EPSILON_ID . |
protected D |
DBSCAN.Parameterizer.epsilon |
protected D |
AbstractProjectedDBSCAN.Parameterizer.epsilon |
Modifier and Type | Class and Description |
---|---|
class |
COPAC<V extends NumberVector<?>,D extends Distance<D>>
Provides the COPAC algorithm, an algorithm to partition a database according
to the correlation dimension of its objects and to then perform an arbitrary
clustering algorithm over the partitions.
|
static class |
COPAC.Parameterizer<V extends NumberVector<?>,D extends Distance<D>>
Parameterization class.
|
Modifier and Type | Class and Description |
---|---|
class |
EpsilonNeighborPredicate<O,D extends Distance<D>>
The default DBSCAN and OPTICS neighbor predicate, using an
epsilon-neighborhood.
|
static class |
EpsilonNeighborPredicate.Instance<D extends Distance<D>>
Instance for a particular data set.
|
static class |
EpsilonNeighborPredicate.Parameterizer<O,D extends Distance<D>>
Parameterization class
|
Modifier and Type | Field and Description |
---|---|
(package private) D |
EpsilonNeighborPredicate.epsilon
Range to query with
|
(package private) D |
EpsilonNeighborPredicate.Instance.epsilon
Range to query with
|
(package private) D |
EpsilonNeighborPredicate.Parameterizer.epsilon
Range to query with
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractKMeans<V extends NumberVector<?>,D extends Distance<D>,M extends MeanModel<V>>
Abstract base class for k-means implementations.
|
class |
KMeansLloyd<V extends NumberVector<?>,D extends Distance<D>>
Provides the k-means algorithm, using Lloyd-style bulk iterations.
|
static class |
KMeansLloyd.Parameterizer<V extends NumberVector<?>,D extends Distance<D>>
Parameterization class.
|
class |
KMeansMacQueen<V extends NumberVector<?>,D extends Distance<D>>
Provides the k-means algorithm, using MacQueen style incremental updates.
|
static class |
KMeansMacQueen.Parameterizer<V extends NumberVector<?>,D extends Distance<D>>
Parameterization class.
|
class |
KMediansLloyd<V extends NumberVector<?>,D extends Distance<D>>
Provides the k-medians clustering algorithm, using Lloyd-style bulk
iterations.
|
static class |
KMediansLloyd.Parameterizer<V extends NumberVector<?>,D extends Distance<D>>
Parameterization class.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDBOutlier<O,D extends Distance<D>>
Simple distance based outlier detection algorithms.
|
static class |
AbstractDBOutlier.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
class |
DBOutlierDetection<O,D extends Distance<D>>
Simple distanced based outlier detection algorithm.
|
static class |
DBOutlierDetection.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
class |
DBOutlierScore<O,D extends Distance<D>>
Compute percentage of neighbors in the given neighborhood with size d.
|
static class |
DBOutlierScore.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
Modifier and Type | Field and Description |
---|---|
private D |
AbstractDBOutlier.d
Holds the value of
AbstractDBOutlier.D_ID . |
protected D |
AbstractDBOutlier.Parameterizer.d
Query radius
|
Modifier and Type | Class and Description |
---|---|
class |
PrecomputedKNearestNeighborNeighborhood<D extends Distance<D>>
Neighborhoods based on k nearest neighbors.
|
static class |
PrecomputedKNearestNeighborNeighborhood.Factory<O,D extends Distance<D>>
Factory class to instantiate for a particular relation.
|
static class |
PrecomputedKNearestNeighborNeighborhood.Factory.Parameterizer<O,D extends Distance<D>>
Parameterization class
|
Modifier and Type | Class and Description |
---|---|
class |
DendrogramModel<D extends Distance<D>>
Model for dendrograms, provides the distance to the child cluster.
|
Modifier and Type | Field and Description |
---|---|
private D |
DendrogramModel.distance
Distance to child cluster
|
Modifier and Type | Method and Description |
---|---|
static <O,D extends Distance<D>> |
QueryUtil.getDistanceQuery(Database database,
DistanceFunction<? super O,D> distanceFunction,
Object... hints)
Get a distance query for a given distance function, automatically choosing
a relation.
|
<O,D extends Distance<D>> |
AbstractDatabase.getDistanceQuery(Relation<O> objQuery,
DistanceFunction<? super O,D> distanceFunction,
Object... hints) |
<O,D extends Distance<D>> |
Database.getDistanceQuery(Relation<O> relation,
DistanceFunction<? super O,D> distanceFunction,
Object... hints)
Get the distance query for a particular distance function.
