Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation |
Affinity Propagation (AP) clustering.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering |
Biclustering algorithms.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical |
Hierarchical agglomerative clustering (HAC).
|
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction |
Extraction of partitional clusterings from hierarchical results.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.optics |
OPTICS family of clustering algorithms.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace |
Axis-parallel subspace clustering algorithms.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.meta |
Meta outlier detection algorithms: external scores, score rescaling.
|
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.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.database.relation |
Relations, materialized and virtual (views).
|
de.lmu.ifi.dbs.elki.datasource.bundle |
Object bundles - exchange container for multi-represented objects.
|
de.lmu.ifi.dbs.elki.evaluation.similaritymatrix |
Render a distance matrix to visualize a clustering-distance-combination.
|
de.lmu.ifi.dbs.elki.index.idistance |
iDistance is a distance based indexing technique, using a reference points embedding.
|
de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics |
Preprocessed index used by the FastOPTICS algorithm.
|
de.lmu.ifi.dbs.elki.index.preprocessed.knn |
Indexes providing KNN and rKNN data.
|
de.lmu.ifi.dbs.elki.index.preprocessed.snn |
Indexes providing nearest neighbor sets
|
de.lmu.ifi.dbs.elki.index.projected |
Projected indexes for data.
|
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.dimensionsimilarity |
Functions to compute the similarity of dimensions (or the interestingness of the combination).
|
de.lmu.ifi.dbs.elki.parallel |
Parallel processing core for ELKI.
|
de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike |
Common API for accessing objects that are "array-like", including lists, numerical vectors, database vectors and arrays.
|
de.lmu.ifi.dbs.elki.utilities.datastructures.unionfind | |
de.lmu.ifi.dbs.elki.utilities.scaling.outlier |
Scaling of Outlier scores, that require a statistical analysis of the occurring values
|
Modifier and Type | Method and Description |
---|---|
double[][] |
SimilarityBasedInitializationWithMedian.getSimilarityMatrix(Database db,
Relation<O> relation,
ArrayDBIDs ids) |
double[][] |
DistanceBasedInitializationWithMedian.getSimilarityMatrix(Database db,
Relation<O> relation,
ArrayDBIDs ids) |
double[][] |
AffinityPropagationInitialization.getSimilarityMatrix(Database db,
Relation<O> relation,
ArrayDBIDs ids)
Compute the initial similarity matrix.
|
Modifier and Type | Field and Description |
---|---|
protected ArrayDBIDs |
AbstractBiclustering.rowIDs
The row ids corresponding to the currently set
AbstractBiclustering.relation . |
Modifier and Type | Method and Description |
---|---|
protected ArrayDBIDs |
AbstractBiclustering.rowsBitsetToIDs(BitSet rows)
Convert a bitset into integer row ids.
|
protected ArrayDBIDs |
AbstractBiclustering.rowsBitsetToIDs(long[] rows)
Convert a bitset into integer row ids.
|
Modifier and Type | Field and Description |
---|---|
private ArrayDBIDs |
AbstractHDBSCAN.HDBSCANAdapter.ids
IDs to process.
|
Modifier and Type | Method and Description |
---|---|
private void |
CLINK.clinkstep4567(DBIDRef id,
ArrayDBIDs ids,
DBIDArrayIter it,
int n,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda,
WritableDoubleDataStore m)
Fourth to seventh step of CLINK: find best insertion
|
protected void |
AbstractHDBSCAN.convertToPointerRepresentation(ArrayDBIDs ids,
DoubleLongHeap heap,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda)
Convert spanning tree to a pointer representation.
|
double |
AbstractHDBSCAN.HDBSCANAdapter.distance(ArrayDBIDs data,
int ip,
int iq) |
protected void |
SLINK.process(DBIDRef id,
ArrayDBIDs ids,
DBIDArrayIter it,
int n,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda,
WritableDoubleDataStore m)
SLINK main loop.
|
protected void |
CLINK.process(DBIDRef id,
ArrayDBIDs ids,
DBIDArrayIter it,
int n,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda,
WritableDoubleDataStore m)
CLINK main loop, based on the SLINK main loop.
|
int |
AbstractHDBSCAN.HDBSCANAdapter.size(ArrayDBIDs data) |
Constructor and Description |
---|
AbstractHDBSCAN.HDBSCANAdapter(ArrayDBIDs ids,
DoubleDataStore coredists,
DistanceQuery<?> distq)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
private int |
ExtractFlatClusteringFromHierarchy.findSplit(ArrayDBIDs order,
DBIDArrayIter it,
DoubleDataStore lambda)
Find the splitting point in the ordered DBIDs list.
