| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
Correlation clustering algorithms
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan |
Generalized DBSCAN
Generalized DBSCAN is an abstraction of the original DBSCAN idea,
that allows the use of arbitrary "neighborhood" and "core point" predicates.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical |
Hierarchical agglomerative clustering (HAC).
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization |
Initialization strategies for k-means.
|
| 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
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.anglebased |
Angle-based outlier detection algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.clustering |
Clustering based outlier detection.
|
| 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.intrinsic |
Outlier detection algorithms based on intrinsic dimensionality.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.lof |
LOF family of outlier detection algorithms
|
| de.lmu.ifi.dbs.elki.database.datastore |
General data store layer API (along the lines of
Map<DBID, T> - use everywhere!) |
| de.lmu.ifi.dbs.elki.database.datastore.memory |
Memory data store implementation for ELKI.
|
| de.lmu.ifi.dbs.elki.index.invertedlist |
Indexes using inverted lists.
|
| de.lmu.ifi.dbs.elki.parallel.processor |
Processor API of ELKI, and some essential shared processors.
|
| Modifier and Type | Field and Description |
|---|---|
private WritableDoubleDataStore |
HiCO.Instance.tmpDistance
Temporary storage of distances.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
LSDBC.fillDensities(KNNQuery<O> knnq,
DBIDs ids,
WritableDoubleDataStore dens)
Collect all densities into an array for sorting.
|
private boolean |
LSDBC.isLocalMaximum(double kdist,
DBIDs neighbors,
WritableDoubleDataStore kdists)
Test if a point is a local density maximum.
|
| Modifier and Type | Field and Description |
|---|---|
private WritableDoubleDataStore |
PointerHierarchyRepresentationResult.Sorter.maxheight
Maximum height.
|
protected WritableDoubleDataStore |
PointerHierarchyRepresentationBuilder.parentDistance
Distance to the parent object.
|
| Modifier and Type | Method and Description |
|---|---|
protected WritableDoubleDataStore |
AbstractHDBSCAN.computeCoreDists(DBIDs ids,
KNNQuery<O> knnQ,
int minPts)
Compute the core distances for all objects.
|
private WritableDoubleDataStore |
PointerHierarchyRepresentationResult.computeMaxHeight()
Compute the maximum height of nodes.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
CLINK.clinkstep3(DBIDRef id,
DBIDArrayIter i,
int n,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda,
WritableDoubleDataStore m)
Third step: Determine the values for P and L
|
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
|
private void |
CLINK.clinkstep8(DBIDRef id,
DBIDArrayIter it,
int n,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda,
WritableDoubleDataStore m)
Update hierarchy.
|
protected void |
AbstractHDBSCAN.convertToPointerRepresentation(ArrayDBIDs ids,
DoubleLongHeap heap,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda)
Convert spanning tree to a pointer representation.
|
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.
|
protected void |
SLINK.process(DBIDRef id,
ArrayDBIDs ids,
DBIDArrayIter it,
int n,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda,
WritableDoubleDataStore m)
SLINK main loop.
|
private void |
SLINK.slinkstep3(DBIDRef id,
DBIDArrayIter it,
int n,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda,
WritableDoubleDataStore m)
Third step: Determine the values for P and L
|
private void |
SLINK.slinkstep4(DBIDRef id,
DBIDArrayIter it,
int n,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda)
Fourth step: Actualize the clusters if necessary
|
private void |
SLINKHDBSCANLinearMemory.step1(DBIDRef id,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda)
First step: Initialize P(id) = id, L(id) = infinity.
|
private void |
SLINK.step2(DBIDRef id,
DBIDArrayIter it,
int n,
DistanceQuery<? super O> distQuery,
WritableDoubleDataStore m)
Second step: Determine the pairwise distances from all objects in the
pointer representation to the new object with the specified id.
|
private void |
SLINKHDBSCANLinearMemory.step2(DBIDRef id,
DBIDs processedIDs,
DistanceQuery<? super O> distQuery,
DoubleDataStore coredists,
WritableDoubleDataStore m)
Second step: Determine the pairwise distances from all objects in the
pointer representation to the new object with the specified id.
|
private void |
SLINK.step2primitive(DBIDRef id,
DBIDArrayIter it,
int n,
Relation<? extends O> relation,
PrimitiveDistanceFunction<? super O> distFunc,
WritableDoubleDataStore m)
Second step: Determine the pairwise distances from all objects in the
pointer representation to the new object with the specified id.