|
static <O,D extends Distance<D>> |
QueryUtil.getKNNQuery(Database database,
DistanceFunction<? super O,D> distanceFunction,
Object... hints)
Get a KNN query object for the given distance function.
|
<O,D extends Distance<D>> |
AbstractDatabase.getKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<O,D extends Distance<D>> |
Database.getKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query.
|
static <O,D extends Distance<D>> |
QueryUtil.getKNNQuery(Relation<O> relation,
DistanceFunction<? super O,D> distanceFunction,
Object... hints)
Get a KNN query object for the given distance function.
|
static <O,D extends Distance<D>> |
QueryUtil.getLinearScanKNNQuery(DistanceQuery<O,D> distanceQuery)
Get a linear scan query for the given distance query.
|
static <O,D extends Distance<D>> |
QueryUtil.getLinearScanRangeQuery(DistanceQuery<O,D> distanceQuery)
Get a linear scan query for the given distance query.
|
static <O,D extends Distance<D>> |
QueryUtil.getRangeQuery(Database database,
DistanceFunction<? super O,D> distanceFunction,
Object... hints)
Get a range query object for the given distance function.
|
<O,D extends Distance<D>> |
AbstractDatabase.getRangeQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<O,D extends Distance<D>> |
Database.getRangeQuery(DistanceQuery<O,D> distanceQuery,
Object... hints)
Get a range query object for the given distance query.
|
static <O,D extends Distance<D>> |
QueryUtil.getRangeQuery(Relation<O> relation,
DistanceFunction<? super O,D> distanceFunction,
Object... hints)
Get a range query object for the given distance function.
|
<O,D extends Distance<D>> |
AbstractDatabase.getRKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<O,D extends Distance<D>> |
Database.getRKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints)
Get a rKNN query object for the given distance query.
|
static <O,D extends Distance<D>> |
QueryUtil.getRKNNQuery(Relation<O> relation,
DistanceFunction<? super O,D> distanceFunction,
Object... hints)
Get a rKNN query object for the given distance function.
|
static <O,D extends Distance<D>> |
QueryUtil.getSimilarityQuery(Database database,
SimilarityFunction<? super O,D> similarityFunction,
Object... hints)
Get a similarity query, automatically choosing a relation.
|
<O,D extends Distance<D>> |
AbstractDatabase.getSimilarityQuery(Relation<O> objQuery,
SimilarityFunction<? super O,D> similarityFunction,
Object... hints) |
<O,D extends Distance<D>> |
Database.getSimilarityQuery(Relation<O> relation,
SimilarityFunction<? super O,D> similarityFunction,
Object... hints)
Get the similarity query for a particular similarity function.
|
Modifier and Type | Interface and Description |
---|---|
interface |
DistanceDBIDPair<D extends Distance<D>>
Pair containing a distance an an object ID
Note: there is no getter for the object, as this is a
DBIDRef . |
Modifier and Type | Method and Description |
---|---|
static <D extends Distance<D>> |
DBIDUtil.newDistancePair(D dist,
DBIDRef id)
Make a DistanceDBIDPair.
|
<D extends Distance<D>> |
DBIDFactory.newDistancePair(D val,
DBIDRef id)
Make a new distance-DBID pair.
|
Modifier and Type | Class and Description |
---|---|
(package private) class |
DistanceIntegerDBIDPair<D extends Distance<D>>
Class storing a double distance a DBID.
|
Modifier and Type | Field and Description |
---|---|
(package private) D |
DistanceIntegerDBIDPair.distance
The distance value.
|
Modifier and Type | Method and Description |
---|---|
<D extends Distance<D>> |
SimpleDBIDFactory.newDistancePair(D val,
DBIDRef id) |
<D extends Distance<D>> |
TrivialDBIDFactory.newDistancePair(D val,
DBIDRef id) |
Modifier and Type | Interface and Description |
---|---|
interface |
DistanceSimilarityQuery<O,D extends Distance<D>>
Interface that is a combination of distance and a similarity function.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDatabaseDistanceQuery<O,D extends Distance<D>>
Run a database query in a database context.
|
class |
AbstractDistanceQuery<O,D extends Distance<D>>
A distance query serves as adapter layer for database and primitive
distances.
|
class |
DBIDDistanceQuery<D extends Distance<D>>
Run a distance query based on DBIDs
|
interface |
DistanceQuery<O,D extends Distance<?>>
A distance query serves as adapter layer for database and primitive distances.
|
class |
PrimitiveDistanceQuery<O,D extends Distance<D>>
Run a database query in a database context.
|
class |
PrimitiveDistanceSimilarityQuery<O,D extends Distance<D>>
Combination query class, for convenience.
