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.lof |
LOF family of outlier detection algorithms.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace |
Subspace outlier detection methods.
|
de.lmu.ifi.dbs.elki.data.type |
Data type information, also used for type restrictions.
|
de.lmu.ifi.dbs.elki.database.ids.distance |
Distance-DBID pairs, lists and heaps.
|
de.lmu.ifi.dbs.elki.database.ids.generic |
Database object identification and ID group handling - generic implementations.
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de.lmu.ifi.dbs.elki.database.ids.integer |
Integer-based DBID implementation --
do not use directly - always use
DBIDUtil . |
de.lmu.ifi.dbs.elki.database.query.range |
Prepared queries for ε-range queries.
|
de.lmu.ifi.dbs.elki.database.query.rknn |
Prepared queries for reverse k nearest neighbor (rkNN) queries.
|
de.lmu.ifi.dbs.elki.distance.distanceresultlist |
Classes for building and storing the results of distance-based 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.lsh |
Locality Sensitive Hashing
|
de.lmu.ifi.dbs.elki.index.preprocessed.localpca |
Index using a preprocessed local PCA.
|
de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj |
Index using a preprocessed local subspaces.
|
de.lmu.ifi.dbs.elki.index.projected |
Projected indexes for data.
|
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.index.tree.spatial.rstarvariants.query |
Queries on the R-Tree family of indexes: kNN and range queries.
|
de.lmu.ifi.dbs.elki.math.linearalgebra.pca |
Principal Component Analysis (PCA) and Eigenvector processing.
|
Modifier and Type | Method and Description |
---|---|
DistanceDBIDList<D> |
EpsilonNeighborPredicate.Instance.getNeighbors(DBIDRef reference) |
Modifier and Type | Method and Description |
---|---|
void |
EpsilonNeighborPredicate.Instance.addDBIDs(ModifiableDBIDs ids,
DistanceDBIDList<D> neighbors) |
Modifier and Type | Method and Description |
---|---|
private Map<DBID,DistanceDBIDList<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 DistanceDBIDList<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(DistanceDBIDList<D> referenceDists,
int index)
Computes the density of an object.
|
Modifier and Type | Method and Description |
---|---|
private ArrayModifiableDBIDs |
OnlineLOF.LOFKNNListener.mergeIDs(List<? extends DistanceDBIDList<D>> queryResults,
DBIDs... ids)
Merges the ids of the query result with the specified ids.
|
Modifier and Type | Method and Description |
---|---|
private DoubleDistanceDBIDList |
OUTRES.refineRange(DistanceDBIDList<DoubleDistance> neighc,
double adjustedEps)
Refine a range query.
|
private DoubleDistanceDBIDList |
OUTRES.subsetNeighborhoodQuery(DistanceDBIDList<DoubleDistance> neighc,
DBIDRef dbid,
PrimitiveDoubleDistanceFunction<? super V> df,
double adjustedEps,
OUTRES.KernelDensityEstimator kernel)
Refine neighbors within a subset.
|
Modifier and Type | Field and Description |
---|---|
static SimpleTypeInformation<DistanceDBIDList<?>> |
TypeUtil.NEIGHBORLIST
A list of neighbors.
|
Modifier and Type | Interface and Description |
---|---|
interface |
DoubleDistanceDBIDList
An object containing Double-DBID-Pairs.
|
interface |
DoubleDistanceKNNList
Double-valued KNN result.
|
interface |
KNNList<D extends Distance<D>>
Interface for kNN results.
|
interface |
ModifiableDistanceDBIDList<D extends Distance<D>>
Modifiable API for Distance-DBID results
|
interface |
ModifiableDoubleDistanceDBIDList
An object containing Double-DBID-Pairs.
|
Modifier and Type | Class and Description |
---|---|
class |
DoubleDistanceDBIDPairList
Default class to keep a list of distance-object pairs.
|
Modifier and Type | Class and Description |
---|---|
class |
DistanceDBIDPairKNNList<D extends Distance<D>>
Finalized KNN List.
|
class |
DoubleDistanceDBIDPairKNNList
Finalized KNN List.
|
class |
DoubleDistanceDBIDPairKNNListHeap
Finalized KNN List.
|
class |
DoubleDistanceKNNSubList
Sublist of an existing result to contain only the first k elements.
|
class |
GenericDistanceDBIDList<D extends Distance<D>>
Default class to keep a list of distance-object pairs.
|
class |
KNNSubList<D extends Distance<D>>
Sublist of an existing result to contain only the first k elements.
|
Modifier and Type | Class and Description |
---|---|
class |
DoubleDistanceIntegerDBIDKNNList
kNN list, but without automatic sorting.
