Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.clustering |
Clustering algorithms.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
Correlation clustering algorithms
|
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical |
Hierarchical agglomerative clustering (HAC).
|
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization |
Initialization strategies for k-means.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.optics |
OPTICS family of clustering algorithms.
|
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.algorithm.outlier.distance |
Distance-based outlier detection algorithms, such as DBOutlier and kNN.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.lof |
LOF family of outlier detection algorithms.
|
de.lmu.ifi.dbs.elki.algorithm.statistics |
Statistical analysis algorithms
The algorithms in this package perform statistical analysis of the data
(e.g. compute distributions, distance distributions etc.)
|
de.lmu.ifi.dbs.elki.database |
ELKI database layer - loading, storing, indexing and accessing data
|
de.lmu.ifi.dbs.elki.database.query |
Database queries - computing distances, neighbors, similarities - API and general documentation.
|
de.lmu.ifi.dbs.elki.database.query.distance |
Prepared queries for distances.
|
de.lmu.ifi.dbs.elki.database.query.knn |
Prepared queries for k nearest neighbor (kNN) queries.
|
de.lmu.ifi.dbs.elki.database.query.range |
Prepared queries for ε-range queries, that return all objects within the radius ε.
|
de.lmu.ifi.dbs.elki.database.query.rknn |
Prepared queries for reverse k nearest neighbor (rkNN) queries.
|
de.lmu.ifi.dbs.elki.database.relation |
Relations, materialized and virtual (views).
|
de.lmu.ifi.dbs.elki.distance.distancefunction |
Distance functions for use within ELKI.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.adapter |
Distance functions deriving distances from e.g. similarity measures
|
de.lmu.ifi.dbs.elki.distance.distancefunction.external |
Distance functions using external data sources.
|
de.lmu.ifi.dbs.elki.evaluation.clustering.internal |
Internal evaluation measures for clusterings.
|
de.lmu.ifi.dbs.elki.index |
Index structure implementations
|
de.lmu.ifi.dbs.elki.index.distancematrix |
Precomputed distance matrix.
|
de.lmu.ifi.dbs.elki.index.idistance |
iDistance is a distance based indexing technique, using a reference points embedding.
|
de.lmu.ifi.dbs.elki.index.invertedlist |
Indexes using inverted lists.
|
de.lmu.ifi.dbs.elki.index.lsh |
Locality Sensitive Hashing
|
de.lmu.ifi.dbs.elki.index.preprocessed.knn |
Indexes providing KNN and rKNN data.
|
de.lmu.ifi.dbs.elki.index.projected |
Projected indexes for data.
|
de.lmu.ifi.dbs.elki.index.tree.metrical.covertree |
Cover-tree variations.
|
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.mkapp | |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop | |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax | |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab | |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree | |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query |
Classes for performing queries (knn, range, ...) on metrical trees.
|
de.lmu.ifi.dbs.elki.index.tree.spatial.kd |
K-d-tree and variants.
|
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu | |
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.flat | |
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn | |
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar | |
de.lmu.ifi.dbs.elki.index.vafile |
Vector Approximation File
|
de.lmu.ifi.dbs.elki.utilities |
Utility and helper classes - commonly used data structures, output formatting, exceptions, ...
|
Modifier and Type | Method and Description |
---|---|
protected DistanceQuery<V> |
AbstractProjectedClustering.getDistanceQuery(Database database)
Returns the distance function.
|
Modifier and Type | Method and Description |
---|---|
private void |
ORCLUS.assign(Relation<V> database,
DistanceQuery<V> distFunc,
List<ORCLUS.ORCLUSCluster> clusters)
Creates a partitioning of the database by assigning each object to its
closest seed.
|
private Matrix |
ORCLUS.findBasis(Relation<V> database,
DistanceQuery<V> distFunc,
ORCLUS.ORCLUSCluster cluster,
int dim)
Finds the basis of the subspace of dimensionality
dim for the
specified cluster. |
private void |
ORCLUS.merge(Relation<V> database,
DistanceQuery<V> distFunc,
List<ORCLUS.ORCLUSCluster> clusters,
int k_new,
int d_new,
IndefiniteProgress cprogress)
Reduces the number of seeds to k_new
|
private ORCLUS.ProjectedEnergy |
ORCLUS.projectedEnergy(Relation<V> database,
DistanceQuery<V> distFunc,
ORCLUS.ORCLUSCluster c_i,
ORCLUS.ORCLUSCluster c_j,
int i,
int j,
int dim)
Computes the projected energy of the specified clusters.