|
interface |
SpatialDistanceQuery<V extends SpatialComparable,D extends Distance<D>>
Query interface for spatial distance queries.
|
class |
SpatialPrimitiveDistanceQuery<V extends SpatialComparable,D extends Distance<D>>
Distance query for spatial distance functions
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDistanceKNNQuery<O,D extends Distance<D>>
Instance for the query on a particular database.
|
interface |
KNNQuery<O,D extends Distance<D>>
The interface of an actual instance.
|
class |
LinearScanKNNQuery<O,D extends Distance<D>>
Instance of this query for a particular database.
|
class |
LinearScanPrimitiveDistanceKNNQuery<O,D extends Distance<D>>
Instance of this query for a particular database.
|
class |
PreprocessorKNNQuery<O,D extends Distance<D>,T extends KNNResult<D>>
Instance for a particular database, invoking the preprocessor.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDistanceRangeQuery<O,D extends Distance<D>>
Abstract base class for range queries that use a distance query in their
instance
|
class |
LinearScanPrimitiveDistanceRangeQuery<O,D extends Distance<D>>
Default linear scan range query class.
|
class |
LinearScanRangeQuery<O,D extends Distance<D>>
Default linear scan range query class.
|
interface |
RangeQuery<O,D extends Distance<D>>
The interface for range queries
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractRKNNQuery<O,D extends Distance<D>>
Instance for the query on a particular database.
|
class |
LinearScanRKNNQuery<O,D extends Distance<D>>
Default linear scan RKNN query class.
|
class |
PreprocessorRKNNQuery<O,D extends Distance<D>>
Instance for a particular database, invoking the preprocessor.
|
interface |
RKNNQuery<O,D extends Distance<D>>
Abstract reverse kNN Query interface.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDBIDSimilarityQuery<O,D extends Distance<D>>
Run a database query in a database context.
|
class |
AbstractSimilarityQuery<O,D extends Distance<D>>
A distance query serves as adapter layer for database and primitive
similarity functions.
|
class |
PrimitiveSimilarityQuery<O,D extends Distance<D>>
Run a database query in a database context.
|
interface |
SimilarityQuery<O,D extends Distance<?>>
A similarity query serves as adapter layer for database and primitive
similarity functions.
|
Modifier and Type | Method and Description |
---|---|
static <D extends Distance<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>> |
DistanceUtil.min(D d1,
D d2)
Returns the minimum of the given Distances or the first, if none is less
than the other one.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDatabaseDistanceFunction<O,D extends Distance<D>>
Abstract super class for distance functions needing a database context.
|
static class |
AbstractDatabaseDistanceFunction.Instance<O,D extends Distance<D>>
The actual instance bound to a particular database.
|
class |
AbstractDBIDDistanceFunction<D extends Distance<D>>
AbstractDistanceFunction provides some methods valid for any extending class.
|
class |
AbstractIndexBasedDistanceFunction<O,I extends Index,D extends Distance<D>>
Abstract super class for distance functions needing a database index.
|
static class |
AbstractIndexBasedDistanceFunction.Instance<O,I extends Index,D extends Distance<D>,F extends DistanceFunction<? super O,D>>
The actual instance bound to a particular database.
|
class |
AbstractPrimitiveDistanceFunction<O,D extends Distance<D>>
AbstractDistanceFunction provides some methods valid for any extending class.
|
interface |
DBIDDistanceFunction<D extends Distance<?>>
Distance functions valid in a database context only (i.e. for DBIDs)
For any "distance" that cannot be computed for arbitrary objects, only those
that exist in the database and referenced by their ID.
|
interface |
DistanceFunction<O,D extends Distance<?>>
Base interface for any kind of distances.
|
interface |
FilteredLocalPCABasedDistanceFunction<O extends NumberVector<?>,P extends FilteredLocalPCAIndex<? super O>,D extends Distance<D>>
Interface for local PCA based preprocessors.
|
static interface |
FilteredLocalPCABasedDistanceFunction.Instance<T extends NumberVector<?>,I extends Index,D extends Distance<D>>
Instance produced by the distance function.
|
interface |
IndexBasedDistanceFunction<O,D extends Distance<D>>
Distance function relying on an index (such as preprocessed neighborhoods).
|
static interface |
IndexBasedDistanceFunction.Instance<T,I extends Index,D extends Distance<D>>
Instance interface for Index based distance functions.
|
class |
MinKDistance<O,D extends Distance<D>>
A distance that is at least the distance to the kth nearest neighbor.
|
static class |
MinKDistance.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
interface |
Norm<O,D extends Distance<D>>
Abstract interface for a mathematical norm.
