| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.algorithm |
Algorithms suitable as a task for the
KDDTask main routine. |
| de.lmu.ifi.dbs.elki.database.query.knn |
Prepared queries for k nearest neighbor (kNN) queries.
|
| 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 | |
| de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab | |
| 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.rstarvariants.query |
Queries on the R-Tree family of indexes: kNN and range queries.
|
| de.lmu.ifi.dbs.elki.utilities.datastructures.heap |
Heap structures and variations such as bounded priority heaps.
|
| Modifier and Type | Method and Description |
|---|---|
private D |
KNNJoin.processDataPages(DistanceQuery<V,D> distQ,
N pr,
N ps,
WritableDataStore<KNNHeap<D>> knnLists,
D pr_knn_distance)
Processes the two data pages pr and ps and determines the k-nearest
neighbors of pr in ps.
|
| Modifier and Type | Method and Description |
|---|---|
void |
KNNQuery.getKNNForBulkHeaps(Map<DBID,KNNHeap<D>> heaps)
Bulk query method configured by a map.
|
void |
PreprocessorKNNQuery.getKNNForBulkHeaps(Map<DBID,KNNHeap<D>> heaps) |
void |
LinearScanKNNQuery.getKNNForBulkHeaps(Map<DBID,KNNHeap<D>> heaps) |
void |
LinearScanPrimitiveDistanceKNNQuery.getKNNForBulkHeaps(Map<DBID,KNNHeap<D>> heaps) |
private void |
LinearScanKNNQuery.linearScanBatchKNN(ArrayDBIDs ids,
List<KNNHeap<D>> heaps)
Linear batch knn for arbitrary distance functions.
|
protected void |
LinearScanPrimitiveDistanceKNNQuery.linearScanBatchKNN(List<O> objs,
List<KNNHeap<D>> heaps)
Perform a linear scan batch kNN for primitive distance functions.
|
protected void |
LinearScanRawDoubleDistanceKNNQuery.linearScanBatchKNN(List<O> objs,
List<KNNHeap<DoubleDistance>> heaps) |
| Modifier and Type | Method and Description |
|---|---|
protected void |
AbstractMTree.doKNNQuery(DBID q,
KNNHeap<D> knnList)
Performs a k-nearest neighbor query for the given FeatureVector with the
given parameter k and the according distance function.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
AbstractMTree.batchNN(N node,
DBIDs ids,
Map<DBID,KNNHeap<D>> knnLists)
Deprecated.
Change to use by-object NN lookups instead.
|
| Modifier and Type | Method and Description |
|---|---|
protected abstract void |
AbstractMkTreeUnified.kNNdistanceAdjustment(E entry,
Map<DBID,KNNHeap<D>> knnLists)
Performs a distance adjustment in the subtree of the specified root entry.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
MkMaxTree.preInsert(MkMaxEntry<D> q,
MkMaxEntry<D> nodeEntry,
KNNHeap<D> knns_q)
Adapts the knn distances before insertion of entry q.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
MkMaxTree.kNNdistanceAdjustment(MkMaxEntry<D> entry,
Map<DBID,KNNHeap<D>> knnLists)
Adjusts the knn distance in the subtree of the specified root entry.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
MkTabTree.kNNdistanceAdjustment(MkTabEntry<D> entry,
Map<DBID,KNNHeap<D>> knnLists) |
| Modifier and Type | Method and Description |
|---|---|
protected void |
MetricalIndexKNNQuery.doKNNQuery(O q,
KNNHeap<D> knnList)
Performs a k-nearest neighbor query for the given FeatureVector with the
given parameter k and the according distance function.
|
| Modifier and Type | Method and Description |
|---|---|
void |
MetricalIndexKNNQuery.getKNNForBulkHeaps(Map<DBID,KNNHeap<D>> heaps) |
| Modifier and Type | Method and Description |
|---|---|
protected void |
GenericRStarTreeKNNQuery.doKNNQuery(O object,
KNNHeap<D> knnList)
Performs a k-nearest neighbor query for the given NumberVector with the
given parameter k and the according distance function.
|
protected void |
DoubleDistanceRStarTreeKNNQuery.doKNNQuery(O object,
KNNHeap<DoubleDistance> knnList)
Performs a k-nearest neighbor query for the given NumberVector with the
given parameter k and the according distance function.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
GenericRStarTreeKNNQuery.batchNN(AbstractRStarTreeNode<?,?> node,
Map<DBID,KNNHeap<D>> knnLists)
Performs a batch knn query.
|
protected void |
DoubleDistanceRStarTreeKNNQuery.batchNN(AbstractRStarTreeNode<?,?> node,
Map<DBID,KNNHeap<DoubleDistance>> knnLists)
Performs a batch knn query.
|
void |
GenericRStarTreeKNNQuery.getKNNForBulkHeaps(Map<DBID,KNNHeap<D>> heaps) |
void |
DoubleDistanceRStarTreeKNNQuery.getKNNForBulkHeaps(Map<DBID,KNNHeap<DoubleDistance>> heaps) |
| Constructor and Description |
|---|
KNNList(KNNHeap<D> heap,
D maxdist)
Constructor, to be called from KNNHeap only!
|