|
class |
DoubleDistanceIntegerDBIDList
Class to store double distance, integer DBID results.
|
class |
DoubleDistanceIntegerDBIDPairKNNListHeap
Finalized KNN List.
|
class |
DoubleDistanceIntegerDBIDPairList
Class to store double distance, integer DBID results.
|
class |
DoubleDistanceIntegerDBIDSortedKNNList
Track the k nearest neighbors, with insertion sort to ensure the correct
order.
|
Modifier and Type | Method and Description |
---|---|
DistanceDBIDList<D> |
LinearScanPrimitiveDistanceRangeQuery.getRangeForDBID(DBIDRef id,
D range) |
DistanceDBIDList<D> |
RangeQuery.getRangeForDBID(DBIDRef id,
D range)
Get the nearest neighbors for a particular id in a given query range
|
DistanceDBIDList<D> |
LinearScanDistanceRangeQuery.getRangeForDBID(DBIDRef id,
D range) |
DistanceDBIDList<D> |
AbstractDistanceRangeQuery.getRangeForDBID(DBIDRef id,
D range) |
DistanceDBIDList<DoubleDistance> |
DoubleOptimizedDistanceRangeQuery.getRangeForDBID(DBIDRef id,
DoubleDistance range) |
DistanceDBIDList<D> |
LinearScanPrimitiveDistanceRangeQuery.getRangeForObject(O obj,
D range) |
DistanceDBIDList<D> |
RangeQuery.getRangeForObject(O obj,
D range)
Get the nearest neighbors for a particular object in a given query range
|
DistanceDBIDList<D> |
LinearScanDistanceRangeQuery.getRangeForObject(O obj,
D range) |
abstract DistanceDBIDList<D> |
AbstractDistanceRangeQuery.getRangeForObject(O obj,
D range) |
DistanceDBIDList<DoubleDistance> |
DoubleOptimizedDistanceRangeQuery.getRangeForObject(O obj,
DoubleDistance range) |
Modifier and Type | Method and Description |
---|---|
DistanceDBIDList<D> |
LinearScanRKNNQuery.getRKNNForDBID(DBIDRef id,
int k) |
DistanceDBIDList<D> |
PreprocessorRKNNQuery.getRKNNForDBID(DBIDRef id,
int k) |
abstract DistanceDBIDList<D> |
AbstractRKNNQuery.getRKNNForDBID(DBIDRef id,
int k) |
DistanceDBIDList<D> |
RKNNQuery.getRKNNForDBID(DBIDRef id,
int k)
Get the reverse k nearest neighbors for a particular id.
|
DistanceDBIDList<D> |
LinearScanRKNNQuery.getRKNNForObject(O obj,
int k) |
DistanceDBIDList<D> |
PreprocessorRKNNQuery.getRKNNForObject(O obj,
int k) |
DistanceDBIDList<D> |
RKNNQuery.getRKNNForObject(O obj,
int k)
Get the reverse k nearest neighbors for a particular object.
|
Modifier and Type | Method and Description |
---|---|
List<? extends DistanceDBIDList<D>> |
PreprocessorRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<? extends DistanceDBIDList<D>> |
RKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k)
Bulk query method for reverse k nearest neighbors for ids.
|
Modifier and Type | Method and Description |
---|---|
static String |
DistanceDBIDResultUtil.toString(DistanceDBIDList<?> res) |
Modifier and Type | Method and Description |
---|---|
static <D extends Distance<D>> |
ROC.computeROCAUCDistanceResult(int size,
Cluster<?> clus,
DistanceDBIDList<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,
DistanceDBIDList<D> nei)
Compute a ROC curves Area-under-curve for a QueryResult and a Cluster.
|
Modifier and Type | Method and Description |
---|---|
DistanceDBIDList<D> |
InMemoryLSHIndex.Instance.LSHRangeQuery.getRangeForObject(V obj,
D range) |
Modifier and Type | Method and Description |
---|---|
protected abstract DistanceDBIDList<DoubleDistance> |
AbstractFilteredPCAIndex.objectsForPCA(DBIDRef id)
Returns the objects to be considered within the PCA for the specified query
object.
|
protected DistanceDBIDList<DoubleDistance> |
RangeQueryFilteredPCAIndex.objectsForPCA(DBIDRef id) |
Modifier and Type | Method and Description |
---|---|
protected abstract P |
AbstractSubspaceProjectionIndex.computeProjection(DBIDRef id,
DistanceDBIDList<D> neighbors,
Relation<NV> relation)
This method implements the type of variance analysis to be computed for a
given point.