|
interface |
NumberVectorDistanceFunction<D extends Distance<D>>
Base interface for the common case of distance functions defined on numerical vectors.
|
interface |
PrimitiveDistanceFunction<O,D extends Distance<?>>
Primitive distance function that is defined on some kind of object.
|
class |
ProxyDistanceFunction<O,D extends Distance<D>>
Distance function to proxy computations to another distance (that probably
was run before).
|
interface |
SpatialPrimitiveDistanceFunction<V extends SpatialComparable,D extends Distance<D>>
API for a spatial primitive distance function.
|
Modifier and Type | Method and Description |
---|---|
static <O,D extends Distance<D>> |
ProxyDistanceFunction.proxy(DistanceQuery<O,D> inner)
Static method version.
|
static <V,T extends V,D extends Distance<D>> |
ProxyDistanceFunction.unwrapDistance(DistanceFunction<V,D> dfun)
Helper function, to resolve any wrapped Proxy Distances
|
Modifier and Type | Interface and Description |
---|---|
interface |
DistanceParser<D extends Distance<D>>
A DistanceParser shall provide a DistanceParsingResult by parsing an InputStream.
|
class |
DistanceParsingResult<D extends Distance<D>>
Provides a cache of precomputed distances between the database objects.
|
Modifier and Type | Interface and Description |
---|---|
interface |
DimensionSelectingSubspaceDistanceFunction<O,D extends Distance<D>>
Interface for dimension selecting subspace distance functions.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractKNNHeap<P extends DistanceDBIDPair<D>,D extends Distance<D>>
Heap used for KNN management.
|
interface |
DistanceDBIDResult<D extends Distance<D>>
Collection of objects and their distances.
|
interface |
DistanceDBIDResultIter<D extends Distance<D>>
Iterator over distance-based query results.
|
class |
GenericDistanceDBIDList<D extends Distance<D>>
Default class to keep a list of distance-object pairs.
|
(package private) class |
GenericKNNHeap<D extends Distance<D>>
Heap for collecting kNN candiates with arbitrary distance types.
|
class |
GenericKNNList<D extends Distance<D>>
Finalized KNN List.
|
interface |
KNNHeap<D extends Distance<D>>
Interface for kNN heaps.
|
interface |
KNNResult<D extends Distance<D>>
Interface for kNN results.
|
protected static class |
KNNUtil.DistanceItr<D extends Distance<D>>
Proxy iterator for accessing DBIDs.
|
protected static class |
KNNUtil.DistanceView<D extends Distance<D>>
A view on the Distances of the result.
|
protected static class |
KNNUtil.KNNSubList<D extends Distance<D>>
Sublist of an existing result to contain only the first k elements.
|
interface |
ModifiableDistanceDBIDResult<D extends Distance<D>>
Modifiable API for Distance-DBID results
|
Modifier and Type | Field and Description |
---|---|
protected D |
GenericKNNHeap.knndistance
Cached distance to k nearest neighbor (to avoid going through
AbstractKNNHeap.peek()
each time). |
Modifier and Type | Method and Description |
---|---|
static <D extends Distance<D>> |
KNNUtil.asDistanceList(KNNResult<D> list)
View as list of distances.
|
static <D extends Distance<D>> |
KNNUtil.newHeap(D factory,
int k)
Create an appropriate heap for the distance function.
|
static <D extends Distance<D>> |
KNNUtil.newHeap(DistanceFunction<?,D> df,
int k)
Create an appropriate heap for the distance function.
|
static <D extends Distance<D>> |
KNNUtil.newHeap(DistanceQuery<?,D> df,
int k)
Create an appropriate heap for the distance function.
|
static <D extends Distance<D>> |
KNNUtil.newHeap(KNNResult<D> exist)
Build a new heap from a given list.
|
static <D extends Distance<D>> |
KNNUtil.subList(KNNResult<D> list,
int k)
Get a subset of the KNN result.
|
Modifier and Type | Interface and Description |
---|---|
interface |
Distance<D extends Distance<D>>
The interface Distance defines the requirements of any instance class.
|
Modifier and Type | Class and Description |
---|---|
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 |
PCACorrelationDistance
The correlation distance is a special Distance that indicates the
dissimilarity between correlation connected objects.
|
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.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDBIDSimilarityFunction<D extends Distance<D>>
Abstract super class for distance functions needing a preprocessor.
|
class |
AbstractIndexBasedSimilarityFunction<O,I extends Index,R,D extends Distance<D>>
Abstract super class for distance functions needing a preprocessor.
|
static class |
AbstractIndexBasedSimilarityFunction.Instance<O,I extends Index,R,D extends Distance<D>>
The actual instance bound to a particular database.
