| Modifier and Type | Method and Description |
|---|---|
CorrelationAnalysisSolution<V> |
DependencyDerivator.generateModel(Relation<V> db,
DBIDs ids)
Runs the pca on the given set of IDs.
|
CorrelationAnalysisSolution<V> |
DependencyDerivator.generateModel(Relation<V> db,
DBIDs ids,
Vector centroid)
Runs the pca on the given set of IDs and for the given centroid.
|
CollectionResult<MaterializeDistances.DistanceEntry> |
MaterializeDistances.run(Database database,
Relation<O> relation)
Iterates over all points in the database.
|
KNNDistancesSampler.KNNDistanceOrderResult |
KNNDistancesSampler.run(Database database,
Relation<O> relation)
Provides an order of the kNN-distances for all objects within the specified
database.
|
Result |
DummyAlgorithm.run(Database database,
Relation<O> relation)
Run the algorithm.
|
CorrelationAnalysisSolution<V> |
DependencyDerivator.run(Database database,
Relation<V> relation)
Computes quantitatively linear dependencies among the attributes of the
given database based on a linear correlation PCA.
|
WritableDataStore<KNNList> |
KNNJoin.run(Relation<V> relation)
Joins in the given spatial database to each object its k-nearest neighbors.
|
| Modifier and Type | Method and Description |
|---|---|
Result |
ValidateApproximativeKNNIndex.run(Database database,
Relation<O> relation)
Run the algorithm.
|
Result |
RangeQueryBenchmarkAlgorithm.run(Database database,
Relation<O> relation)
Run the algorithm, with a separate query set.
|
Result |
KNNBenchmarkAlgorithm.run(Database database,
Relation<O> relation)
Run the algorithm.
|
Result |
RangeQueryBenchmarkAlgorithm.run(Database database,
Relation<O> relation,
Relation<NumberVector> radrel)
Run the algorithm, with separate radius relation
|
Result |
RangeQueryBenchmarkAlgorithm.run(Database database,
Relation<O> relation,
Relation<NumberVector> radrel)
Run the algorithm, with separate radius relation
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends ClassLabel> |
KNNClassifier.labelrep
Class label representation.
|
| Modifier and Type | Method and Description |
|---|---|
void |
PriorProbabilityClassifier.buildClassifier(Database database,
Relation<? extends ClassLabel> labelrep)
Learns the prior probability for all classes.
|
void |
KNNClassifier.buildClassifier(Database database,
Relation<? extends ClassLabel> labels) |
void |
Classifier.buildClassifier(Database database,
Relation<? extends ClassLabel> classLabels)
Performs the training.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
DBSCAN.expandCluster(Relation<O> relation,
RangeQuery<O> rangeQuery,
DBIDRef startObjectID,
FiniteProgress objprog,
IndefiniteProgress clusprog)
DBSCAN-function expandCluster.
|
Clustering<Model> |
SNNClustering.run(Database database,
Relation<O> relation)
Perform SNN clustering
|
Clustering<PrototypeModel<O>> |
CanopyPreClustering.run(Database database,
Relation<O> relation)
Run the algorithm
|
Clustering<MeanModel> |
NaiveMeanShiftClustering.run(Database database,
Relation<V> relation)
Run the mean-shift clustering algorithm.
|
Clustering<Model> |
DBSCAN.run(Relation<O> relation)
Performs the DBSCAN algorithm on the given database.
|
protected void |
DBSCAN.runDBSCAN(Relation<O> relation,
RangeQuery<O> rangeQuery)
Run the DBSCAN algorithm
|
| 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.
|
Clustering<MedoidModel> |
AffinityPropagationClusteringAlgorithm.run(Database db,
Relation<O> relation)
Perform affinity propagation clustering.
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<V> |
AbstractBiclustering.relation
Relation we use.
|
| Modifier and Type | Method and Description |
|---|---|
Relation<V> |
AbstractBiclustering.getRelation()
Getter for the relation.
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<M> |
AbstractBiclustering.run(Relation<V> relation)
Prepares the algorithm for running on a specific database.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<ParameterizationFunction> |
CASH.fulldatabase
The entire relation.
|
private Relation<V> |
HiCO.Instance.relation
Data relation.
|
| Modifier and Type | Method and Description |
|---|---|
private Relation<ParameterizationFunction> |
CASH.preprocess(Database db,
Relation<V> vrel)
Preprocess the dataset, precomputing the parameterization functions.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
ORCLUS.assign(Relation<V> database,
DistanceQuery<V> distFunc,
List<ORCLUS.ORCLUSCluster> clusters)
Creates a partitioning of the database by assigning each object to its
closest seed.
|
private MaterializedRelation<ParameterizationFunction> |
CASH.buildDB(int dim,
Matrix basis,
DBIDs ids,
Relation<ParameterizationFunction> relation)
Builds a dim-1 dimensional database where the objects are projected into
the specified subspace.
|
private Database |
CASH.buildDerivatorDB(Relation<ParameterizationFunction> relation,
CASHInterval interval)
Builds a database for the derivator consisting of the ids in the specified
interval.
|
private Database |
CASH.buildDerivatorDB(Relation<ParameterizationFunction> relation,
DBIDs ids)
Builds a database for the derivator consisting of the ids in the specified
interval.
|
private double[] |
CASH.determineMinMaxDistance(Relation<ParameterizationFunction> relation,
int dimensionality)
Determines the minimum and maximum function value of all parameterization
functions stored in the specified database.
|
private static int |
CASH.dimensionality(Relation<ParameterizationFunction> relation)
Get the dimensionality of a vector field.