|
Modifier and Type | Method and Description |
---|---|
protected double |
CLARA.assignRemainingToNearestCluster(ArrayDBIDs means,
DBIDs ids,
DBIDs rids,
WritableIntegerDataStore assignment,
DistanceQuery<V> distQ)
Returns a list of clusters.
|
protected double |
KMedoidsPAM.assignToNearestCluster(ArrayDBIDs means,
DBIDs ids,
WritableDoubleDataStore nearest,
WritableDoubleDataStore second,
WritableIntegerDataStore assignment,
DistanceQuery<V> distQ)
Returns a list of clusters.
|
protected boolean |
KMedoidsEM.assignToNearestCluster(ArrayDBIDs means,
Mean[] mdist,
List<? extends ModifiableDBIDs> clusters,
DistanceQuery<V> distQ)
Returns a list of clusters.
|
Modifier and Type | Method and Description |
---|---|
ArrayDBIDs |
ClusterOrder.getDBIDs() |
Modifier and Type | Method and Description |
---|---|
private ArrayDBIDs |
PROCLUS.computeM_current(DBIDs m,
DBIDs m_best,
DBIDs m_bad,
Random random)
Computes the set of medoids in current iteration.
|
private ArrayDBIDs |
PROCLUS.greedy(DistanceQuery<V> distFunc,
DBIDs sampleSet,
int m,
Random random)
Returns a piercing set of k medoids from the specified sample set.
|
private ArrayDBIDs |
PROCLUS.initialSet(DBIDs sampleSet,
int k,
Random random)
Returns a set of k elements from the specified sample set.
|
Modifier and Type | Method and Description |
---|---|
private ArrayList<PROCLUS.PROCLUSCluster> |
PROCLUS.assignPoints(ArrayDBIDs m_current,
long[][] dimensions,
Relation<V> database)
Assigns the objects to the clusters.
|
private DBIDs |
PROCLUS.computeBadMedoids(ArrayDBIDs m_current,
ArrayList<PROCLUS.PROCLUSCluster> clusters,
int threshold)
Computes the bad medoids, where the medoid of a cluster with less than the
specified threshold of objects is bad.
|
private long[][] |
PROCLUS.findDimensions(ArrayDBIDs medoids,
Relation<V> database,
DistanceQuery<V> distFunc,
RangeQuery<V> rangeQuery)
Determines the set of correlated dimensions for each medoid in the
specified medoid set.
|
Modifier and Type | Method and Description |
---|---|
private ArrayList<ArrayDBIDs> |
HiCS.buildOneDimIndexes(Relation<? extends NumberVector> relation)
Calculates "index structures" for every attribute, i.e. sorts a
ModifiableArray of every DBID in the database for every dimension and
stores them in a list
|
Modifier and Type | Method and Description |
---|---|
private void |
HiCS.calculateContrast(Relation<? extends NumberVector> relation,
HiCS.HiCSSubspace subspace,
ArrayList<ArrayDBIDs> subspaceIndex,
Random random)
Calculates the actual contrast of a given subspace.
|
private Set<HiCS.HiCSSubspace> |
HiCS.calculateSubspaces(Relation<? extends NumberVector> relation,
ArrayList<ArrayDBIDs> subspaceIndex,
Random random)
Identifies high contrast subspaces in a given full-dimensional database.
|
Modifier and Type | Interface and Description |
---|---|
interface |
ArrayModifiableDBIDs
Array-oriented implementation of a modifiable DBID collection.
|
interface |
ArrayStaticDBIDs
Unmodifiable, indexed DBIDs.
|
interface |
DBID
Database ID object.
|
interface |
DBIDPair
Immutable pair of two DBIDs.
|
interface |
DBIDRange
Static DBID range.
|
interface |
DBIDVar
(Persistent) variable storing a DBID reference.
|
Modifier and Type | Class and Description |
---|---|
class |
EmptyDBIDs
Empty DBID collection.
|
Modifier and Type | Method and Description |
---|---|
static ArrayDBIDs |
DBIDUtil.ensureArray(DBIDs ids)
Ensure that the given DBIDs are array-indexable.
|
static ArrayDBIDs[] |
DBIDUtil.randomSplit(DBIDs oids,
int p,
Random random)
Randomly split IDs into
p partitions of almost-equal size. |
static ArrayDBIDs[] |
DBIDUtil.randomSplit(DBIDs ids,
int p,
RandomFactory rnd)
Randomly split IDs into
p partitions of almost-equal size. |
ArrayDBIDs |
EmptyDBIDs.slice(int begin,
int end) |
ArrayDBIDs |
ArrayDBIDs.slice(int begin,
int end)
Slice a subarray (as view, not copy!)
|
Modifier and Type | Class and Description |
---|---|
class |
UnmodifiableArrayDBIDs
Unmodifiable wrapper for DBIDs.
