Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan |
Generalized DBSCAN.
|
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.subspace |
Subspace outlier detection methods.
|
de.lmu.ifi.dbs.elki.database.query |
Database queries - computing distances, neighbors, similarities - API and general documentation.
|
de.lmu.ifi.dbs.elki.database.query.knn |
Prepared queries for k nearest neighbor (kNN) queries.
|
de.lmu.ifi.dbs.elki.database.query.rknn |
Prepared queries for reverse k nearest neighbor (rkNN) queries.
|
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.preprocessed.knn |
Indexes providing KNN and rKNN data.
|
de.lmu.ifi.dbs.elki.index.preprocessed.localpca |
Index using a preprocessed local PCA.
|
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.query |
Classes for performing queries (knn, range, ...) on metrical trees.
|
de.lmu.ifi.dbs.elki.math.linearalgebra.pca |
Principal Component Analysis (PCA) and Eigenvector processing.
|
de.lmu.ifi.dbs.elki.utilities.datastructures.heap |
Heap structures and variations such as bounded priority heaps.
|
Modifier and Type | Method and Description |
---|---|
boolean |
MinPtsCorePredicate.NeighborListInstance.isCorePoint(DBIDRef point,
List<? extends DistanceResultPair<?>> neighbors) |
Modifier and Type | Method and Description |
---|---|
private Map<DBID,List<DistanceResultPair<DoubleDistance>>> |
PROCLUS.getLocalities(DBIDs medoids,
Relation<V> database,
DistanceQuery<V,DoubleDistance> distFunc,
RangeQuery<V,DoubleDistance> 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.
|
Modifier and Type | Method and Description |
---|---|
protected List<DistanceResultPair<D>> |
ReferenceBasedOutlierDetection.computeDistanceVector(V refPoint,
Relation<V> database,
DistanceQuery<V,D> distFunc)
Computes for each object the distance to one reference point.
|
Modifier and Type | Method and Description |
---|---|
protected double |
ReferenceBasedOutlierDetection.computeDensity(List<DistanceResultPair<D>> referenceDists,
int index)
Computes the density of an object.
|
private ArrayModifiableDBIDs |
OnlineLOF.LOFKNNListener.mergeIDs(List<List<DistanceResultPair<D>>> queryResults,
DBIDs... ids)
Merges the ids of the query result with the specified ids.
|
Modifier and Type | Method and Description |
---|---|
private List<DoubleDistanceResultPair> |
OUTRES.refineRange(List<DistanceResultPair<DoubleDistance>> neighc,
double adjustedEps)
Refine a range query.
|
private List<DoubleDistanceResultPair> |
OUTRES.subsetNeighborhoodQuery(List<DistanceResultPair<DoubleDistance>> neighc,
DBID dbid,
PrimitiveDoubleDistanceFunction<? super V> df,
double adjustedEps,
OUTRES.KernelDensityEstimator kernel)
Refine neighbors within a subset.
|
Modifier and Type | Class and Description |
---|---|
class |
DoubleDistanceResultPair
Optimized DistanceResultPair that avoids/postpones an extra layer of boxing
for double values.
|
class |
GenericDistanceResultPair<D extends Distance<D>>
Trivial implementation using a generic pair.
|
Modifier and Type | Method and Description |
---|---|
int |
DistanceResultPair.compareByDistance(DistanceResultPair<D> o)
Compare value, but by distance only.
|
int |
GenericDistanceResultPair.compareByDistance(DistanceResultPair<D> o) |
int |
DoubleDistanceResultPair.compareByDistance(DistanceResultPair<DoubleDistance> o) |
int |
GenericDistanceResultPair.compareTo(DistanceResultPair<D> o) |
int |
DoubleDistanceResultPair.compareTo(DistanceResultPair<DoubleDistance> o) |
Constructor and Description |
---|
GenericDistanceDBIDList(Collection<? extends DistanceResultPair<D>> c)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
(package private) DistanceResultPair<?> |
KNNUtil.DBIDItr.cur
Current result
|
Modifier and Type | Field and Description |
---|---|
(package private) Iterator<? extends DistanceResultPair<?>> |
KNNUtil.DBIDIterator.itr
The real iterator.
|
(package private) Iterator<? extends DistanceResultPair<?>> |
KNNUtil.DBIDItr.itr
The real iterator.
|
(package private) Iterator<? extends DistanceResultPair<D>> |
KNNUtil.DistanceItr.itr
The real iterator.
|
Modifier and Type | Method and Description |
---|---|
DistanceResultPair<D> |
KNNResult.get(int index)
Direct object access.