|
private ORCLUS.ORCLUSCluster |
ORCLUS.union(Relation<V> relation,
DistanceQuery<V> distFunc,
ORCLUS.ORCLUSCluster c1,
ORCLUS.ORCLUSCluster c2,
int dim)
Returns the union of the two specified clusters.
|
Modifier and Type | Field and Description |
---|---|
private DistanceQuery<?> |
AbstractHDBSCAN.HDBSCANAdapter.distq
Distance query for exact distances.
|
Modifier and Type | Method and Description |
---|---|
protected static <O> void |
AGNES.initializeDistanceMatrix(double[] scratch,
DistanceQuery<O> dq,
DBIDArrayIter ix,
DBIDArrayIter iy,
boolean square)
Initialize a distance matrix.
|
private void |
SLINK.step2(DBIDRef id,
DBIDArrayIter it,
int n,
DistanceQuery<? super O> distQuery,
WritableDoubleDataStore m)
Second step: Determine the pairwise distances from all objects in the
pointer representation to the new object with the specified id.
|
private void |
SLINKHDBSCANLinearMemory.step2(DBIDRef id,
DBIDs processedIDs,
DistanceQuery<? super O> distQuery,
DoubleDataStore coredists,
WritableDoubleDataStore m)
Second step: Determine the pairwise distances from all objects in the
pointer representation to the new object with the specified id.
|
Constructor and Description |
---|
HDBSCANAdapter(ArrayDBIDs ids,
DoubleDataStore coredists,
DistanceQuery<?> distq)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
protected double |
CLARA.assignRemainingToNearestCluster(ArrayDBIDs means,
DBIDs ids,
DBIDs rids,
WritableIntegerDataStore assignment,
DistanceQuery<V> distQ)
Returns a list of clusters.
|
protected double |
KMedoidsPAM.assignToNearestCluster(ArrayDBIDs means,
DBIDs ids,
WritableDoubleDataStore nearest,
WritableDoubleDataStore second,
WritableIntegerDataStore assignment,
DistanceQuery<V> distQ)
Returns a list of clusters.
|
protected boolean |
KMedoidsEM.assignToNearestCluster(ArrayDBIDs means,
Mean[] mdist,
List<? extends ModifiableDBIDs> clusters,
DistanceQuery<V> distQ)
Returns a list of clusters.
|
protected void |
KMedoidsPAM.runPAMOptimization(DistanceQuery<V> distQ,
DBIDs ids,
ArrayModifiableDBIDs medoids,
WritableIntegerDataStore assignment)
Run the PAM optimization phase.
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
FarthestSumPointsInitialMeans.chooseInitialMedoids(int k,
DBIDs ids,
DistanceQuery<? super O> distQ) |
DBIDs |
RandomlyChosenInitialMeans.chooseInitialMedoids(int k,
DBIDs ids,
DistanceQuery<? super O> distanceFunction) |
DBIDs |
FirstKInitialMeans.chooseInitialMedoids(int k,
DBIDs ids,
DistanceQuery<? super O> distanceFunction) |
DBIDs |
KMeansPlusPlusInitialMeans.chooseInitialMedoids(int k,
DBIDs ids,
DistanceQuery<? super O> distQ) |
DBIDs |
PAMInitialMeans.chooseInitialMedoids(int k,
DBIDs ids,
DistanceQuery<? super O> distQ) |
DBIDs |
FarthestPointsInitialMeans.chooseInitialMedoids(int k,
DBIDs ids,
DistanceQuery<? super O> distQ) |
DBIDs |
KMedoidsInitialization.chooseInitialMedoids(int k,
DBIDs ids,
DistanceQuery<? super V> distanceFunction)
Choose initial means
|
protected <T> double |
KMeansPlusPlusInitialMeans.initialWeights(WritableDoubleDataStore weights,
DBIDs ids,
T latest,
DistanceQuery<? super T> distQ)
Initialize the weight list.
