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.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.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 |
Database object identification and ID group handling API.
|
de.lmu.ifi.dbs.elki.database.ids.generic |
Database object identification and ID group handling - generic implementations.
|
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, 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.evaluation.scores |
Evaluation of rankings and scorings.
|
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.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.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.index.tree.spatial.rstarvariants.rdknn | |
de.lmu.ifi.dbs.elki.math.linearalgebra.pca |
Principal Component Analysis (PCA) and Eigenvector processing.
|
Modifier and Type | Method and Description |
---|---|
DoubleDBIDList |
EpsilonNeighborPredicate.Instance.getNeighbors(DBIDRef reference) |
Modifier and Type | Method and Description |
---|---|
protected abstract M |
AbstractRangeQueryNeighborPredicate.computeLocalModel(DBIDRef id,
DoubleDBIDList neighbors,
Relation<O> relation)
Method to compute the actual data model.
|
protected PreDeConNeighborPredicate.PreDeConModel |
FourCNeighborPredicate.computeLocalModel(DBIDRef id,
DoubleDBIDList neighbors,
Relation<V> relation) |
protected PreDeConNeighborPredicate.PreDeConModel |
PreDeConNeighborPredicate.computeLocalModel(DBIDRef id,
DoubleDBIDList neighbors,
Relation<V> relation) |
protected COPACNeighborPredicate.COPACModel |
COPACNeighborPredicate.computeLocalModel(DBIDRef id,
DoubleDBIDList knnneighbors,
Relation<V> relation)
COPAC model computation
|
DBIDIter |
EpsilonNeighborPredicate.Instance.iterDBIDs(DoubleDBIDList neighbors) |
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
protected DoubleDBIDList |
ReferenceBasedOutlierDetection.computeDistanceVector(NumberVector refPoint,
Relation<? extends NumberVector> database,
PrimitiveDistanceQuery<? super NumberVector> distFunc)
Computes for each object the distance to one reference point.
|
Modifier and Type | Method and Description |
---|---|
protected double |
ReferenceBasedOutlierDetection.computeDensity(DoubleDBIDList referenceDists,
DoubleDBIDListIter iter,
int index)
Computes the density of an object.
|
protected void |
ReferenceBasedOutlierDetection.updateDensities(WritableDoubleDataStore rbod_score,
DoubleDBIDList referenceDists)
Update the density estimates for each object.
|
Modifier and Type | Method and Description |
---|---|
private ArrayModifiableDBIDs |
OnlineLOF.LOFKNNListener.mergeIDs(List<? extends DoubleDBIDList> queryResults,
DBIDs... ids)
Merges the ids of the query result with the specified ids.
|
Modifier and Type | Method and Description |
---|---|
private DoubleDBIDList |
OUTRES.refineRange(DoubleDBIDList neighc,
double adjustedEps)
Refine a range query.
|
private DoubleDBIDList |
OUTRES.subsetNeighborhoodQuery(DoubleDBIDList neighc,
DBIDRef dbid,
PrimitiveDistanceFunction<? super V> df,
double adjustedEps,
OUTRES.KernelDensityEstimator kernel)
Refine neighbors within a subset.
|
Modifier and Type | Method and Description |
---|---|
private DoubleDBIDList |
OUTRES.refineRange(DoubleDBIDList neighc,
double adjustedEps)
Refine a range query.
|
protected boolean |
OUTRES.relevantSubspace(long[] subspace,
DoubleDBIDList neigh,
OUTRES.KernelDensityEstimator kernel)
Subspace relevance test.
|
private DoubleDBIDList |
OUTRES.subsetNeighborhoodQuery(DoubleDBIDList neighc,
DBIDRef dbid,
PrimitiveDistanceFunction<? super V> df,
double adjustedEps,
OUTRES.KernelDensityEstimator kernel)
Refine neighbors within a subset.
|
protected double |
OUTRES.KernelDensityEstimator.subspaceDensity(long[] subspace,
DoubleDBIDList neighbors)
Compute density in the given subspace.
|
Modifier and Type | Field and Description |
---|---|
static SimpleTypeInformation<DoubleDBIDList> |
TypeUtil.NEIGHBORLIST
A list of neighbors.
|
Modifier and Type | Interface and Description |
---|---|
interface |
KNNList
Interface for kNN results.
|
interface |
ModifiableDoubleDBIDList
Modifiable API for Distance-DBID results
|
Modifier and Type | Class and Description |
---|---|
class |
KNNSubList
Sublist of an existing result to contain only the first k elements.
