| Modifier and Type | Class and Description |
|---|---|
class |
PrecomputedDistanceMatrix<O>
Distance matrix, for precomputing similarity for a small data set.
|
| Modifier and Type | Class and Description |
|---|---|
class |
InMemoryIDistanceIndex<O>
In-memory iDistance index, a metric indexing method using a reference point
embedding.
|
| Modifier and Type | Class and Description |
|---|---|
class |
InMemoryInvertedIndex<V extends NumberVector>
Simple index using inverted lists, for cosine distance only.
|
| Modifier and Type | Class and Description |
|---|---|
class |
InMemoryLSHIndex.Instance
Instance of a LSH index for a single relation.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractMaterializeKNNPreprocessor<O>
Abstract base class for KNN Preprocessors.
|
class |
CachedDoubleDistanceKNNPreprocessor<O>
Preprocessor that loads an existing cached kNN result.
|
class |
KNNJoinMaterializeKNNPreprocessor<V extends NumberVector>
Class to materialize the kNN using a spatial join on an R-tree.
|
class |
MaterializeKNNAndRKNNPreprocessor<O>
A preprocessor for annotation of the k nearest neighbors and the reverse k
nearest neighbors (and their distances) to each database object.
|
class |
MaterializeKNNPreprocessor<O>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
class |
MetricalIndexApproximationMaterializeKNNPreprocessor<O extends NumberVector,N extends Node<E>,E extends MTreeEntry>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
class |
NaiveProjectedKNNPreprocessor<O extends NumberVector>
Compute the approximate k nearest neighbors using 1 dimensional projections.
|
class |
NNDescent<O>
NN-desent (also known as KNNGraph) is an approximate nearest neighbor search
algorithm beginning with a random sample, then iteratively refining this
sample until.
|
class |
PartitionApproximationMaterializeKNNPreprocessor<O>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
class |
RandomSampleKNNPreprocessor<O>
Class that computed the kNN only on a random sample.
|
class |
SpacefillingKNNPreprocessor<O extends NumberVector>
Compute the nearest neighbors approximatively using space filling curves.
|
class |
SpacefillingMaterializeKNNPreprocessor<O extends NumberVector>
Compute the nearest neighbors approximatively using space filling curves.
|
class |
SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LatLngAsECEFIndex<O extends NumberVector>
Index a 2d data set (consisting of Lat/Lng pairs) by using a projection to 3D
coordinates (WGS-86 to ECEF).
|
class |
LngLatAsECEFIndex<O extends NumberVector>
Index a 2d data set (consisting of Lng/Lat pairs) by using a projection to 3D
coordinates (WGS-86 to ECEF).
|
class |
ProjectedIndex<O,I>
Class to index data in an arbitrary projection only.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CoverTree<O>
Cover tree data structure (in-memory).
|
class |
SimplifiedCoverTree<O>
Simplified cover tree data structure (in-memory).
|
| Modifier and Type | Class and Description |
|---|---|
class |
MkAppTreeIndex<O>
MkAppTree used as database index.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MkCoPTreeIndex<O>
MkCoPTree used as database index.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MkMaxTreeIndex<O>
MkMax tree
|
| Modifier and Type | Class and Description |
|---|---|
class |
MkTabTreeIndex<O>
MkTabTree used as database index.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MTreeIndex<O>
Class for using an m-tree as database index.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MinimalisticMemoryKDTree<O extends NumberVector>
Simple implementation of a static in-memory K-D-tree.
|
class |
SmallMemoryKDTree<O extends NumberVector>
Simple implementation of a static in-memory K-D-tree.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DeLiCluTreeIndex<O extends NumberVector>
The common use of the DeLiClu tree: indexing number vectors.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FlatRStarTreeIndex<O extends NumberVector>
The common use of the flat rstar tree: indexing number vectors.
|
| Modifier and Type | Class and Description |
|---|---|
class |
RdKNNTree<O extends NumberVector>
RDkNNTree is a spatial index structure based on the concepts of the R*-Tree
supporting efficient processing of reverse k nearest neighbor queries.
|
| Modifier and Type | Class and Description |
|---|---|
class |
RStarTreeIndex<O extends NumberVector>
The common use of the rstar tree: indexing number vectors.
|
| Modifier and Type | Class and Description |
|---|---|
class |
PartialVAFile<V extends NumberVector>
PartialVAFile.
|
class |
VAFile<V extends NumberVector>
Vector-approximation file (VAFile)
Reference:
R.
|
Copyright © 2019 ELKI Development Team. License information.