|
protected <T> double |
KMeansPlusPlusInitialMeans.updateWeights(WritableDoubleDataStore weights,
DBIDs ids,
T latest,
DistanceQuery<? super T> distQ)
Update the weight list.
|
Modifier and Type | Method and Description |
---|---|
protected void |
FastOPTICS.expandClusterOrder(DBID ipt,
ClusterOrder order,
DistanceQuery<V> dq,
FiniteProgress prog)
OPTICS algorithm for processing a point, but with different density
estimates
|
Modifier and Type | Method and Description |
---|---|
private long[][] |
PROCLUS.findDimensions(ArrayDBIDs medoids,
Relation<V> database,
DistanceQuery<V> distFunc,
RangeQuery<V> rangeQuery)
Determines the set of correlated dimensions for each medoid in the
specified medoid set.
|
private DataStore<DoubleDBIDList> |
PROCLUS.getLocalities(DBIDs medoids,
Relation<V> database,
DistanceQuery<V> distFunc,
RangeQuery<V> rangeQuery)
Computes the localities of the specified medoids: for each medoid m the
objects in the sphere centered at m with radius minDist are determined,
where minDist is the minimum distance between medoid m and any other medoid
m_i.
|
private ArrayDBIDs |
PROCLUS.greedy(DistanceQuery<V> distFunc,
DBIDs sampleSet,
int m,
Random random)
Returns a piercing set of k medoids from the specified sample set.
|
Modifier and Type | Method and Description |
---|---|
private void |
DWOF.initializeRadii(DBIDs ids,
KNNQuery<O> knnq,
DistanceQuery<O> distFunc,
WritableDoubleDataStore radii)
This method prepares a container for the radii of the objects and
initializes radii according to the equation:
initialRadii of a certain object = (absoluteMinDist of all objects) *
(avgDist of the object) / (minAvgDist of all objects)
|
Modifier and Type | Field and Description |
---|---|
private DistanceQuery<O> |
HilOut.distq
Distance query
|
Modifier and Type | Method and Description |
---|---|
protected void |
COF.computeAverageChainingDistances(KNNQuery<O> knnq,
DistanceQuery<O> dq,
DBIDs ids,
WritableDoubleDataStore acds)
Computes the average chaining distance, the average length of a path
through the given set of points to each target.
|
Modifier and Type | Method and Description |
---|---|
private void |
EvaluateRetrievalPerformance.computeDistances(ModifiableDoubleDBIDList nlist,
DBIDIter query,
DistanceQuery<O> distQuery,
Relation<O> relation)
Compute the distances to the neighbor objects.
|
private DoubleMinMax |
DistanceStatisticsWithClasses.exactMinMax(Relation<O> relation,
DistanceQuery<O> distFunc)
Compute the exact maximum and minimum.
|
private DoubleMinMax |
DistanceStatisticsWithClasses.sampleMinMax(Relation<O> relation,
DistanceQuery<O> distFunc)
Estimate minimum and maximum via sampling.
|
Modifier and Type | Method and Description |
---|---|
static <O> DistanceQuery<O> |
QueryUtil.getDistanceQuery(Database database,
DistanceFunction<? super O> distanceFunction,
Object... hints)
Get a distance query for a given distance function, automatically choosing
a relation.
|
<O> DistanceQuery<O> |
Database.getDistanceQuery(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
Object... hints)
Get the distance query for a particular distance function.
|
<O> DistanceQuery<O> |
AbstractDatabase.getDistanceQuery(Relation<O> objQuery,
DistanceFunction<? super O> distanceFunction,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
<O> KNNQuery<O> |
Database.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query.
|
<O> KNNQuery<O> |
AbstractDatabase.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
static <O> KNNQuery<O> |
QueryUtil.getLinearScanKNNQuery(DistanceQuery<O> distanceQuery)
Get a linear scan query for the given distance query.
|
static <O> RangeQuery<O> |
QueryUtil.getLinearScanRangeQuery(DistanceQuery<O> distanceQuery)
Get a linear scan query for the given distance query.
|
<O> RangeQuery<O> |
Database.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a range query object for the given distance query for radius-based
neighbor search.
|
<O> RangeQuery<O> |
AbstractDatabase.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
<O> RKNNQuery<O> |
Database.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a rKNN query object for the given distance query.