|
private Clustering<Model> |
CASH.doRun(Relation<ParameterizationFunction> relation,
FiniteProgress progress)
Runs the CASH algorithm on the specified database, this method is
recursively called until only noise is left.
|
private List<List<Cluster<CorrelationModel<V>>>> |
ERiC.extractCorrelationClusters(Clustering<Model> dbscanResult,
Relation<V> database,
int dimensionality,
ERiCNeighborPredicate.Instance npred)
Extracts the correlation clusters and noise from the copac result and
returns a mapping of correlation dimension to maps of clusters within this
correlation dimension.
|
private Matrix |
ORCLUS.findBasis(Relation<V> database,
DistanceQuery<V> distFunc,
ORCLUS.ORCLUSCluster cluster,
int dim)
Finds the basis of the subspace of dimensionality
dim for the
specified cluster. |
private LMCLUS.Separation |
LMCLUS.findSeparation(Relation<NumberVector> relation,
DBIDs currentids,
int dimension,
Random r)
This method samples a number of linear manifolds an tries to determine
which the one with the best cluster is.
|
private void |
CASH.initHeap(ObjectHeap<IntegerPriorityObject<CASHInterval>> heap,
Relation<ParameterizationFunction> relation,
int dim,
DBIDs ids)
Initializes the heap with the root intervals.
|
private List<ORCLUS.ORCLUSCluster> |
ORCLUS.initialSeeds(Relation<V> database,
int k)
Initializes the list of seeds wit a random sample of size k.
|
private void |
ORCLUS.merge(Relation<V> database,
DistanceQuery<V> distFunc,
List<ORCLUS.ORCLUSCluster> clusters,
int k_new,
int d_new,
IndefiniteProgress cprogress)
Reduces the number of seeds to k_new
|
private Relation<ParameterizationFunction> |
CASH.preprocess(Database db,
Relation<V> vrel)
Preprocess the dataset, precomputing the parameterization functions.
|
private ORCLUS.ProjectedEnergy |
ORCLUS.projectedEnergy(Relation<V> database,
DistanceQuery<V> distFunc,
ORCLUS.ORCLUSCluster c_i,
ORCLUS.ORCLUSCluster c_j,
int i,
int j,
int dim)
Computes the projected energy of the specified clusters.
|
Clustering<Model> |
LMCLUS.run(Database database,
Relation<NumberVector> relation)
The main LMCLUS (Linear manifold clustering algorithm) is processed in this
method.
|
Clustering<Model> |
ORCLUS.run(Database database,
Relation<V> relation)
Performs the ORCLUS algorithm on the given database.
|
CorrelationClusterOrder |
HiCO.run(Database db,
Relation<V> relation) |
Clustering<CorrelationModel<V>> |
ERiC.run(Database database,
Relation<V> relation)
Performs the ERiC algorithm on the given database.
|
Clustering<DimensionModel> |
COPAC.run(Database database,
Relation<V> relation)
Run the COPAC algorithm.
|
Clustering<Model> |
CASH.run(Database database,
Relation<V> vrel)
Run CASH on the relation.
|
private Matrix |
CASH.runDerivator(Relation<ParameterizationFunction> relation,
int dim,
CASHInterval interval,
ModifiableDBIDs ids)
Runs the derivator on the specified interval and assigns all points having
a distance less then the standard deviation of the derivator model to the
model to this model.
|
private LinearEquationSystem |
CASH.runDerivator(Relation<ParameterizationFunction> relation,
int dimensionality,
DBIDs ids)
Runs the derivator on the specified interval and assigns all points having
a distance less then the standard deviation of the derivator model to the
model to this model.
|
private ORCLUS.ORCLUSCluster |
ORCLUS.union(Relation<V> relation,
DistanceQuery<V> distFunc,
ORCLUS.ORCLUSCluster c1,
ORCLUS.ORCLUSCluster c2,
int dim)
Returns the union of the two specified clusters.
|
| Constructor and Description |
|---|
HiCO.Instance(Database db,
Relation<V> relation)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<ParameterizationFunction> |
CASHIntervalSplit.database
The database storing the parameterization functions.
|
| Constructor and Description |
|---|
CASHIntervalSplit(Relation<ParameterizationFunction> database,
int minPts)
Initializes the logger and sets the debug status to the given value.
|
| Modifier and Type | Method and Description |
|---|---|
static double |
EM.assignProbabilitiesToInstances(Relation<? extends NumberVector> relation,
List<? extends EMClusterModel<?>> models,
WritableDataStore<double[]> probClusterIGivenX)
Assigns the current probability values to the instances in the database and
compute the expectation value of the current mixture of distributions.
|
List<SphericalGaussianModel> |
SphericalGaussianModelFactory.buildInitialModels(Database database,
Relation<V> relation,
int k,
NumberVectorDistanceFunction<? super V> df) |
List<MultivariateGaussianModel> |
MultivariateGaussianModelFactory.buildInitialModels(Database database,
Relation<V> relation,
int k,
NumberVectorDistanceFunction<? super V> df) |
List<? extends EMClusterModel<M>> |
EMClusterModelFactory.buildInitialModels(Database database,
Relation<V> relation,
int k,
NumberVectorDistanceFunction<? super V> df)
Build the initial models
|
List<DiagonalGaussianModel> |
DiagonalGaussianModelFactory.buildInitialModels(Database database,
Relation<V> relation,
int k,
NumberVectorDistanceFunction<? super V> df) |
static void |
EM.recomputeCovarianceMatrices(Relation<? extends NumberVector> relation,
WritableDataStore<double[]> probClusterIGivenX,
List<? extends EMClusterModel<?>> models)
Recompute the covariance matrixes.