|
private void |
SLINKHDBSCANLinearMemory.step3(DBIDRef id,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda,
DBIDs processedIDs,
WritableDoubleDataStore m)
Third step: Determine the values for P and L
|
private void |
SLINKHDBSCANLinearMemory.step4(DBIDRef id,
WritableDBIDDataStore pi,
WritableDoubleDataStore lambda,
DBIDs processedIDs)
Fourth step: Actualize the clusters if necessary
|
| Constructor and Description |
|---|
Sorter(WritableDoubleDataStore maxheight)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) WritableDoubleDataStore |
KMeansHamerly.Instance.lower
Lower bounding distance
|
(package private) WritableDoubleDataStore |
KMedoidsPAM.Instance.nearest
Distance to the nearest medoid of each point.
|
(package private) WritableDoubleDataStore |
CLARANS.Assignment.nearest
Distance to the nearest medoid of each point.
|
(package private) WritableDoubleDataStore |
KMedoidsPAM.Instance.second
Distance to the second nearest medoid.
|
(package private) WritableDoubleDataStore |
CLARANS.Assignment.second
Distance to the second nearest medoid.
|
(package private) WritableDoubleDataStore |
KMeansHamerly.Instance.upper
Upper bounding distance
|
(package private) WritableDoubleDataStore |
KMeansSimplifiedElkan.Instance.upper
Upper bounds
|
| Modifier and Type | Method and Description |
|---|---|
protected double |
KMedoidsPAMReynolds.Instance.computeReassignmentCost(DBIDRef h,
WritableDoubleDataStore tnearest)
Compute the reassignment cost, for all medoids in one pass.
|
protected double |
KMedoidsPAMReynolds.Instance.computeRemovalCost(int i,
WritableDoubleDataStore tnearest)
Compute the cost of removing a medoid just once.
|
| Modifier and Type | Method and Description |
|---|---|
(package private) static void |
KMeansPlusPlusInitialMeans.chooseRemaining(DBIDs ids,
DistanceQuery<?> distQ,
int k,
ArrayModifiableDBIDs means,
WritableDoubleDataStore weights,
double weightsum,
java.util.Random random)
Choose remaining means, weighted by distance.
|
(package private) static void |
KMeansPlusPlusInitialMeans.chooseRemaining(Relation<? extends NumberVector> relation,
DBIDs ids,
DistanceQuery<NumberVector> distQ,
int k,
java.util.List<NumberVector> means,
WritableDoubleDataStore weights,
double weightsum,
java.util.Random random)
Choose remaining means, weighted by distance.
|
protected static double |
LABInitialMeans.getMinDist(DBIDArrayIter j,
DistanceQuery<?> distQ,
DBIDArrayIter mi,
WritableDoubleDataStore mindist)
Get the minimum distance to previous medoids.
|
(package private) static double |
KMeansPlusPlusInitialMeans.initialWeights(WritableDoubleDataStore weights,
DBIDs ids,
DBIDRef latest,
DistanceQuery<?> distQ)
Initialize the weight list.
|
(package private) static double |
KMeansPlusPlusInitialMeans.initialWeights(WritableDoubleDataStore weights,
DBIDs ids,
NumberVector first,
DistanceQuery<? super NumberVector> distQ)
Initialize the weight list.
|
protected static <T> double |
OstrovskyInitialMeans.initialWeights(WritableDoubleDataStore weights,
Relation<? extends T> relation,
DBIDs ids,
T first,
T second,
DistanceQuery<? super T> distQ)
Initialize the weight list.
|
private static double |
KMeansPlusPlusInitialMeans.updateWeights(WritableDoubleDataStore weights,
DBIDs ids,
DBIDRef latest,
DistanceQuery<?> distQ)
Update the weight list.
|
private static double |
KMeansPlusPlusInitialMeans.updateWeights(WritableDoubleDataStore weights,
DBIDs ids,
NumberVector latest,
DistanceQuery<? super NumberVector> distQ)
Update the weight list.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) WritableDoubleDataStore |
OPTICSList.Instance.reachability
Reachability storage.
|
protected WritableDoubleDataStore |
GeneralizedOPTICS.Instance.reachability
Reachability storage.
|
(package private) WritableDoubleDataStore |
ClusterOrder.reachability
Reachability storage.
|
(package private) WritableDoubleDataStore |
FastOPTICS.reachDist
Result: reachability distances
|
| Constructor and Description |
|---|
ClusterOrder(java.lang.String name,
java.lang.String shortname,
ArrayModifiableDBIDs ids,
WritableDoubleDataStore reachability,
WritableDBIDDataStore predecessor)
Constructor
|
CorrelationClusterOrder(java.lang.String name,
java.lang.String shortname,
ArrayModifiableDBIDs ids,
WritableDoubleDataStore reachability,
WritableDBIDDataStore predecessor,
WritableIntegerDataStore corrdim)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private WritableDoubleDataStore |
DiSH.Instance.tmpDistance
Temporary storage of distances.