|
DistanceResultPair<D> |
KNNUtil.KNNSubList.get(int index) |
DistanceResultPair<D> |
KNNUtil.KNNSubList.Itr.next() |
Modifier and Type | Method and Description |
---|---|
Iterator<DistanceResultPair<D>> |
KNNUtil.KNNSubList.iterator() |
Constructor and Description |
---|
KNNUtil.DBIDIterator(Iterator<? extends DistanceResultPair<?>> itr)
Constructor.
|
KNNUtil.DBIDItr(Iterator<? extends DistanceResultPair<?>> itr)
Constructor.
|
KNNUtil.DistanceItr(Iterator<? extends DistanceResultPair<D>> itr)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
List<List<DistanceResultPair<D>>> |
LinearScanRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<List<DistanceResultPair<D>>> |
RKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k)
Bulk query method for reverse k nearest neighbors for ids.
|
List<List<DistanceResultPair<D>>> |
PreprocessorRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<DistanceResultPair<D>> |
LinearScanRKNNQuery.getRKNNForDBID(DBIDRef id,
int k) |
List<DistanceResultPair<D>> |
RKNNQuery.getRKNNForDBID(DBIDRef id,
int k)
Get the reverse k nearest neighbors for a particular id.
|
List<DistanceResultPair<D>> |
PreprocessorRKNNQuery.getRKNNForDBID(DBIDRef id,
int k) |
abstract List<DistanceResultPair<D>> |
AbstractRKNNQuery.getRKNNForDBID(DBIDRef id,
int k) |
List<DistanceResultPair<D>> |
LinearScanRKNNQuery.getRKNNForObject(O obj,
int k) |
List<DistanceResultPair<D>> |
RKNNQuery.getRKNNForObject(O obj,
int k)
Get the reverse k nearest neighbors for a particular object.
|
List<DistanceResultPair<D>> |
PreprocessorRKNNQuery.getRKNNForObject(O obj,
int k) |
Modifier and Type | Field and Description |
---|---|
private Iterator<? extends DistanceResultPair<D>> |
ROC.DistanceResultAdapter.iter
Original Iterator
|
Modifier and Type | Method and Description |
---|---|
static <D extends Distance<D>> |
ROC.computeROCAUCDistanceResult(int size,
Cluster<?> clus,
Iterable<? extends DistanceResultPair<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,
Iterable<? extends DistanceResultPair<D>> nei)
Compute a ROC curves Area-under-curve for a QueryResult and a Cluster.
|
Constructor and Description |
---|
ROC.DistanceResultAdapter(Iterator<? extends DistanceResultPair<D>> iter)
Constructor
|
Modifier and Type | Field and Description |
---|---|
private WritableDataStore<SortedSet<DistanceResultPair<D>>> |
MaterializeKNNAndRKNNPreprocessor.materialized_RkNN
Additional data storage for RkNN.
|
Modifier and Type | Method and Description |
---|---|
List<DistanceResultPair<D>> |
MaterializeKNNAndRKNNPreprocessor.getRKNN(DBIDRef id)
Returns the materialized RkNNs of the specified id.
|
Modifier and Type | Method and Description |
---|---|
protected ArrayDBIDs |
MaterializeKNNPreprocessor.extractAndRemoveIDs(List<? extends Collection<DistanceResultPair<D>>> extraxt,
ArrayDBIDs remove)
Extracts and removes the DBIDs in the given collections.
|
Modifier and Type | Method and Description |
---|---|
protected abstract Collection<DistanceResultPair<DoubleDistance>> |
AbstractFilteredPCAIndex.objectsForPCA(DBID id)
Returns the objects to be considered within the PCA for the specified query
object.
|
Modifier and Type | Method and Description |
---|---|
abstract List<DistanceResultPair<D>> |
AbstractMkTree.reverseKNNQuery(DBIDRef id,
int k)
Performs a reverse k-nearest neighbor query for the given object ID.
|
Modifier and Type | Method and Description |
---|---|
private List<DistanceResultPair<D>> |
MkAppTree.doReverseKNNQuery(int k,
DBIDRef q)
Performs a reverse knn query.
|
List<DistanceResultPair<D>> |
MkAppTree.reverseKNNQuery(DBIDRef id,
int k)
Performs a reverse k-nearest neighbor query for the given object ID.
|
Modifier and Type | Method and Description |
---|---|
List<DistanceResultPair<D>> |
MkCoPTree.reverseKNNQuery(DBIDRef id,
int k)
Performs a reverse k-nearest neighbor query for the given object ID.
|
Modifier and Type | Method and Description |
---|---|
private void |
MkCoPTree.doReverseKNNQuery(int k,
DBIDRef q,
List<DistanceResultPair<D>> result,
ModifiableDBIDs candidates)
Performs a reverse knn query.
|
Modifier and Type | Method and Description |
---|---|
List<DistanceResultPair<D>> |
MkMaxTree.reverseKNNQuery(DBIDRef id,
int k)
Performs a reverse k-nearest neighbor query for the given object ID.