|
Clustering<M> |
EM.run(Database database,
Relation<V> relation)
Performs the EM clustering algorithm on the given database.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<? extends NumberVector> |
ERiCNeighborPredicate.Instance.relation
Vector data relation.
|
| 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 |
PreDeConNeighborPredicate.computeLocalModel(DBIDRef id,
DoubleDBIDList neighbors,
Relation<V> relation) |
protected PreDeConNeighborPredicate.PreDeConModel |
FourCNeighborPredicate.computeLocalModel(DBIDRef id,
DoubleDBIDList neighbors,
Relation<V> relation) |
protected COPACNeighborPredicate.COPACModel |
COPACNeighborPredicate.computeLocalModel(DBIDRef id,
DoubleDBIDList knnneighbors,
Relation<V> relation)
COPAC model computation
|
ERiCNeighborPredicate.Instance |
ERiCNeighborPredicate.instantiate(Database database,
Relation<V> relation)
Full instantiation interface.
|
COPACNeighborPredicate.Instance |
COPACNeighborPredicate.instantiate(Database database,
Relation<V> relation)
Full instantiation method.
|
DataStore<M> |
AbstractRangeQueryNeighborPredicate.preprocess(Class<? super M> modelcls,
Relation<O> relation,
RangeQuery<O> query)
Perform the preprocessing step.
|
Clustering<Model> |
LSDBC.run(Database database,
Relation<O> relation)
Run the LSDBC algorithm
|
| Constructor and Description |
|---|
ERiCNeighborPredicate.Instance(DBIDs ids,
DataStore<PCAFilteredResult> storage,
Relation<? extends NumberVector> relation)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
PointerDensityHierarchyRepresentationResult |
SLINKHDBSCANLinearMemory.run(Database db,
Relation<O> relation)
Run the algorithm
|
PointerHierarchyRepresentationResult |
SLINK.run(Database database,
Relation<O> relation)
Performs the SLINK algorithm on the given database.
|
PointerDensityHierarchyRepresentationResult |
HDBSCANLinearMemory.run(Database db,
Relation<O> relation)
Run the algorithm
|
PointerHierarchyRepresentationResult |
AnderbergHierarchicalClustering.run(Database db,
Relation<O> relation)
Run the algorithm
|
PointerHierarchyRepresentationResult |
AGNES.run(Database db,
Relation<O> relation)
Run the algorithm
|
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.
|
| Modifier and Type | Method and Description |
|---|---|
protected boolean |
AbstractKMeans.assignToNearestCluster(Relation<? extends V> relation,
List<? extends NumberVector> means,
List<? extends ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
double[] varsum)
Returns a list of clusters.
|
protected boolean |
KMeansBatchedLloyd.assignToNearestCluster(Relation<V> relation,
DBIDs ids,
List<? extends NumberVector> oldmeans,
double[][] meanshift,
int[] changesize,
List<? extends ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
double[] varsum)
Returns a list of clusters.
|
private int |
KMeansElkan.assignToNearestCluster(Relation<V> relation,
List<Vector> means,
List<Vector> sums,
List<ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
double[] sep,
double[][] cdist,
WritableDoubleDataStore upper,
WritableDataStore<double[]> lower)
Reassign objects, but only if their bounds indicate it is necessary to do
so.
|
private int |
KMeansHamerly.assignToNearestCluster(Relation<V> relation,
List<Vector> means,
List<Vector> sums,
List<ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
double[] sep,
WritableDoubleDataStore upper,
WritableDoubleDataStore lower)
Reassign objects, but only if their bounds indicate it is necessary to do
so.
|
private int |
KMeansElkan.initialAssignToNearestCluster(Relation<V> relation,
List<Vector> means,
List<Vector> sums,
List<ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
WritableDoubleDataStore upper,
WritableDataStore<double[]> lower)
Reassign objects, but only if their bounds indicate it is necessary to do
so.
|
private int |
KMeansHamerly.initialAssignToNearestCluster(Relation<V> relation,
List<Vector> means,
List<Vector> sums,
List<ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
WritableDoubleDataStore upper,
WritableDoubleDataStore lower)
Reassign objects, but only if their bounds indicate it is necessary to do
so.
|
protected boolean |
AbstractKMeans.macQueenIterate(Relation<V> relation,
List<Vector> means,
List<ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
double[] varsum)
Perform a MacQueen style iteration.
|
protected List<Vector> |
AbstractKMeans.means(List<? extends DBIDs> clusters,
List<? extends NumberVector> means,
Relation<V> database)
Returns the mean vectors of the given clusters in the given database.
|
protected List<Vector> |
AbstractKMeans.medians(List<? extends DBIDs> clusters,
List<Vector> medians,
Relation<V> database)
Returns the median vectors of the given clusters in the given database.
|
Clustering<M> |
XMeans.run(Database database,
Relation<V> relation)
Run the algorithm on a database and relation.
|
Clustering<KMeansModel> |
SingleAssignmentKMeans.run(Database database,
Relation<V> relation) |
Clustering<MedoidModel> |
KMedoidsPAM.run(Database database,
Relation<V> relation)
Run k-medoids
|
Clustering<MedoidModel> |
KMedoidsEM.run(Database database,
Relation<V> relation)
Run k-medoids
|
Clustering<MeanModel> |
KMediansLloyd.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansMacQueen.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansLloyd.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansHybridLloydMacQueen.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansHamerly.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansElkan.run(Database database,
Relation<V> relation) |
Clustering<M> |
KMeansBisecting.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansBatchedLloyd.run(Database database,
Relation<V> relation) |
Clustering<M> |
KMeans.run(Database database,
Relation<V> rel)
Run the clustering algorithm.