|
<O> RKNNQuery<O> |
AbstractDatabase.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Interface and Description |
---|---|
interface |
DistanceSimilarityQuery<O>
Interface that is a combination of distance and a similarity function.
|
Modifier and Type | Interface and Description |
---|---|
interface |
SpatialDistanceQuery<V extends SpatialComparable>
Query interface for spatial distance queries.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDatabaseDistanceQuery<O>
Run a database query in a database context.
|
class |
AbstractDistanceQuery<O>
A distance query serves as adapter layer for database and primitive
distances.
|
class |
DBIDDistanceQuery
Run a distance query based on DBIDs
|
class |
DBIDRangeDistanceQuery
Run a distance query based on DBIDRanges
|
class |
PrimitiveDistanceQuery<O>
Run a database query in a database context.
|
class |
PrimitiveDistanceSimilarityQuery<O>
Combination query class, for convenience.
|
class |
SpatialPrimitiveDistanceQuery<V extends SpatialComparable>
Distance query for spatial distance functions
|
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O> |
AbstractDistanceKNNQuery.distanceQuery
Hold the distance function to be used.
|
Constructor and Description |
---|
AbstractDistanceKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
LinearScanDistanceKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O> |
AbstractDistanceRangeQuery.distanceQuery
Hold the distance function to be used.
|
Constructor and Description |
---|
AbstractDistanceRangeQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
LinearScanDistanceRangeQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O> |
AbstractRKNNQuery.distanceQuery
Hold the distance function to be used.
|
Constructor and Description |
---|
AbstractRKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
LinearScanRKNNQuery(DistanceQuery<O> distanceQuery,
KNNQuery<O> knnQuery,
Integer maxk)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
DistanceQuery<O> |
AbstractRelation.getDistanceQuery(DistanceFunction<? super O> distanceFunction,
Object... hints) |
DistanceQuery<O> |
Relation.getDistanceQuery(DistanceFunction<? super O> distanceFunction,
Object... hints)
Get the distance query for a particular distance function.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
AbstractRelation.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
Relation.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query.
|
RangeQuery<O> |
AbstractRelation.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
Relation.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a range query object for the given distance query.
|
RKNNQuery<O> |
AbstractRelation.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
Relation.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a rKNN query object for the given distance query.
|
Modifier and Type | Interface and Description |
---|---|
static interface |
IndexBasedDistanceFunction.Instance<T,I extends Index>
Instance interface for Index based distance functions.
|
Modifier and Type | Class and Description |
---|---|
static class |
AbstractDatabaseDistanceFunction.Instance<O>
The actual instance bound to a particular database.
|
static class |
AbstractIndexBasedDistanceFunction.Instance<O,I extends Index,F extends DistanceFunction<? super O>>
The actual instance bound to a particular database.
|
static class |
SharedNearestNeighborJaccardDistanceFunction.Instance<T>
Actual instance for a dataset.
|
Modifier and Type | Method and Description |
---|---|
<O extends DBID> |
AbstractDBIDRangeDistanceFunction.instantiate(Relation<O> database) |
<T extends DBID> |
RandomStableDistanceFunction.instantiate(Relation<T> relation) |
<T extends O> |
AbstractPrimitiveDistanceFunction.instantiate(Relation<T> relation)
Instantiate with a database to get the actual distance query.
|
<T extends O> |
DistanceFunction.instantiate(Relation<T> relation)
Instantiate with a database to get the actual distance query.
|
Modifier and Type | Class and Description |
---|---|
static class |
AbstractSimilarityAdapter.Instance<O>
Inner proxy class for SNN distance function.