|
protected PCAFilteredResult |
FourCSubspaceIndex.computeProjection(DBIDRef id,
DistanceDBIDList<D> neighbors,
Relation<V> database) |
protected SubspaceProjectionResult |
PreDeConSubspaceIndex.computeProjection(DBIDRef id,
DistanceDBIDList<D> neighbors,
Relation<V> database) |
Modifier and Type | Method and Description |
---|---|
DistanceDBIDList<D> |
ProjectedIndex.ProjectedRangeQuery.getRangeForDBID(DBIDRef id,
D range) |
DistanceDBIDList<D> |
ProjectedIndex.ProjectedRangeQuery.getRangeForObject(O obj,
D range) |
DistanceDBIDList<D> |
ProjectedIndex.ProjectedRKNNQuery.getRKNNForDBID(DBIDRef id,
int k) |
DistanceDBIDList<D> |
ProjectedIndex.ProjectedRKNNQuery.getRKNNForObject(O obj,
int k) |
Modifier and Type | Method and Description |
---|---|
List<? extends DistanceDBIDList<D>> |
ProjectedIndex.ProjectedRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
Modifier and Type | Method and Description |
---|---|
abstract DistanceDBIDList<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 |
---|---|
DistanceDBIDList<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 |
---|---|
DistanceDBIDList<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 |
---|---|
DistanceDBIDList<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 |
---|---|
DistanceDBIDList<D> |
MkTabTree.reverseKNNQuery(DBIDRef id,
int k) |
Modifier and Type | Method and Description |
---|---|
DistanceDBIDList<D> |
MetricalIndexRangeQuery.getRangeForObject(O obj,
D range) |
DistanceDBIDList<DoubleDistance> |
DoubleDistanceMetricalIndexRangeQuery.getRangeForObject(O obj,
DoubleDistance range) |
DistanceDBIDList<D> |
MkTreeRKNNQuery.getRKNNForDBID(DBIDRef id,
int k) |
DistanceDBIDList<D> |
MkTreeRKNNQuery.getRKNNForObject(O obj,
int k) |
Modifier and Type | Method and Description |
---|---|
List<? extends DistanceDBIDList<D>> |
MkTreeRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
Modifier and Type | Method and Description |
---|---|
protected DistanceDBIDList<D> |
GenericRStarTreeRangeQuery.doRangeQuery(O object,
D epsilon)
Perform the actual query process.
|
DistanceDBIDList<D> |
GenericRStarTreeRangeQuery.getRangeForObject(O obj,
D range) |
DistanceDBIDList<DoubleDistance> |
DoubleDistanceRStarTreeRangeQuery.getRangeForObject(O obj,
DoubleDistance range) |
Modifier and Type | Method and Description |
---|---|
private <D extends NumberDistance<D,?>> |
PCAFilteredAutotuningRunner.assertSortedByDistance(DistanceDBIDList<D> results)
Ensure that the results are sorted by distance.
|
<D extends NumberDistance<D,?>> |
PCAFilteredAutotuningRunner.processQueryResult(DistanceDBIDList<D> results,
Relation<? extends V> database) |
<D extends NumberDistance<D,?>> |
PCARunner.processQueryResult(DistanceDBIDList<D> results,
Relation<? extends V> database)
Run PCA on a QueryResult Collection.
|
<D extends NumberDistance<D,?>> |
PCAFilteredRunner.processQueryResult(DistanceDBIDList<D> results,
Relation<? extends V> database)
Run PCA on a QueryResult Collection.
|
<D extends NumberDistance<D,?>> |
AbstractCovarianceMatrixBuilder.processQueryResults(DistanceDBIDList<D> results,
Relation<? extends V> database) |
<D extends NumberDistance<D,?>> |
CovarianceMatrixBuilder.processQueryResults(DistanceDBIDList<D> results,
Relation<? extends V> database)
Compute Covariance Matrix for a QueryResult Collection.
|
<D extends NumberDistance<D,?>> |
AbstractCovarianceMatrixBuilder.processQueryResults(DistanceDBIDList<D> results,
Relation<? extends V> database,
int k) |
<D extends NumberDistance<D,?>> |
CovarianceMatrixBuilder.processQueryResults(DistanceDBIDList<D> results,
Relation<? extends V> database,
int k)
Compute Covariance Matrix for a QueryResult Collection.
|
<D extends NumberDistance<D,?>> |
WeightedCovarianceMatrixBuilder.processQueryResults(DistanceDBIDList<D> results,
Relation<? extends V> database,
int k)
Compute Covariance Matrix for a QueryResult Collection.
|