|
Clustering<MedoidModel> |
CLARA.run(Database database,
Relation<V> relation) |
Clustering<M> |
BestOfMultipleKMeans.run(Database database,
Relation<V> relation) |
protected List<? extends NumberVector> |
XMeans.splitCentroid(Cluster<? extends MeanModel> parentCluster,
Relation<V> relation)
Split an existing centroid into two initial centers.
|
protected List<Cluster<M>> |
XMeans.splitCluster(Cluster<M> parentCluster,
Database database,
Relation<V> relation)
Conditionally splits the clusters based on the information criterion.
|
private void |
KMeansElkan.updateBounds(Relation<V> relation,
WritableIntegerDataStore assignment,
WritableDoubleDataStore upper,
WritableDataStore<double[]> lower,
double[] move)
Update the bounds for k-means.
|
private void |
KMeansHamerly.updateBounds(Relation<V> relation,
WritableIntegerDataStore assignment,
WritableDoubleDataStore upper,
WritableDoubleDataStore lower,
double[] move,
double delta)
Update the bounds for k-means.
|
| Modifier and Type | Method and Description |
|---|---|
<T extends NumberVector,O extends NumberVector> |
PredefinedInitialMeans.chooseInitialMeans(Database database,
Relation<T> relation,
int k,
NumberVectorDistanceFunction<? super T> distanceFunction,
NumberVector.Factory<O> factory) |
<T extends V,O extends NumberVector> |
SampleKMeansInitialization.chooseInitialMeans(Database database,
Relation<T> relation,
int k,
NumberVectorDistanceFunction<? super T> distanceFunction,
NumberVector.Factory<O> factory) |
<T extends V,O extends NumberVector> |
KMeansInitialization.chooseInitialMeans(Database database,
Relation<T> relation,
int k,
NumberVectorDistanceFunction<? super T> distanceFunction,
NumberVector.Factory<O> factory)
Choose initial means
|
<T extends NumberVector,V extends NumberVector> |
RandomlyGeneratedInitialMeans.chooseInitialMeans(Database database,
Relation<T> relation,
int k,
NumberVectorDistanceFunction<? super T> distanceFunction,
NumberVector.Factory<V> factory) |
<T extends NumberVector,V extends NumberVector> |
RandomlyChosenInitialMeans.chooseInitialMeans(Database database,
Relation<T> relation,
int k,
NumberVectorDistanceFunction<? super T> distanceFunction,
NumberVector.Factory<V> factory) |
<T extends NumberVector,V extends NumberVector> |
PAMInitialMeans.chooseInitialMeans(Database database,
Relation<T> relation,
int k,
NumberVectorDistanceFunction<? super T> distanceFunction,
NumberVector.Factory<V> factory) |
<T extends NumberVector,V extends NumberVector> |
KMeansPlusPlusInitialMeans.chooseInitialMeans(Database database,
Relation<T> relation,
int k,
NumberVectorDistanceFunction<? super T> distanceFunction,
NumberVector.Factory<V> factory) |
<T extends NumberVector,V extends NumberVector> |
FirstKInitialMeans.chooseInitialMeans(Database database,
Relation<T> relation,
int k,
NumberVectorDistanceFunction<? super T> distanceFunction,
NumberVector.Factory<V> factory) |
<T extends NumberVector,V extends NumberVector> |
FarthestSumPointsInitialMeans.chooseInitialMeans(Database database,
Relation<T> relation,
int k,
NumberVectorDistanceFunction<? super T> distanceFunction,
NumberVector.Factory<V> factory) |
<T extends NumberVector,V extends NumberVector> |
FarthestPointsInitialMeans.chooseInitialMeans(Database database,
Relation<T> relation,
int k,
NumberVectorDistanceFunction<? super T> distanceFunction,
NumberVector.Factory<V> factory) |
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<V> |
KMeansProcessor.relation
Data relation.
|
private Relation<V> |
KMeansProcessor.Instance.relation
Data relation.
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<KMeansModel> |
ParallelLloydKMeans.run(Database database,
Relation<V> relation) |
| Constructor and Description |
|---|
KMeansProcessor.Instance(Relation<V> relation,
NumberVectorDistanceFunction<? super V> distance,
WritableIntegerDataStore assignment,
List<? extends NumberVector> means)
Constructor.
|
KMeansProcessor(Relation<V> relation,
NumberVectorDistanceFunction<? super V> distance,
WritableIntegerDataStore assignment,
double[] varsum)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
static <V extends NumberVector> |
AbstractKMeansQualityMeasure.logLikelihood(Relation<V> relation,
Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction)
Computes log likelihood of an entire clustering.
|
static <V extends NumberVector> |
AbstractKMeansQualityMeasure.logLikelihoodAlternate(Relation<V> relation,
Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction)
Computes log likelihood of an entire clustering.
|
static int |
AbstractKMeansQualityMeasure.numberOfFreeParameters(Relation<? extends NumberVector> relation,
Clustering<? extends MeanModel> clustering)
Compute the number of free parameters.
|
<V extends NumberVector> |
WithinClusterVarianceQualityMeasure.quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation) |
<V extends NumberVector> |
WithinClusterMeanDistanceQualityMeasure.quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation) |
<V extends NumberVector> |
BayesianInformationCriterionZhao.quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation) |
<V extends NumberVector> |
BayesianInformationCriterion.quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation) |
<V extends NumberVector> |
AkaikeInformationCriterion.quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation) |
<V extends O> |
KMeansQualityMeasure.quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation)
Calculates and returns the quality measure.
|
static <V extends NumberVector> |
AbstractKMeansQualityMeasure.varianceOfCluster(Cluster<? extends MeanModel> cluster,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation)
Variance contribution of a single cluster.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
ExternalClustering.attachToRelation(Database database,
Relation<?> r,
TIntArrayList assignment,
ArrayList<String> name)
Build a clustering from the file result.