|
static class |
ArccosSimilarityAdapter.Instance<O>
Distance function instance
|
static class |
LinearAdapterLinear.Instance<O>
Distance function instance
|
static class |
LnSimilarityAdapter.Instance<O>
Distance function instance
|
Modifier and Type | Method and Description |
---|---|
<T extends O> |
ArccosSimilarityAdapter.instantiate(Relation<T> database) |
<T extends O> |
LnSimilarityAdapter.instantiate(Relation<T> database) |
<T extends O> |
LinearAdapterLinear.instantiate(Relation<T> database) |
abstract <T extends O> |
AbstractSimilarityAdapter.instantiate(Relation<T> database) |
Modifier and Type | Method and Description |
---|---|
<O extends DBID> |
FileBasedFloatDistanceFunction.instantiate(Relation<O> database) |
<O extends DBID> |
FileBasedDoubleDistanceFunction.instantiate(Relation<O> database) |
Modifier and Type | Method and Description |
---|---|
double |
EvaluateCIndex.evaluateClustering(Database db,
Relation<? extends O> rel,
DistanceQuery<O> dq,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateSilhouette.evaluateClustering(Database db,
Relation<O> rel,
DistanceQuery<O> dq,
Clustering<?> c)
Evaluate a single clustering.
|
Modifier and Type | Method and Description |
---|---|
DistanceQuery<O> |
DistanceIndex.getDistanceQuery(DistanceFunction<? super O> distanceFunction,
Object... hints)
Get a KNN query object for the given distance query and k.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
KNNIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query and k.
|
RangeQuery<O> |
RangeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a range query object for the given distance query and k.
|
RKNNQuery<O> |
RKNNIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query and k.
|
Constructor and Description |
---|
AbstractKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
AbstractRangeQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
Modifier and Type | Class and Description |
---|---|
private class |
PrecomputedDistanceMatrix.PrecomputedDistanceQuery
Distance query using the precomputed matrix.
|
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O> |
PrecomputedDistanceMatrix.distanceQuery
Nested distance query.
|
Modifier and Type | Method and Description |
---|---|
DistanceQuery<O> |
PrecomputedDistanceMatrix.getDistanceQuery(DistanceFunction<? super O> distanceFunction,
Object... hints) |
Modifier and Type | Field and Description |
---|---|
private DistanceQuery<O> |
InMemoryIDistanceIndex.distanceQuery
Distance query.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
InMemoryIDistanceIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
InMemoryIDistanceIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
protected static <O> DoubleIntPair[] |
InMemoryIDistanceIndex.rankReferencePoints(DistanceQuery<O> distanceQuery,
O obj,
ArrayDBIDs referencepoints)
Sort the reference points by distance to the query object
|
Constructor and Description |
---|
IDistanceKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
IDistanceRangeQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
InMemoryIDistanceIndex(Relation<O> relation,
DistanceQuery<O> distance,
KMedoidsInitialization<O> initialization,
int numref)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<V> |
InMemoryInvertedIndex.getKNNQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
RangeQuery<V> |
InMemoryInvertedIndex.getRangeQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
Constructor and Description |
---|
ArcCosineKNNQuery(DistanceQuery<V> distanceQuery)
Constructor.
|
ArcCosineRangeQuery(DistanceQuery<V> distanceQuery)
Constructor.
|
CosineKNNQuery(DistanceQuery<V> distanceQuery)
Constructor.
|
CosineRangeQuery(DistanceQuery<V> distanceQuery)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<V> |
InMemoryLSHIndex.Instance.getKNNQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
RangeQuery<V> |
InMemoryLSHIndex.Instance.getRangeQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
Constructor and Description |
---|
LSHKNNQuery(DistanceQuery<V> distanceQuery)
Constructor.
|
LSHRangeQuery(DistanceQuery<V> distanceQuery)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O> |
AbstractMaterializeKNNPreprocessor.distanceQuery
The distance query we used.
|
(package private) DistanceQuery<O> |
SpacefillingKNNPreprocessor.SpaceFillingKNNQuery.distq
Distance query to use for refinement
|
(package private) DistanceQuery<O> |
NaiveProjectedKNNPreprocessor.NaiveProjectedKNNQuery.distq
Distance query to use for refinement
|
Modifier and Type | Method and Description |
---|---|
DistanceQuery<O> |
AbstractMaterializeKNNPreprocessor.getDistanceQuery()
The distance query we used.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
SpacefillingKNNPreprocessor.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
AbstractMaterializeKNNPreprocessor.getKNNQuery(DistanceQuery<O> distQ,
Object... hints) |
KNNQuery<O> |
SpacefillingMaterializeKNNPreprocessor.getKNNQuery(DistanceQuery<O> distQ,
Object... hints) |
KNNQuery<O> |
NaiveProjectedKNNPreprocessor.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
MaterializeKNNAndRKNNPreprocessor.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Constructor and Description |
---|
NaiveProjectedKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
SpaceFillingKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
(package private) DistanceQuery<O> |
ProjectedIndex.ProjectedKNNQuery.distq
Distance query for refinement.