|
Modifier and Type | Interface and Description |
---|---|
interface |
IntegerDBIDKNNList
Combination interface for KNNList and IntegerDBIDs.
|
Modifier and Type | Class and Description |
---|---|
class |
DoubleIntegerDBIDKNNList
kNN list, but without automatic sorting.
|
(package private) class |
DoubleIntegerDBIDList
Class to store double distance, integer DBID results.
|
(package private) class |
DoubleIntegerDBIDListKNNHeap
Track the k nearest neighbors, with insertion sort to ensure the correct
order.
|
(package private) class |
DoubleIntegerDBIDPairKNNListHeap
KNN Heap implemented using a list of DoubleInt pair objects.
|
(package private) class |
DoubleIntegerDBIDPairList
Class to store double distance, integer DBID results.
|
class |
IntegerDBIDKNNSubList
Sublist of an existing result to contain only the first k elements.
|
Modifier and Type | Method and Description |
---|---|
DoubleDBIDList |
LinearScanPrimitiveDistanceRangeQuery.getRangeForDBID(DBIDRef id,
double range) |
DoubleDBIDList |
LinearScanEuclideanDistanceRangeQuery.getRangeForDBID(DBIDRef id,
double range) |
DoubleDBIDList |
RangeQuery.getRangeForDBID(DBIDRef id,
double range)
Get the neighbors for a particular id in a given query range
|
DoubleDBIDList |
LinearScanDistanceRangeQuery.getRangeForDBID(DBIDRef id,
double range) |
DoubleDBIDList |
AbstractDistanceRangeQuery.getRangeForDBID(DBIDRef id,
double range) |
DoubleDBIDList |
LinearScanPrimitiveDistanceRangeQuery.getRangeForObject(O obj,
double range) |
DoubleDBIDList |
LinearScanEuclideanDistanceRangeQuery.getRangeForObject(O obj,
double range) |
DoubleDBIDList |
RangeQuery.getRangeForObject(O obj,
double range)
Get the neighbors for a particular object in a given query range
|
DoubleDBIDList |
LinearScanDistanceRangeQuery.getRangeForObject(O obj,
double range) |
DoubleDBIDList |
AbstractDistanceRangeQuery.getRangeForObject(O obj,
double range) |
Modifier and Type | Method and Description |
---|---|
DoubleDBIDList |
LinearScanRKNNQuery.getRKNNForDBID(DBIDRef id,
int k) |
DoubleDBIDList |
PreprocessorRKNNQuery.getRKNNForDBID(DBIDRef id,
int k) |
abstract DoubleDBIDList |
AbstractRKNNQuery.getRKNNForDBID(DBIDRef id,
int k) |
DoubleDBIDList |
RKNNQuery.getRKNNForDBID(DBIDRef id,
int k)
Get the reverse k nearest neighbors for a particular id.
|
DoubleDBIDList |
LinearScanRKNNQuery.getRKNNForObject(O obj,
int k) |
DoubleDBIDList |
PreprocessorRKNNQuery.getRKNNForObject(O obj,
int k) |
DoubleDBIDList |
RKNNQuery.getRKNNForObject(O obj,
int k)
Get the reverse k nearest neighbors for a particular object.
|
Modifier and Type | Method and Description |
---|---|
List<? extends DoubleDBIDList> |
LinearScanRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<? extends DoubleDBIDList> |
PreprocessorRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<? extends DoubleDBIDList> |
RKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k)
Bulk query method for reverse k nearest neighbors for ids.
|
Modifier and Type | Method and Description |
---|---|
double |
ScoreEvaluation.evaluate(Cluster<?> clus,
DoubleDBIDList nei)
Evaluate given a cluster (of positive elements) and a scoring list.
|
double |
AbstractScoreEvaluation.evaluate(Cluster<?> clus,
DoubleDBIDList nei) |
double |
ScoreEvaluation.evaluate(DBIDs ids,
DoubleDBIDList nei)
Evaluate given a list of positives and a scoring.
|
double |
AbstractScoreEvaluation.evaluate(DBIDs ids,
DoubleDBIDList nei) |
Modifier and Type | Method and Description |
---|---|
DoubleDBIDList |
MaterializeKNNAndRKNNPreprocessor.getRKNN(DBIDRef id)
Returns the materialized RkNNs of the specified id.