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<ClusterModel> |
KNNKernelDensityMinimaClustering.run(Relation<V> relation)
Run the clustering algorithm on a data relation.
|
| Modifier and Type | Method and Description |
|---|---|
private Clustering<OPTICSModel> |
OPTICSXi.extractClusters(ClusterOrder clusterOrderResult,
Relation<?> relation,
double ixi,
int minpts)
Extract clusters from a cluster order result.
|
Clustering<OPTICSModel> |
OPTICSXi.run(Database database,
Relation<?> relation) |
ClusterOrder |
DeLiClu.run(Database database,
Relation<NV> relation) |
ClusterOrder |
OPTICSList.run(Database db,
Relation<O> relation) |
ClusterOrder |
OPTICSHeap.run(Database db,
Relation<O> relation) |
abstract ClusterOrder |
GeneralizedOPTICS.run(Database db,
Relation<O> relation)
Run OPTICS on the database.
|
abstract ClusterOrder |
AbstractOPTICS.run(Database db,
Relation<O> relation)
Run OPTICS on the database.
|
ClusterOrder |
FastOPTICS.run(Database db,
Relation<V> rel)
Run the algorithm.
|
| Constructor and Description |
|---|
GeneralizedOPTICS.Instance(Database db,
Relation<O> relation)
Constructor for a single data set.
|
OPTICSHeap.Instance(Database db,
Relation<O> relation)
Constructor for a single data set.
|
OPTICSList.Instance(Database db,
Relation<O> relation)
Constructor for a single data set.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<V> |
HiSC.Instance.relation
Data relation.
|
private Relation<V> |
DiSH.Instance.relation
Data relation.
|
| 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 void |
P3C.assignUnassigned(Relation<V> relation,
WritableDataStore<double[]> probClusterIGivenX,
List<MultivariateGaussianModel> models,
ModifiableDBIDs unassigned)
Assign unassigned objects to best candidate based on shortest Mahalanobis
distance.
|
private double |
PROCLUS.avgDistance(Vector centroid,
DBIDs objectIDs,
Relation<V> database,
int dimension)
Computes the average distance of the objects to the centroid along the
specified dimension.
|
private void |
DiSH.buildHierarchy(Relation<V> database,
Clustering<SubspaceModel> clustering,
List<Cluster<SubspaceModel>> clusters,
int dimensionality)
Builds the cluster hierarchy.
|
private void |
DiSH.checkClusters(Relation<V> relation,
TCustomHashMap<long[],List<ArrayModifiableDBIDs>> clustersMap)
Removes the clusters with size < minpts from the cluster map and adds them
to their parents.
|
private Clustering<SubspaceModel> |
DiSH.computeClusters(Relation<V> database,
DiSH.DiSHClusterOrder clusterOrder)
Computes the hierarchical clusters according to the cluster order.
|
private void |
P3C.computeFuzzyMembership(Relation<V> relation,
ArrayList<P3C.Signature> clusterCores,
ModifiableDBIDs unassigned,
WritableDataStore<double[]> probClusterIGivenX,
List<MultivariateGaussianModel> models,
int dim)
Computes a fuzzy membership with the weights based on which cluster cores
each data point is part of.
|
private boolean |
DOC.dimensionIsRelevant(int dimension,
Relation<V> relation,
DBIDs points)
Utility method to test if a given dimension is relevant as determined via a
set of reference points (i.e. if the variance along the attribute is lower
than the threshold).
|
private double |
PROCLUS.evaluateClusters(ArrayList<PROCLUS.PROCLUSCluster> clusters,
long[][] dimensions,
Relation<V> database)
Evaluates the quality of the clusters.
|
private TCustomHashMap<long[],List<ArrayModifiableDBIDs>> |
DiSH.extractClusters(Relation<V> relation,
DiSH.DiSHClusterOrder clusterOrder)
Extracts the clusters from the cluster order.
|
private List<PROCLUS.PROCLUSCluster> |
PROCLUS.finalAssignment(List<Pair<Vector,long[]>> dimensions,
Relation<V> database)
Refinement step to assign the objects to the final clusters.
|
private List<CLIQUESubspace<V>> |
CLIQUE.findDenseSubspaceCandidates(Relation<V> database,
List<CLIQUESubspace<V>> denseSubspaces)
Determines the
k-dimensional dense subspace candidates from the
specified (k-1)-dimensional dense subspaces. |
private List<CLIQUESubspace<V>> |
CLIQUE.findDenseSubspaces(Relation<V> database,
List<CLIQUESubspace<V>> denseSubspaces)
Determines the
k-dimensional dense subspaces and performs a pruning
if this option is chosen. |
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.
|
private List<Pair<Vector,long[]>> |
PROCLUS.findDimensions(ArrayList<PROCLUS.PROCLUSCluster> clusters,
Relation<V> database)
Refinement step that determines the set of correlated dimensions for each
cluster centroid.
|
private List<CLIQUESubspace<V>> |
CLIQUE.findOneDimensionalDenseSubspaceCandidates(Relation<V> database)
Determines the one-dimensional dense subspace candidates by making a pass
over the database.
|
private List<CLIQUESubspace<V>> |
CLIQUE.findOneDimensionalDenseSubspaces(Relation<V> database)
Determines the one dimensional dense subspaces and performs a pruning if
this option is chosen.