|
(package private) DistanceQuery<O> |
ProjectedIndex.ProjectedRKNNQuery.distq
Distance query for refinement.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
LatLngAsECEFIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
ProjectedIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
LngLatAsECEFIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
LatLngAsECEFIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
ProjectedIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
LngLatAsECEFIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
LatLngAsECEFIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
ProjectedIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
LngLatAsECEFIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Constructor and Description |
---|
ProjectedKNNQuery(DistanceQuery<O> distanceQuery,
KNNQuery<I> inner)
Constructor.
|
ProjectedRangeQuery(DistanceQuery<O> distanceQuery,
RangeQuery<I> inner)
Constructor.
|
ProjectedRKNNQuery(DistanceQuery<O> distanceQuery,
RKNNQuery<I> inner)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
private DistanceQuery<O> |
AbstractCoverTree.distanceQuery
Distance query, on the data relation.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
CoverTree.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
SimplifiedCoverTree.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
CoverTree.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
SimplifiedCoverTree.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Constructor and Description |
---|
CoverTreeKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
CoverTreeKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
CoverTreeRangeQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
CoverTreeRangeQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
private DistanceQuery<O> |
AbstractMkTree.distanceQuery
Distance query to use.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MkAppTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
MkAppTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
MkAppTreeIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MkCoPTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
MkCoPTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
MkCoPTreeIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MkMaxTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
MkMaxTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
MkMaxTreeIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MkTabTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
MkTabTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
MkTabTreeIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O> |
MTreeIndex.distanceQuery
The distance query.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
MTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
static <O> KNNQuery<O> |
MTreeQueryUtil.getKNNQuery(AbstractMTree<O,?,?,?> tree,
DistanceQuery<O> distanceQuery,
Object... hints)
Get an RTree knn query, using an optimized double implementation when
possible.
|
static <O> RangeQuery<O> |
MTreeQueryUtil.getRangeQuery(AbstractMTree<O,?,?,?> tree,
DistanceQuery<O> distanceQuery,
Object... hints)
Get an RTree knn query, using an optimized double implementation when
possible.
|
Constructor and Description |
---|
MetricalIndexKNNQuery(AbstractMTree<O,?,?,?> index,
DistanceQuery<O> distanceQuery)
Constructor.
|
MetricalIndexRangeQuery(AbstractMTree<O,?,?,?> index,
DistanceQuery<O> distanceQuery)
Constructor.
|
MkTreeRKNNQuery(AbstractMkTree<O,?,?,?> index,
DistanceQuery<O> distanceQuery)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MinimalisticMemoryKDTree.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
SmallMemoryKDTree.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
MinimalisticMemoryKDTree.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
SmallMemoryKDTree.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Constructor and Description |
---|
KDTreeKNNQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.
|
KDTreeKNNQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.
|
KDTreeRangeQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.
|
KDTreeRangeQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
DeLiCluTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
DeLiCluTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
FlatRStarTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
FlatRStarTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
RdKNNTree.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
RdKNNTree.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
RdKNNTree.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
RStarTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
RStarTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<V> |
PartialVAFile.getKNNQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
KNNQuery<V> |
VAFile.getKNNQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
RangeQuery<V> |
PartialVAFile.getRangeQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
RangeQuery<V> |
VAFile.getRangeQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
Constructor and Description |
---|
PartialVAFileKNNQuery(DistanceQuery<V> ddq,
double p,
long[] subspace)
Constructor.
|
PartialVAFileRangeQuery(DistanceQuery<V> ddq,
double p,
long[] subspace)
Constructor.
|
VAFileKNNQuery(DistanceQuery<V> distanceQuery,
double p)
Constructor.
|
VAFileRangeQuery(DistanceQuery<V> distanceQuery,
double p)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
static <O> KNNQuery<O> |
DatabaseUtil.precomputedKNNQuery(Database database,
Relation<O> relation,
DistanceQuery<O> dq,
int k)
Get (or create) a precomputed kNN query for the database.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.