|
| Constructor and Description |
|---|
DiSHClusterOrder(java.lang.String name,
java.lang.String shortname,
ArrayModifiableDBIDs ids,
WritableDoubleDataStore reachability,
WritableDBIDDataStore predecessor,
WritableIntegerDataStore corrdim,
WritableDataStore<long[]> commonPreferenceVectors)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
DWOF.clusterData(DBIDs ids,
RangeQuery<O> rnnQuery,
WritableDoubleDataStore radii,
WritableDataStore<ModifiableDBIDs> labels)
This method applies a density based clustering algorithm.
|
private void |
DWOF.initializeRadii(DBIDs ids,
KNNQuery<O> knnq,
DistanceQuery<O> distFunc,
WritableDoubleDataStore radii)
This method prepares a container for the radii of the objects and
initializes radii according to the equation:
initialRadii of a certain object = (absoluteMinDist of all objects) *
(avgDist of the object) / (minAvgDist of all objects)
|
| Modifier and Type | Method and Description |
|---|---|
private void |
FastABOD.fastABOD(Database db,
Relation<V> relation,
DBIDs ids,
WritableDoubleDataStore abodvalues,
DoubleMinMax minmaxabod)
Full kernel-based version.
|
private boolean |
FastABOD.kNNABOD(Database db,
Relation<V> relation,
DBIDs ids,
WritableDoubleDataStore abodvalues,
DoubleMinMax minmaxabod)
Simpler kNN based, can use more indexing.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
CBLOF.computeCBLOFs(Relation<O> relation,
NumberVectorDistanceFunction<? super O> distance,
WritableDoubleDataStore cblofs,
DoubleMinMax cblofMinMax,
java.util.List<? extends Cluster<MeanModel>> largeClusters,
java.util.List<? extends Cluster<MeanModel>> smallClusters)
Compute the CBLOF scores for all the data.
|
private void |
CBLOF.storeCBLOFScore(WritableDoubleDataStore cblofs,
DoubleMinMax cblofMinMax,
double cblof,
DBIDIter iter) |
| Modifier and Type | Method and Description |
|---|---|
static void |
SOS.nominateNeighbors(DBIDIter ignore,
DBIDArrayIter di,
double[] p,
double norm,
WritableDoubleDataStore scores)
Vote for neighbors not being outliers.
|
protected void |
ReferenceBasedOutlierDetection.updateDensities(WritableDoubleDataStore rbod_score,
DoubleDBIDList referenceDists)
Update the density estimates for each object.
|
| Modifier and Type | Method and Description |
|---|---|
static void |
ISOS.nominateNeighbors(DBIDIter ignore,
DBIDArrayIter di,
double[] p,
double norm,
WritableDoubleDataStore scores)
Vote for neighbors not being outliers.
|
static DoubleMinMax |
ISOS.transformScores(WritableDoubleDataStore scores,
DBIDs ids,
double logPerp,
double phi)
Transform scores
|
| Modifier and Type | Field and Description |
|---|---|
private WritableDoubleDataStore |
FlexibleLOF.LOFResult.lofs
The LOF values of the objects.
|
private WritableDoubleDataStore |
FlexibleLOF.LOFResult.lrds
The LRD values of the objects.
|
| Modifier and Type | Method and Description |
|---|---|
WritableDoubleDataStore |
FlexibleLOF.LOFResult.getLofs()
Get the LOF data store.
|
WritableDoubleDataStore |
FlexibleLOF.LOFResult.getLrds()
Get the LRD data store.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
COF.computeAverageChainingDistances(KNNQuery<O> knnq,
DistanceQuery<O> dq,
DBIDs ids,
WritableDoubleDataStore acds)
Computes the average chaining distance, the average length of a path
through the given set of points to each target.
|
private void |
COF.computeCOFScores(KNNQuery<O> knnq,
DBIDs ids,
DoubleDataStore acds,
WritableDoubleDataStore cofs,
DoubleMinMax cofminmax)
Compute Connectivity outlier factors.
|
protected void |
INFLO.computeINFLO(Relation<O> relation,
ModifiableDBIDs pruned,
KNNQuery<O> knnq,
WritableDataStore<ModifiableDBIDs> rNNminuskNNs,
WritableDoubleDataStore inflos,
DoubleMinMax inflominmax)
Compute the final INFLO scores.
|
protected void |
FlexibleLOF.computeLOFs(KNNQuery<O> knnq,
DBIDs ids,
DoubleDataStore lrds,
WritableDoubleDataStore lofs,
DoubleMinMax lofminmax)
Computes the Local outlier factor (LOF) of the specified objects.
|
private void |
LOF.computeLOFScores(KNNQuery<O> knnq,
DBIDs ids,
DoubleDataStore lrds,
WritableDoubleDataStore lofs,
DoubleMinMax lofminmax)
Compute local outlier factors.