|
private void |
P3C.findOutliers(Relation<V> relation,
List<MultivariateGaussianModel> models,
ArrayList<P3C.ClusterCandidate> clusterCandidates,
ModifiableDBIDs noise)
Performs outlier detection by testing the Mahalanobis distance of each
point in a cluster against the critical value of the ChiSquared
distribution with as many degrees of freedom as the cluster has relevant
attributes.
|
private Pair<long[],ArrayModifiableDBIDs> |
DiSH.findParent(Relation<V> relation,
Pair<long[],ArrayModifiableDBIDs> child,
TCustomHashMap<long[],List<ArrayModifiableDBIDs>> clustersMap)
Returns the parent of the specified cluster
|
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.
|
private Collection<CLIQUEUnit<V>> |
CLIQUE.initOneDimensionalUnits(Relation<V> database)
Initializes and returns the one dimensional units.
|
private boolean |
DiSH.isParent(Relation<V> relation,
Cluster<SubspaceModel> parent,
Hierarchy.Iter<Cluster<SubspaceModel>> iter,
int db_dim)
Returns true, if the specified parent cluster is a parent of one child of
the children clusters.
|
private Cluster<SubspaceModel> |
DOC.makeCluster(Relation<V> relation,
DBIDs C,
long[] D)
Utility method to create a subspace cluster from a list of DBIDs and the
relevant attributes.
|
private SetDBIDs[][] |
P3C.partitionData(Relation<V> relation,
int bins)
Partition the data set into
bins bins in each dimension
independently. |
Clustering<SubspaceModel> |
PROCLUS.run(Database database,
Relation<V> relation)
Performs the PROCLUS algorithm on the given database.
|
Clustering<SubspaceModel> |
P3C.run(Database database,
Relation<V> relation)
Performs the P3C algorithm on the given Database.
|
ClusterOrder |
HiSC.run(Database db,
Relation<V> relation) |
Clustering<SubspaceModel> |
DiSH.run(Database db,
Relation<V> relation)
Performs the DiSH algorithm on the given database.
|
Clustering<SubspaceModel> |
DOC.run(Database database,
Relation<V> relation)
Performs the DOC or FastDOC (as configured) algorithm on the given
Database.
|
Clustering<SubspaceModel> |
SUBCLU.run(Relation<V> relation)
Performs the SUBCLU algorithm on the given database.
|
Clustering<SubspaceModel> |
CLIQUE.run(Relation<V> relation)
Performs the CLIQUE algorithm on the given database.
|
private List<Cluster<Model>> |
SUBCLU.runDBSCAN(Relation<V> relation,
DBIDs ids,
Subspace subspace)
Runs the DBSCAN algorithm on the specified partition of the database in the
given subspace.
|
private Cluster<SubspaceModel> |
DOC.runDOC(Database database,
Relation<V> relation,
ArrayModifiableDBIDs S,
int d,
int n,
int m,
int r,
int minClusterSize)
Performs a single run of DOC, finding a single cluster.
|
private Cluster<SubspaceModel> |
DOC.runFastDOC(Database database,
Relation<V> relation,
ArrayModifiableDBIDs S,
int d,
int n,
int m,
int r)
Performs a single run of FastDOC, finding a single cluster.
|
private List<Cluster<SubspaceModel>> |
DiSH.sortClusters(Relation<V> relation,
TCustomHashMap<long[],List<ArrayModifiableDBIDs>> clustersMap)
Returns a sorted list of the clusters w.r.t. the subspace dimensionality in
descending order.
|
| Constructor and Description |
|---|
DiSH.Instance(Database db,
Relation<V> relation)
Constructor.
|
HiSC.Instance(Database db,
Relation<V> relation)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
private HashMap<String,DBIDs> |
ByLabelClustering.multipleAssignment(Relation<?> data)
Assigns the objects of the database to multiple clusters according to their
labels.
|
Clustering<Model> |
TrivialAllNoise.run(Relation<?> relation) |
Clustering<Model> |
TrivialAllInOne.run(Relation<?> relation) |
Clustering<Model> |
ByLabelHierarchicalClustering.run(Relation<?> relation)
Run the actual clustering algorithm.
|
Clustering<Model> |
ByLabelClustering.run(Relation<?> relation)
Run the actual clustering algorithm.
|
Clustering<Model> |
ByModelClustering.run(Relation<Model> relation)
Run the actual clustering algorithm.
|
private HashMap<String,DBIDs> |
ByLabelClustering.singleAssignment(Relation<?> data)
Assigns the objects of the database to single clusters according to their
labels.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<? extends UncertainObject> |
FDBSCANNeighborPredicate.Instance.relation
The relation holding the uncertain objects.
|
| Modifier and Type | Method and Description |
|---|---|
protected boolean |
UKMeans.assignToNearestCluster(Relation<DiscreteUncertainObject> relation,
List<Vector> means,
List<? extends ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
double[] varsum)
Returns a list of clusters.
|
protected List<Vector> |
UKMeans.means(List<? extends ModifiableDBIDs> clusters,
List<? extends NumberVector> means,
Relation<DiscreteUncertainObject> database)
Returns the mean vectors of the given clusters in the given database.
|
Clustering<?> |
RepresentativeUncertainClustering.run(Database database,
Relation<? extends UncertainObject> relation)
This run method will do the wrapping.
|
C |
CenterOfMassMetaClustering.run(Database database,
Relation<? extends UncertainObject> relation)
This run method will do the wrapping.
|
Clustering<?> |
UKMeans.run(Database database,
Relation<DiscreteUncertainObject> relation)
Run the clustering.