|
Modifier and Type | Method and Description |
---|---|
private void |
MkMaxTree.doReverseKNNQuery(DBIDRef q,
MkMaxTreeNode<O,D> node,
MkMaxEntry<D> node_entry,
List<DistanceResultPair<D>> result)
Performs a reverse k-nearest neighbor query in the specified subtree for
the given query object with k =
AbstractMkTreeUnified.k_max . |
Modifier and Type | Method and Description |
---|---|
List<DistanceResultPair<D>> |
MkTabTree.reverseKNNQuery(DBIDRef id,
int k) |
Modifier and Type | Method and Description |
---|---|
private void |
MkTabTree.doReverseKNNQuery(int k,
DBIDRef q,
MkTabEntry<D> node_entry,
MkTabTreeNode<O,D> node,
List<DistanceResultPair<D>> result)
Performs a k-nearest neighbor query in the specified subtree for the given
query object and the given parameter k.
|
Modifier and Type | Method and Description |
---|---|
List<List<DistanceResultPair<D>>> |
MkTreeRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<DistanceResultPair<D>> |
MkTreeRKNNQuery.getRKNNForDBID(DBIDRef id,
int k) |
List<DistanceResultPair<D>> |
MkTreeRKNNQuery.getRKNNForObject(O obj,
int k) |
Modifier and Type | Method and Description |
---|---|
private void |
MetricalIndexRangeQuery.doRangeQuery(DBID o_p,
AbstractMTreeNode<O,D,?,?> node,
DBID q,
D r_q,
List<DistanceResultPair<D>> result)
Performs a range query on the specified subtree.
|
private void |
MetricalIndexRangeQuery.doRangeQuery(DBID o_p,
AbstractMTreeNode<O,D,?,?> node,
O q,
D r_q,
List<DistanceResultPair<D>> result)
Performs a range query on the specified subtree.
|
Modifier and Type | Method and Description |
---|---|
private <D extends NumberDistance<?,?>> |
PCAFilteredAutotuningRunner.assertSortedByDistance(Collection<? extends DistanceResultPair<D>> results)
Ensure that the results are sorted by distance.
|
<D extends NumberDistance<?,?>> |
PCAFilteredAutotuningRunner.processQueryResult(Collection<? extends DistanceResultPair<D>> results,
Relation<? extends V> database) |
<D extends NumberDistance<?,?>> |
PCARunner.processQueryResult(Collection<? extends DistanceResultPair<D>> results,
Relation<? extends V> database)
Run PCA on a QueryResult Collection
|
<D extends NumberDistance<?,?>> |
PCAFilteredRunner.processQueryResult(Collection<? extends DistanceResultPair<D>> results,
Relation<? extends V> database)
Run PCA on a QueryResult Collection
|
<D extends NumberDistance<?,?>> |
AbstractCovarianceMatrixBuilder.processQueryResults(Collection<? extends DistanceResultPair<D>> results,
Relation<? extends V> database) |
<D extends NumberDistance<?,?>> |
CovarianceMatrixBuilder.processQueryResults(Collection<? extends DistanceResultPair<D>> results,
Relation<? extends V> database)
Compute Covariance Matrix for a QueryResult Collection
By default it will just collect the ids and run processIds
|
<D extends NumberDistance<?,?>> |
WeightedCovarianceMatrixBuilder.processQueryResults(Collection<? extends DistanceResultPair<D>> results,
Relation<? extends V> database,
int k)
Compute Covariance Matrix for a QueryResult Collection
By default it will just collect the ids and run processIds
|
<D extends NumberDistance<?,?>> |
AbstractCovarianceMatrixBuilder.processQueryResults(Collection<? extends DistanceResultPair<D>> results,
Relation<? extends V> database,
int k) |
<D extends NumberDistance<?,?>> |
CovarianceMatrixBuilder.processQueryResults(Collection<? extends DistanceResultPair<D>> results,
Relation<? extends V> database,
int k)
Compute Covariance Matrix for a QueryResult Collection
By default it will just collect the ids and run processIds
|
Modifier and Type | Method and Description |
---|---|
DistanceResultPair<D> |
KNNList.get(int index) |
DistanceResultPair<D> |
KNNList.Itr.next() |
Modifier and Type | Method and Description |
---|---|
Iterator<DistanceResultPair<D>> |
KNNList.iterator() |
ArrayList<DistanceResultPair<D>> |
KNNHeap.toSortedArrayList() |
Modifier and Type | Method and Description |
---|---|
int |
KNNHeap.Comp.compare(DistanceResultPair<D> o1,
DistanceResultPair<D> o2) |
int |
KNNHeap.Comp.compare(DistanceResultPair<D> o1,
DistanceResultPair<D> o2) |