|
Modifier and Type | Field and Description |
---|---|
protected ArrayDBIDs |
MaskedDBIDs.data
Data storage.
|
private ArrayDBIDs |
UnmodifiableArrayDBIDs.inner
The DBIDs we wrap.
|
Modifier and Type | Method and Description |
---|---|
ArrayDBIDs |
UnmodifiableArrayDBIDs.slice(int begin,
int end) |
Constructor and Description |
---|
MaskedDBIDs(ArrayDBIDs data,
long[] bits,
boolean inverse)
Constructor.
|
UnmodifiableArrayDBIDs(ArrayDBIDs inner)
Constructor.
|
Modifier and Type | Interface and Description |
---|---|
interface |
IntegerArrayDBIDs
Trivial combination interface.
|
(package private) interface |
IntegerArrayStaticDBIDs
Combination of
ArrayStaticDBIDs and IntegerDBIDs . |
Modifier and Type | Class and Description |
---|---|
(package private) class |
ArrayModifiableIntegerDBIDs
Class using a primitive int[] array as storage.
|
private class |
ArrayModifiableIntegerDBIDs.Slice
Slice of an array.
|
(package private) class |
ArrayStaticIntegerDBIDs
Static (no modifications allowed) set of Database Object IDs.
|
private class |
ArrayStaticIntegerDBIDs.Slice
Slice of an array.
|
(package private) class |
IntegerDBID
Database ID object.
|
(package private) class |
IntegerDBIDPair
DBID pair using two ints for storage.
|
private class |
IntegerDBIDPair.Slice
Slice of an array.
|
(package private) class |
IntegerDBIDRange
Representing a DBID range allocation.
|
(package private) class |
IntegerDBIDVar
Variable for storing a single DBID reference.
|
class |
UnmodifiableIntegerArrayDBIDs
Unmodifiable wrapper for DBIDs.
|
Modifier and Type | Method and Description |
---|---|
ArrayDBIDs |
IntegerDBIDVar.slice(int begin,
int end) |
ArrayDBIDs |
IntegerDBIDRange.slice(int begin,
int end) |
ArrayDBIDs |
IntegerDBIDPair.slice(int begin,
int end) |
ArrayDBIDs |
IntegerDBID.slice(int begin,
int end) |
Modifier and Type | Method and Description |
---|---|
List<KNNList> |
PreprocessorKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<KNNList> |
LinearScanPrimitiveDistanceKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<KNNList> |
LinearScanEuclideanDistanceKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<KNNList> |
LinearScanDistanceKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<? extends KNNList> |
KNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k)
Bulk query method
|
List<? extends KNNList> |
AbstractDistanceKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
private void |
LinearScanDistanceKNNQuery.linearScanBatchKNN(ArrayDBIDs ids,
List<KNNHeap> heaps)
Linear batch knn for arbitrary distance functions.
|
Modifier and Type | Method and Description |
---|---|
List<? extends DoubleDBIDList> |
RKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k)
Bulk query method for reverse k nearest neighbors for ids.
|
List<? extends DoubleDBIDList> |
PreprocessorRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<? extends DoubleDBIDList> |
LinearScanRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
Modifier and Type | Method and Description |
---|---|
static double[][] |
RelationUtil.relationAsMatrix(Relation<? extends NumberVector> relation,
ArrayDBIDs ids)
Copy a relation into a double matrix.
|
Modifier and Type | Field and Description |
---|---|
private ArrayDBIDs |
MultipleObjectsBundle.ids
DBIDs for these objects, but may be null.
|
Modifier and Type | Method and Description |
---|---|
ArrayDBIDs |
MultipleObjectsBundle.getDBIDs()
Get the DBIDs, may be
null . |
Modifier and Type | Method and Description |
---|---|
void |
MultipleObjectsBundle.setDBIDs(ArrayDBIDs ids)
Set the DBID range for this bundle.
|
Modifier and Type | Field and Description |
---|---|
(package private) ArrayDBIDs |
ComputeSimilarityMatrixImage.SimilarityMatrix.ids
The database IDs used
|
Modifier and Type | Method and Description |
---|---|
ArrayDBIDs |
ComputeSimilarityMatrixImage.SimilarityMatrix.getIDs()
Get the IDs
|
Constructor and Description |
---|
ComputeSimilarityMatrixImage.SimilarityMatrix(RenderedImage img,
Relation<?> relation,
ArrayDBIDs ids)
Constructor
|
Modifier and Type | Field and Description |
---|---|
private ArrayDBIDs |
InMemoryIDistanceIndex.referencepoints
Reference points.