|
class |
AbstractPrimitiveSimilarityFunction<O,D extends Distance<D>>
Base implementation of a similarity function.
|
interface |
DBIDSimilarityFunction<D extends Distance<D>>
Interface DBIDSimilarityFunction describes the requirements of any similarity
function defined over object IDs.
|
interface |
IndexBasedSimilarityFunction<O,D extends Distance<D>>
Interface for preprocessor/index based similarity functions.
|
static interface |
IndexBasedSimilarityFunction.Instance<T,I extends Index,D extends Distance<D>>
Instance interface for index/preprocessor based distance functions.
|
interface |
NormalizedPrimitiveSimilarityFunction<O,D extends Distance<D>>
Marker interface for similarity functions working on primitive objects, and
limited to the 0-1 value range.
|
interface |
NormalizedSimilarityFunction<O,D extends Distance<?>>
Marker interface to signal that the similarity function is normalized to
produce values in the range of [0:1].
|
interface |
PrimitiveSimilarityFunction<O,D extends Distance<D>>
Interface SimilarityFunction describes the requirements of any similarity
function.
|
interface |
SimilarityFunction<O,D extends Distance<?>>
Interface SimilarityFunction describes the requirements of any similarity
function.
|
Modifier and Type | Class and Description |
---|---|
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) !
|
Modifier and Type | Method and Description |
---|---|
static <D extends Distance<D>> |
ROC.computeROCAUCDistanceResult(int size,
Cluster<?> clus,
DistanceDBIDResult<D> nei)
Compute a ROC curves Area-under-curve for a QueryResult and a Cluster.
|
static <D extends Distance<D>> |
ROC.computeROCAUCDistanceResult(int size,
DBIDs ids,
DistanceDBIDResult<D> nei)
Compute a ROC curves Area-under-curve for a QueryResult and a Cluster.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractRefiningIndex.AbstractKNNQuery<D extends Distance<D>>
KNN query for this index.
|
class |
AbstractRefiningIndex.AbstractRangeQuery<D extends Distance<D>>
Range query for this index.
|
Modifier and Type | Method and Description |
---|---|
<D extends Distance<D>> |
KNNIndex.getKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query and k.
|
<D extends Distance<D>> |
RangeIndex.getRangeQuery(DistanceQuery<O,D> distanceQuery,
Object... hints)
Get a range query object for the given distance query and k.
|
<D extends Distance<D>> |
RKNNIndex.getRKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query and k.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractMaterializeKNNPreprocessor<O,D extends Distance<D>,T extends KNNResult<D>>
Abstract base class for KNN Preprocessors.
|
static class |
AbstractMaterializeKNNPreprocessor.Factory<O,D extends Distance<D>,T extends KNNResult<D>>
The parameterizable factory.
|
static class |
AbstractMaterializeKNNPreprocessor.Factory.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
class |
KNNJoinMaterializeKNNPreprocessor<V extends NumberVector<?>,D extends Distance<D>>
Class to materialize the kNN using a spatial join on an R-tree.
|
static class |
KNNJoinMaterializeKNNPreprocessor.Factory<O extends NumberVector<?>,D extends Distance<D>>
The parameterizable factory.
|
static class |
KNNJoinMaterializeKNNPreprocessor.Factory.Parameterizer<O extends NumberVector<?>,D extends Distance<D>>
Parameterization class
|
class |
MaterializeKNNAndRKNNPreprocessor<O,D extends Distance<D>>
A preprocessor for annotation of the k nearest neighbors and the reverse k
nearest neighbors (and their distances) to each database object.
|
static class |
MaterializeKNNAndRKNNPreprocessor.Factory<O,D extends Distance<D>>
The parameterizable factory.
|
static class |
MaterializeKNNAndRKNNPreprocessor.Factory.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
class |
MaterializeKNNPreprocessor<O,D extends Distance<D>>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
static class |
MaterializeKNNPreprocessor.Factory<O,D extends Distance<D>>
The parameterizable factory.
|
static class |
MaterializeKNNPreprocessor.Factory.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
class |
MetricalIndexApproximationMaterializeKNNPreprocessor<O extends NumberVector<?>,D extends Distance<D>,N extends Node<E>,E extends MTreeEntry<D>>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
static class |
MetricalIndexApproximationMaterializeKNNPreprocessor.Factory<O extends NumberVector<?>,D extends Distance<D>,N extends Node<E>,E extends MTreeEntry<D>>
The parameterizable factory.
|
static class |
MetricalIndexApproximationMaterializeKNNPreprocessor.Factory.Parameterizer<O extends NumberVector<?>,D extends Distance<D>,N extends Node<E>,E extends MTreeEntry<D>>
Parameterization class.