|
Modifier and Type | Method and Description |
---|---|
protected abstract DoubleDBIDList |
AbstractFilteredPCAIndex.objectsForPCA(DBIDRef id)
Returns the objects to be considered within the PCA for the specified query
object.
|
Modifier and Type | Method and Description |
---|---|
DoubleDBIDList |
ProjectedIndex.ProjectedRKNNQuery.getRKNNForDBID(DBIDRef id,
int k) |
DoubleDBIDList |
ProjectedIndex.ProjectedRKNNQuery.getRKNNForObject(O obj,
int k) |
Modifier and Type | Method and Description |
---|---|
List<? extends DoubleDBIDList> |
ProjectedIndex.ProjectedRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
Modifier and Type | Method and Description |
---|---|
protected double |
AbstractCoverTree.maxDistance(DoubleDBIDList elems)
Find maximum in a list via scanning.
|
Constructor and Description |
---|
Node(DBIDRef r,
double maxDist,
DoubleDBIDList singletons)
Constructor for leaf node.
|
Node(DBIDRef r,
double maxDist,
double parentDist,
DoubleDBIDList singletons)
Constructor for leaf node.
|
Modifier and Type | Method and Description |
---|---|
abstract DoubleDBIDList |
AbstractMkTree.reverseKNNQuery(DBIDRef id,
int k)
Performs a reverse k-nearest neighbor query for the given object ID.
|
Modifier and Type | Method and Description |
---|---|
DoubleDBIDList |
MkAppTree.reverseKNNQuery(DBIDRef id,
int k)
Performs a reverse k-nearest neighbor query for the given object ID.
|
Modifier and Type | Method and Description |
---|---|
DoubleDBIDList |
MkCoPTree.reverseKNNQuery(DBIDRef id,
int k)
Performs a reverse k-nearest neighbor query for the given object ID.
|
Modifier and Type | Method and Description |
---|---|
DoubleDBIDList |
MkMaxTree.reverseKNNQuery(DBIDRef id,
int k)
Performs a reverse k-nearest neighbor query for the given object ID.
|
Modifier and Type | Method and Description |
---|---|
DoubleDBIDList |
MkTabTree.reverseKNNQuery(DBIDRef id,
int k) |
Modifier and Type | Method and Description |
---|---|
DoubleDBIDList |
MkTreeRKNNQuery.getRKNNForDBID(DBIDRef id,
int k) |
DoubleDBIDList |
MkTreeRKNNQuery.getRKNNForObject(O obj,
int k) |
Modifier and Type | Method and Description |
---|---|
List<? extends DoubleDBIDList> |
MkTreeRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
Modifier and Type | Method and Description |
---|---|
DoubleDBIDList |
RStarTreeRangeQuery.getRangeForDBID(DBIDRef id,
double range) |
DoubleDBIDList |
RStarTreeRangeQuery.getRangeForObject(O obj,
double range) |
Modifier and Type | Method and Description |
---|---|
DoubleDBIDList |
RdKNNTree.reverseKNNQuery(DBID oid,
int k,
SpatialPrimitiveDistanceFunction<? super O> distanceFunction,
KNNQuery<O> knnQuery) |
Modifier and Type | Method and Description |
---|---|
private void |
PCAFilteredAutotuningRunner.assertSortedByDistance(DoubleDBIDList results)
Ensure that the results are sorted by distance.
|
PCAFilteredResult |
PCAFilteredAutotuningRunner.processQueryResult(DoubleDBIDList results,
Relation<? extends NumberVector> database) |
PCAResult |
PCARunner.processQueryResult(DoubleDBIDList results,
Relation<? extends NumberVector> database)
Run PCA on a QueryResult Collection.
|
PCAFilteredResult |
PCAFilteredRunner.processQueryResult(DoubleDBIDList results,
Relation<? extends NumberVector> database)
Run PCA on a QueryResult Collection.
|
Matrix |
AbstractCovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results,
Relation<? extends NumberVector> database) |
Matrix |
CovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results,
Relation<? extends NumberVector> database)
Compute Covariance Matrix for a QueryResult Collection.
|
Matrix |
AbstractCovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results,
Relation<? extends NumberVector> database,
int k) |
Matrix |
CovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results,
Relation<? extends NumberVector> database,
int k)
Compute Covariance Matrix for a QueryResult Collection.
|
Matrix |
WeightedCovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results,
Relation<? extends NumberVector> database,
int k)
Compute Covariance Matrix for a QueryResult Collection.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.