|
| Constructor and Description |
|---|
FDBSCANNeighborPredicate.Instance(double epsilon,
int sampleSize,
double threshold,
Relation<? extends UncertainObject> relation,
RandomFactory rand)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
private FPGrowth.FPTree |
FPGrowth.buildFPTree(Relation<BitVector> relation,
int[] iidx,
int items)
Build the actual FP-tree structure.
|
protected List<OneItemset> |
APRIORI.buildFrequentOneItemsets(Relation<? extends SparseFeatureVector<?>> relation,
int dim,
int needed)
Build the 1-itemsets.
|
protected List<SparseItemset> |
APRIORI.buildFrequentTwoItemsets(List<OneItemset> oneitems,
Relation<BitVector> relation,
int dim,
int needed,
DBIDs ids,
ArrayModifiableDBIDs survivors)
Build the 2-itemsets.
|
private DBIDs[] |
Eclat.buildIndex(Relation<BitVector> relation,
int dim,
int minsupp) |
private int[] |
FPGrowth.countItemSupport(Relation<BitVector> relation,
int dim)
Count the support of each 1-item.
|
protected List<? extends Itemset> |
APRIORI.frequentItemsets(List<? extends Itemset> candidates,
Relation<BitVector> relation,
int needed,
DBIDs ids,
ArrayModifiableDBIDs survivors,
int length)
Returns the frequent BitSets out of the given BitSets with respect to the
given database.
|
protected List<SparseItemset> |
APRIORI.frequentItemsetsSparse(List<SparseItemset> candidates,
Relation<BitVector> relation,
int needed,
DBIDs ids,
ArrayModifiableDBIDs survivors,
int length)
Returns the frequent BitSets out of the given BitSets with respect to the
given database.
|
FrequentItemsetsResult |
FPGrowth.run(Database db,
Relation<BitVector> relation)
Run the FP-Growth algorithm
|
FrequentItemsetsResult |
Eclat.run(Database db,
Relation<BitVector> relation)
Run the Eclat algorithm
|
FrequentItemsetsResult |
APRIORI.run(Relation<BitVector> relation)
Performs the APRIORI algorithm on the given database.
|
| Modifier and Type | Method and Description |
|---|---|
private double |
GaussianUniformMixture.loglikelihoodNormal(DBIDs objids,
Relation<V> database)
Computes the loglikelihood of all normal objects.
|
OutlierResult |
OPTICSOF.run(Database database,
Relation<O> relation)
Perform OPTICS-based outlier detection.
|
OutlierResult |
DWOF.run(Database database,
Relation<O> relation)
Performs the Generalized DWOF_SCORE algorithm on the given database by
calling all the other methods in the proper order.
|
OutlierResult |
SimpleCOP.run(Database database,
Relation<V> data) |
OutlierResult |
GaussianUniformMixture.run(Relation<V> relation)
Run the algorithm
|
OutlierResult |
GaussianModel.run(Relation<V> relation)
Run the algorithm
|
OutlierResult |
COP.run(Relation<V> relation)
Process a single relation.
|
| Modifier and Type | Method and Description |
|---|---|
OutlierResult |
LBABOD.run(Database db,
Relation<V> relation)
Run LB-ABOD on the data set.
|
OutlierResult |
FastABOD.run(Database db,
Relation<V> relation)
Run Fast-ABOD on the data set.
|
OutlierResult |
ABOD.run(Database db,
Relation<V> relation)
Run ABOD on the data set.
|
| Modifier and Type | Method and Description |
|---|---|
OutlierResult |
KMeansOutlierDetection.run(Database database,
Relation<O> relation)
Run the outlier detection algorithm.
|
OutlierResult |
EMOutlier.run(Database database,
Relation<V> relation)
Runs the algorithm in the timed evaluation part.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<O> |
HilOut.HilbertFeatures.relation
Relation indexed
|
| 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.
|
protected DoubleDataStore |
DBOutlierScore.computeOutlierScores(Database database,
Relation<O> relation,
double d) |
protected DoubleDataStore |
DBOutlierDetection.computeOutlierScores(Database database,
Relation<O> relation,
double neighborhoodSize) |
protected abstract DoubleDataStore |
AbstractDBOutlier.computeOutlierScores(Database database,
Relation<O> relation,
double d)
computes an outlier score for each object of the database.
|
OutlierResult |
ReferenceBasedOutlierDetection.run(Database database,
Relation<? extends NumberVector> relation)
Run the algorithm on the given relation.
|
OutlierResult |
ODIN.run(Database database,
Relation<O> relation)
Run the ODIN algorithm
|
OutlierResult |
LocalIsolationCoefficient.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
KNNWeightOutlier.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
KNNOutlier.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
HilOut.run(Database database,
Relation<O> relation) |
OutlierResult |
AbstractDBOutlier.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
KNNOutlier.run(Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
| Constructor and Description |
|---|
HilOut.HilbertFeatures(Relation<O> relation,
double[] min,
double diameter)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
OutlierResult |
ParallelKNNWeightOutlier.run(Database database,
Relation<O> relation)
Run the parallel kNN weight outlier detector.
|
OutlierResult |
ParallelKNNOutlier.run(Database database,
Relation<O> relation) |
| Modifier and Type | Method and Description |
|---|---|
OutlierResult |
IntrinsicDimensionalityOutlier.run(Database database,
Relation<O> relation)
Run the algorithm
|
OutlierResult |
IDOS.run(Database database,
Relation<O> relation)
Run the algorithm
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<? extends NumberVector> |
ALOCI.ALOCIQuadTree.relation
Relation indexed.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
INFLO.computeINFLO(Relation<O> relation,
ModifiableDBIDs pruned,
WritableDataStore<ModifiableDBIDs> knns,
WritableDataStore<ModifiableDBIDs> rnns,
WritableDoubleDataStore density,
WritableDoubleDataStore inflos,
DoubleMinMax inflominmax)
Compute the final INFLO scores.