|
class |
PartitionApproximationMaterializeKNNPreprocessor<O,D extends Distance<D>>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
static class |
PartitionApproximationMaterializeKNNPreprocessor.Factory<O,D extends Distance<D>>
The parameterizable factory.
|
static class |
PartitionApproximationMaterializeKNNPreprocessor.Factory.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
class |
RandomSampleKNNPreprocessor<O,D extends Distance<D>>
Class that computed the kNN only on a random sample.
|
static class |
RandomSampleKNNPreprocessor.Factory<O,D extends Distance<D>>
The parameterizable factory.
|
static class |
RandomSampleKNNPreprocessor.Factory.Parameterizer<O,D extends Distance<D>>
Parameterization class
|
class |
SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector<?>,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.
|
static class |
SpatialApproximationMaterializeKNNPreprocessor.Factory<D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry>
The actual preprocessor instance.
|
static class |
SpatialApproximationMaterializeKNNPreprocessor.Factory.Parameterizer<D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry>
Parameterization class.
|
Modifier and Type | Method and Description |
---|---|
<S extends Distance<S>> |
AbstractMaterializeKNNPreprocessor.getKNNQuery(DistanceQuery<O,S> distQ,
Object... hints) |
<S extends Distance<S>> |
MaterializeKNNAndRKNNPreprocessor.getRKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
Modifier and Type | Class and Description |
---|---|
class |
SharedNearestNeighborPreprocessor<O,D extends Distance<D>>
A preprocessor for annotation of the ids of nearest neighbors to each
database object.
|
static class |
SharedNearestNeighborPreprocessor.Factory<O,D extends Distance<D>>
Factory class
|
static class |
SharedNearestNeighborPreprocessor.Factory.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractSubspaceProjectionIndex<NV extends NumberVector<?>,D extends Distance<D>,P extends ProjectionResult>
Abstract base class for a local PCA based index.
|
static class |
AbstractSubspaceProjectionIndex.Factory<NV extends NumberVector<?>,D extends Distance<D>,I extends AbstractSubspaceProjectionIndex<NV,D,?>>
Factory class
|
static class |
AbstractSubspaceProjectionIndex.Factory.Parameterizer<NV extends NumberVector<?>,D extends Distance<D>,C>
Parameterization class.
|
class |
FourCSubspaceIndex<V extends NumberVector<?>,D extends Distance<D>>
Preprocessor for 4C local dimensionality and locally weighted matrix
assignment to objects of a certain database.
|
static class |
FourCSubspaceIndex.Factory<V extends NumberVector<?>,D extends Distance<D>>
Factory class for 4C preprocessors.
|
static class |
FourCSubspaceIndex.Factory.Parameterizer<V extends NumberVector<?>,D extends Distance<D>>
Parameterization class.
|
class |
PreDeConSubspaceIndex<V extends NumberVector<?>,D extends Distance<D>>
Preprocessor for PreDeCon local dimensionality and locally weighted matrix
assignment to objects of a certain database.
|
static class |
PreDeConSubspaceIndex.Factory<V extends NumberVector<?>,D extends Distance<D>>
Factory.
|
static class |
PreDeConSubspaceIndex.Factory.Parameterizer<V extends NumberVector<?>,D extends Distance<D>>
Parameterization class.
|
Modifier and Type | Field and Description |
---|---|
protected D |
AbstractSubspaceProjectionIndex.epsilon
Contains the value of parameter epsilon;
|
protected D |
AbstractSubspaceProjectionIndex.Factory.epsilon
Contains the value of parameter epsilon;
|
protected D |
AbstractSubspaceProjectionIndex.Factory.Parameterizer.epsilon
Contains the value of parameter epsilon;
|
Modifier and Type | Class and Description |
---|---|
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.
|
Modifier and Type | Field and Description |
---|---|
private D |
DistanceEntry.distance
The distance value belonging to the entry.
|
Modifier and Type | Class and Description |
---|---|
class |
MetricalIndexTree<O,D extends Distance<D>,N extends Node<E>,E extends Entry>
Abstract super class for all metrical index classes.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractMTree<O,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>>
Abstract super class for all M-Tree variants.
|
class |
AbstractMTreeFactory<O,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>,I extends AbstractMTree<O,D,N,E> & Index>
Abstract factory for various MTrees
|
static class |
AbstractMTreeFactory.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
class |
AbstractMTreeNode<O,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.
|
Modifier and Type | Field and Description |
---|---|
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.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractMkTree<O,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 given parameter.
|
class |
AbstractMkTreeUnified<O,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 given parameter.