|
Modifier and Type | Method and Description |
---|---|
protected static <O> DoubleIntPair[] |
InMemoryIDistanceIndex.rankReferencePoints(DistanceQuery<O> distanceQuery,
O obj,
ArrayDBIDs referencepoints)
Sort the reference points by distance to the query object
|
Modifier and Type | Field and Description |
---|---|
(package private) ArrayList<ArrayDBIDs> |
RandomProjectedNeighborsAndDensities.splitsets
sets that resulted from recursive split of entire point set
|
Modifier and Type | Method and Description |
---|---|
protected ArrayDBIDs |
MaterializeKNNAndRKNNPreprocessor.affectedkNN(List<? extends KNNList> extract,
DBIDs remove)
Extracts and removes the DBIDs in the given collections.
|
protected ArrayDBIDs |
MaterializeKNNAndRKNNPreprocessor.affectedRkNN(List<? extends Collection<DoubleDBIDPair>> extract,
DBIDs remove)
Extracts and removes the DBIDs in the given collections.
|
private ArrayDBIDs |
MaterializeKNNPreprocessor.updateKNNsAfterDeletion(DBIDs ids)
Updates the kNNs of the RkNNs of the specified ids.
|
private ArrayDBIDs |
MaterializeKNNPreprocessor.updateKNNsAfterInsertion(DBIDs ids)
Updates the kNNs of the RkNNs of the specified ids.
|
private ArrayDBIDs |
MaterializeKNNAndRKNNPreprocessor.updateKNNsAndRkNNs(DBIDs ids)
Updates the kNNs and RkNNs after insertion of the specified ids.
|
Modifier and Type | Method and Description |
---|---|
List<KNNList> |
SpacefillingKNNPreprocessor.SpaceFillingKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<KNNList> |
NaiveProjectedKNNPreprocessor.NaiveProjectedKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
private void |
MaterializeKNNAndRKNNPreprocessor.materializeKNNAndRKNNs(ArrayDBIDs ids,
FiniteProgress progress)
Materializes the kNNs and RkNNs of the specified object IDs.
|
Modifier and Type | Method and Description |
---|---|
ArrayDBIDs |
SharedNearestNeighborPreprocessor.getNearestNeighborSet(DBIDRef objid) |
ArrayDBIDs |
SharedNearestNeighborIndex.getNearestNeighborSet(DBIDRef id)
Get the precomputed nearest neighbors
|
Modifier and Type | Method and Description |
---|---|
List<? extends KNNList> |
ProjectedIndex.ProjectedKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<? extends DoubleDBIDList> |
ProjectedIndex.ProjectedRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
Modifier and Type | Method and Description |
---|---|
List<? extends DoubleDBIDList> |
MkTreeRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
Modifier and Type | Method and Description |
---|---|
List<KNNList> |
RStarTreeKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<KNNList> |
EuclideanRStarTreeKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
Modifier and Type | Method and Description |
---|---|
private void |
RdKNNTree.adjustKNNDistance(RdKNNEntry entry,
ArrayDBIDs ids,
List<? extends KNNList> knnLists)
Adjusts the knn distance in the subtree of the specified root entry.
|
Modifier and Type | Method and Description |
---|---|
private ArrayList<ArrayDBIDs> |
HiCSDimensionSimilarity.buildOneDimIndexes(Relation<? extends NumberVector> relation,
DBIDs ids,
DimensionSimilarityMatrix matrix)
Calculates "index structures" for every attribute, i.e. sorts a
ModifiableArray of every DBID in the database for every dimension and
stores them in a list
|
Modifier and Type | Method and Description |
---|---|
private double |
HiCSDimensionSimilarity.calculateContrast(Relation<? extends NumberVector> relation,
DBIDs subset,
ArrayDBIDs subspaceIndex1,
ArrayDBIDs subspaceIndex2,
int dim1,
int dim2,
Random random)
Calculates the actual contrast of a given subspace
|
Modifier and Type | Field and Description |
---|---|
private ArrayDBIDs |
ParallelExecutor.BlockArrayRunner.ids
Array IDs to process
|
Modifier and Type | Method and Description |
---|---|
ArrayDBIDs |
ParallelExecutor.BlockArrayRunner.call() |
Constructor and Description |
---|
ParallelExecutor.BlockArrayRunner(ArrayDBIDs ids,
int start,
int end,
Processor[] procs)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
DBID |
ArrayDBIDsAdapter.get(ArrayDBIDs array,
int off)
Deprecated.
|
int |
ArrayDBIDsAdapter.size(ArrayDBIDs array) |
Modifier and Type | Field and Description |
---|---|
private ArrayDBIDs |
WeightedQuickUnionStaticDBIDs.ids
Object ID range.
|
Modifier and Type | Method and Description |
---|---|
private double[] |
SigmoidOutlierScalingFunction.MStepLevenbergMarquardt(double a,
double b,
ArrayDBIDs ids,
long[] t,
DoubleRelation scores)
M-Step using a modified Levenberg-Marquardt method.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.