|
private void |
LOF.computeLRDs(KNNQuery<O> knnq,
DBIDs ids,
WritableDoubleDataStore lrds)
Compute local reachability distances.
|
protected void |
FlexibleLOF.computeLRDs(KNNQuery<O> knnq,
DBIDs ids,
WritableDoubleDataStore lrds)
Computes the local reachability density (LRD) of the specified objects.
|
protected void |
KDEOS.computeOutlierScores(KNNQuery<O> knnq,
DBIDs ids,
WritableDataStore<double[]> densities,
WritableDoubleDataStore kdeos,
DoubleMinMax minmax)
Compute the final KDEOS scores.
|
protected void |
LoOP.computePDists(Relation<O> relation,
KNNQuery<O> knn,
WritableDoubleDataStore pdists)
Compute the probabilistic distances used by LoOP.
|
protected double |
LoOP.computePLOFs(Relation<O> relation,
KNNQuery<O> knn,
WritableDoubleDataStore pdists,
WritableDoubleDataStore plofs)
Compute the LOF values, using the pdist distances.
|
private void |
SimplifiedLOF.computeSimplifiedLOFs(DBIDs ids,
KNNQuery<O> knnq,
WritableDoubleDataStore slrds,
WritableDoubleDataStore lofs,
DoubleMinMax lofminmax)
Compute the simplified LOF factors.
|
private void |
SimplifiedLOF.computeSimplifiedLRDs(DBIDs ids,
KNNQuery<O> knnq,
WritableDoubleDataStore lrds)
Compute the simplified reachability densities.
|
private void |
VarianceOfVolume.computeVolumes(KNNQuery<O> knnq,
int dim,
DBIDs ids,
WritableDoubleDataStore vols)
Compute volumes
|
private void |
VarianceOfVolume.computeVOVs(KNNQuery<O> knnq,
DBIDs ids,
DoubleDataStore vols,
WritableDoubleDataStore vovs,
DoubleMinMax vovminmax)
Compute variance of volumes.
|
| Constructor and Description |
|---|
LOFResult(OutlierResult result,
KNNQuery<O> kNNRefer,
KNNQuery<O> kNNReach,
WritableDoubleDataStore lrds,
WritableDoubleDataStore lofs)
Encapsulates information generated during a run of the
FlexibleLOF algorithm. |
| Modifier and Type | Method and Description |
|---|---|
static WritableDoubleDataStore |
DataStoreUtil.makeDoubleStorage(DBIDs ids,
int hints)
Make a new storage, to associate the given ids with an object of class
dataclass.
|
WritableDoubleDataStore |
DataStoreFactory.makeDoubleStorage(DBIDs ids,
int hints)
Make a new storage, to associate the given ids with an object of class
dataclass.
|
static WritableDoubleDataStore |
DataStoreUtil.makeDoubleStorage(DBIDs ids,
int hints,
double def)
Make a new storage, to associate the given ids with an object of class
dataclass.
|
WritableDoubleDataStore |
DataStoreFactory.makeDoubleStorage(DBIDs ids,
int hints,
double def)
Make a new storage, to associate the given ids with an object of class
dataclass.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ArrayDoubleStore
A class to answer representation queries using the stored Array.
|
class |
MapIntegerDBIDDoubleStore
Writable data store for double values.
|
| Modifier and Type | Method and Description |
|---|---|
WritableDoubleDataStore |
MemoryDataStoreFactory.makeDoubleStorage(DBIDs ids,
int hints) |
WritableDoubleDataStore |
MemoryDataStoreFactory.makeDoubleStorage(DBIDs ids,
int hints,
double def) |
| Modifier and Type | Field and Description |
|---|---|
(package private) WritableDoubleDataStore |
InMemoryInvertedIndex.length
Length storage.
|
| Modifier and Type | Method and Description |
|---|---|
private double |
InMemoryInvertedIndex.naiveQuery(V obj,
WritableDoubleDataStore scores,
HashSetModifiableDBIDs cands)
Query the most similar objects, abstract version.
|
private double |
InMemoryInvertedIndex.naiveQueryDense(NumberVector obj,
WritableDoubleDataStore scores,
HashSetModifiableDBIDs cands)
Query the most similar objects, dense version.
|
private double |
InMemoryInvertedIndex.naiveQuerySparse(SparseNumberVector obj,
WritableDoubleDataStore scores,
HashSetModifiableDBIDs cands)
Query the most similar objects, sparse version.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) WritableDoubleDataStore |
WriteDoubleDataStoreProcessor.store
Store to write to
|
| Constructor and Description |
|---|
WriteDoubleDataStoreProcessor(WritableDoubleDataStore store)
Constructor.
|
Copyright © 2019 ELKI Development Team. License information.