|
class |
AbstractMkTreeUnifiedFactory<O,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>,I extends AbstractMkTree<O,D,N,E> & Index>
Abstract factory for various Mk-Trees
|
static class |
AbstractMkTreeUnifiedFactory.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
Modifier and Type | Method and Description |
---|---|
<S extends Distance<S>> |
MkAppTreeIndex.getKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkAppTreeIndex.getRangeQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkAppTreeIndex.getRKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
<S extends Distance<S>> |
MkCoPTreeIndex.getKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkCoPTreeIndex.getRangeQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkCoPTreeIndex.getRKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
Modifier and Type | Class and Description |
---|---|
(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,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.
|
class |
MkMaxTreeFactory<O,D extends Distance<D>>
Factory for MkMaxTrees
|
static class |
MkMaxTreeFactory.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
class |
MkMaxTreeIndex<O,D extends Distance<D>>
MkMax tree
|
(package private) class |
MkMaxTreeNode<O,D extends Distance<D>>
Represents a node in an
MkMaxTree . |
Modifier and Type | Field and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
<S extends Distance<S>> |
MkMaxTreeIndex.getKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkMaxTreeIndex.getRangeQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkMaxTreeIndex.getRKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
Modifier and Type | Class and Description |
---|---|
(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,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.
|
class |
MkTabTreeFactory<O,D extends Distance<D>>
Factory for MkTabTrees
|
static class |
MkTabTreeFactory.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
class |
MkTabTreeIndex<O,D extends Distance<D>>
MkTabTree used as database index.
|
(package private) class |
MkTabTreeNode<O,D extends Distance<D>>
Represents a node in a MkMax-Tree.
|
Modifier and Type | Method and Description |
---|---|
<S extends Distance<S>> |
MkTabTreeIndex.getKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkTabTreeIndex.getRangeQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkTabTreeIndex.getRKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
Modifier and Type | Class and Description |
---|---|
class |
MTree<O,D extends Distance<D>>
MTree is a metrical index structure based on the concepts of the M-Tree.
|
class |
MTreeFactory<O,D extends Distance<D>>
Factory for a M-Tree
|
static class |
MTreeFactory.Parameterizer<O,D extends Distance<D>>
Parameterization class.
|
class |
MTreeIndex<O,D extends Distance<D>>
Class for using an m-tree as database index.
|
class |
MTreeNode<O,D extends Distance<D>>
Represents a node in an M-Tree.
|
Modifier and Type | Method and Description |
---|---|
<S extends Distance<S>> |
MTreeIndex.getKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MTreeIndex.getRangeQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
Modifier and Type | Class and Description |
---|---|
class |
MetricalIndexKNNQuery<O,D extends Distance<D>>
Instance of a KNN query for a particular spatial index.
|
class |
MetricalIndexRangeQuery<O,D extends Distance<D>>
Instance of a range query for a particular spatial index.
|
class |
MkTreeRKNNQuery<O,D extends Distance<D>>
Instance of a rKNN query for a particular spatial index.
|
Modifier and Type | Method and Description |
---|---|
static <O,D extends Distance<D>> |
MTreeQueryUtil.getKNNQuery(AbstractMTree<O,D,?,?> tree,
DistanceQuery<O,D> distanceQuery,
Object... hints)
Get an RTree knn query, using an optimized double implementation when
possible.
|
static <O,D extends Distance<D>> |
MTreeQueryUtil.getRangeQuery(AbstractMTree<O,D,?,?> tree,
DistanceQuery<O,D> distanceQuery,
Object... hints)
Get an RTree knn query, using an optimized double implementation when
possible.
|
Modifier and Type | Class and Description |
---|---|
class |
Assignments<D extends Distance<D>,E extends MTreeEntry<D>>
Encapsulates the attributes of an assignment during a split.
|
class |
MLBDistSplit<O,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,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,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.
|
Modifier and Type | Field and Description |
---|---|
private D |
Assignments.firstCoveringRadius
The first covering radius.
|
private D |
Assignments.secondCoveringRadius
The second covering radius.
|
Modifier and Type | Class and Description |
---|---|
class |
GenericDistanceSearchCandidate<D extends Distance<D>>
Candidate for expansion in a distance search (generic implementation).
|
class |
GenericMTreeDistanceSearchCandidate<D extends Distance<D>>
Encapsulates the attributes for a object that can be stored in a heap.
|
Modifier and Type | Field and Description |
---|---|
D |
GenericDistanceSearchCandidate.mindist
Distance value
|
D |
GenericMTreeDistanceSearchCandidate.routingDistance
The distance from the query to the routing object.
|
Modifier and Type | Method and Description |
---|---|
<D extends Distance<D>> |
DeLiCluTreeIndex.getKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
DeLiCluTreeIndex.getRangeQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
Modifier and Type | Class and Description |
---|---|
class |
GenericRStarTreeKNNQuery<O extends SpatialComparable,D extends Distance<D>>
Instance of a KNN query for a particular spatial index.
|
class |
GenericRStarTreeRangeQuery<O extends SpatialComparable,D extends Distance<D>>
Instance of a range query for a particular spatial index.
|
Modifier and Type | Method and Description |
---|---|
static <O extends SpatialComparable,D extends Distance<D>> |
RStarTreeUtil.getKNNQuery(AbstractRStarTree<?,?> tree,
SpatialDistanceQuery<O,D> distanceQuery,
Object... hints)
Get an RTree knn query, using an optimized double implementation when
possible.
|
static <O extends SpatialComparable,D extends Distance<D>> |
RStarTreeUtil.getRangeQuery(AbstractRStarTree<?,?> tree,
SpatialDistanceQuery<O,D> distanceQuery,
Object... hints)
Get an RTree range query, using an optimized double implementation when
possible.
|
Modifier and Type | Method and Description |
---|---|
<D extends Distance<D>> |
RStarTreeIndex.getKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
RStarTreeIndex.getRangeQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
<D extends Distance<D>> |
VAFile.getKNNQuery(DistanceQuery<V,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
PartialVAFile.getKNNQuery(DistanceQuery<V,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
VAFile.getRangeQuery(DistanceQuery<V,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
PartialVAFile.getRangeQuery(DistanceQuery<V,D> distanceQuery,
Object... hints) |
Modifier and Type | Class and Description |
---|---|
class |
KNNDistanceOrderResult<D extends Distance<D>>
Wraps a list containing the knn distances.
|
Modifier and Type | Interface and Description |
---|---|
interface |
ClusterOrderEntry<D extends Distance<D>>
Generic Cluster Order Entry Interface.
|
class |
ClusterOrderResult<D extends Distance<D>>
Class to store the result of an ordering clustering algorithm such as OPTICS.
|
class |
GenericClusterOrderEntry<D extends Distance<D>>
Provides an entry in a cluster order.
|
Modifier and Type | Field and Description |
---|---|
private D |
GenericClusterOrderEntry.reachability
The reachability of the entry.
|
Modifier and Type | Class and Description |
---|---|
class |
DistanceParameter<D extends Distance<D>>
Parameter class for a parameter specifying a double value.
|
Modifier and Type | Field and Description |
---|---|
(package private) D |
DistanceParameter.dist
Distance type
|
Modifier and Type | Interface and Description |
---|---|
interface |
OPTICSDistanceAdapter<D extends Distance<D>>
Interface to map ClusterOrderEntries to double values to use in the OPTICS plot.
|
class |
OPTICSPlot<D extends Distance<D>>
Class to produce an OPTICS plot image.
|
Modifier and Type | Method and Description |
---|---|
static <D extends Distance<D>> |
OPTICSPlot.canPlot(ClusterOrderResult<D> co)
Test whether this class can produce an OPTICS plot for the given cluster
order.
|
private static <D extends Distance<D>> |
OPTICSPlot.getAdapterForDistance(ClusterOrderResult<D> co)
Try to find a distance adapter.
|
static <D extends Distance<D>> |
OPTICSCut.makeOPTICSCut(ClusterOrderResult<D> co,
OPTICSDistanceAdapter<D> adapter,
double epsilon)
Compute an OPTICS cut clustering
|
static <D extends Distance<D>> |
OPTICSPlot.plotForClusterOrder(ClusterOrderResult<D> co,
VisualizerContext context)
Static method to find an optics plot for a result, or to create a new one
using the given context.
|
Modifier and Type | Class and Description |
---|---|
class |
OPTICSProjection<D extends Distance<D>>
OPTICS projection.
|
Modifier and Type | Class and Description |
---|---|
class |
OPTICSProjector<D extends Distance<D>>
Projection for OPTICS plots.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractOPTICSVisualization<D extends Distance<D>>
Abstract base class for OPTICS visualizer
|
class |
OPTICSClusterVisualization.Instance<D extends Distance<D>>
Instance.
|
class |
OPTICSPlotCutVisualization.Instance<D extends Distance<D>>
Instance.
|
class |
OPTICSPlotSelectionVisualization.Instance<D extends Distance<D>>
Instance.
|
class |
OPTICSPlotVisualizer.Instance<D extends Distance<D>>
Instance.
|
class |
OPTICSSteepAreaVisualization.Instance<D extends Distance<D>>
Instance
|