|
protected void |
INFLO.computeNeighborhoods(Relation<O> relation,
KNNQuery<O> knnQuery,
ModifiableDBIDs pruned,
WritableDataStore<ModifiableDBIDs> knns,
WritableDataStore<ModifiableDBIDs> rnns,
WritableDoubleDataStore density)
Compute neighborhoods
|
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 int |
KDEOS.dimensionality(Relation<O> rel)
Ugly hack to allow using this implementation without having a well-defined
dimensionality.
|
protected void |
KDEOS.estimateDensities(Relation<O> rel,
KNNQuery<O> knnq,
DBIDs ids,
WritableDataStore<double[]> densities)
Perform the kernel density estimation step.
|
private Pair<Pair<KNNQuery<O>,KNNQuery<O>>,Pair<RKNNQuery<O>,RKNNQuery<O>>> |
OnlineLOF.getKNNAndRkNNQueries(Database database,
Relation<O> relation,
StepProgress stepprog)
Get the kNN and rkNN queries for the algorithm.
|
protected Pair<KNNQuery<O>,KNNQuery<O>> |
LoOP.getKNNQueries(Database database,
Relation<O> relation,
StepProgress stepprog)
Get the kNN queries for the algorithm.
|
private Pair<KNNQuery<O>,KNNQuery<O>> |
FlexibleLOF.getKNNQueries(Database database,
Relation<O> relation,
StepProgress stepprog)
Get the kNN queries for the algorithm.
|
OutlierResult |
VarianceOfVolume.run(Database database,
Relation<O> relation)
Runs the VOV algorithm on the given database.
|
OutlierResult |
SimplifiedLOF.run(Database database,
Relation<O> relation)
Run the Simple LOF algorithm.
|
OutlierResult |
SimpleKernelDensityLOF.run(Database database,
Relation<O> relation)
Run the naive kernel density LOF algorithm.
|
OutlierResult |
OnlineLOF.run(Database database,
Relation<O> relation)
Performs the Generalized LOF_SCORE algorithm on the given database by
calling
#doRunInTime(Database) and adds a OnlineLOF.LOFKNNListener to
the preprocessors. |
OutlierResult |
LoOP.run(Database database,
Relation<O> relation)
Performs the LoOP algorithm on the given database.
|
OutlierResult |
LOF.run(Database database,
Relation<O> relation)
Runs the LOF algorithm on the given database.
|
OutlierResult |
LOCI.run(Database database,
Relation<O> relation)
Run the algorithm
|
OutlierResult |
LDOF.run(Database database,
Relation<O> relation)
Run the algorithm
|
OutlierResult |
LDF.run(Database database,
Relation<O> relation)
Run the naive kernel density LOF algorithm.
|
OutlierResult |
KDEOS.run(Database database,
Relation<O> rel)
Run the KDEOS outlier detection algorithm.
|
OutlierResult |
INFLO.run(Database database,
Relation<O> relation)
Run the algorithm
|
OutlierResult |
FlexibleLOF.run(Database database,
Relation<O> relation)
Performs the Generalized LOF algorithm on the given database by calling
FlexibleLOF.doRunInTime(de.lmu.ifi.dbs.elki.database.ids.DBIDs, de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery<O>, de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery<O>, de.lmu.ifi.dbs.elki.logging.progress.StepProgress). |
OutlierResult |
COF.run(Database database,
Relation<O> relation)
Runs the COF algorithm on the given database.
|
OutlierResult |
ALOCI.run(Database database,
Relation<O> relation) |
| Constructor and Description |
|---|
ALOCI.ALOCIQuadTree(double[] min,
double[] max,
double[] shift,
int nmin,
Relation<? extends NumberVector> relation)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
OutlierResult |
ParallelSimplifiedLOF.run(Database database,
Relation<O> relation) |
OutlierResult |
ParallelLOF.run(Database database,
Relation<O> relation) |
| 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
|
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.
|
OutlierResult |
ExternalDoubleOutlierScore.run(Database database,
Relation<?> relation)
Run the algorithm.
|
OutlierResult |
FeatureBagging.run(Database database,
Relation<NumberVector> relation)
Run the algorithm on a data set.
|
OutlierResult |
HiCS.run(Relation<V> relation)
Perform HiCS on a given database.
|
| Modifier and Type | Method and Description |
|---|---|
OutlierResult |
TrimmedMeanApproach.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Run the algorithm.
|
OutlierResult |
TrimmedMeanApproach.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Run the algorithm.
|
OutlierResult |
CTLuZTestOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuZTestOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuScatterplotOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuScatterplotOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuMoranScatterplotOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuMoranScatterplotOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuMedianAlgorithm.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuMedianAlgorithm.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
SOF.run(Database database,
Relation<N> spatial,
Relation<O> relation)
The main run method
|
OutlierResult |
SOF.run(Database database,
Relation<N> spatial,
Relation<O> relation)
The main run method
|
OutlierResult |
SLOM.run(Database database,
Relation<N> spatial,
Relation<O> relation) |
OutlierResult |
SLOM.run(Database database,
Relation<N> spatial,
Relation<O> relation) |
OutlierResult |
CTLuMedianMultipleAttributes.run(Database database,
Relation<N> spatial,
Relation<O> attributes)
Run the algorithm
|
OutlierResult |
CTLuMedianMultipleAttributes.run(Database database,
Relation<N> spatial,
Relation<O> attributes)
Run the algorithm
|
OutlierResult |
CTLuMeanMultipleAttributes.run(Database database,
Relation<N> spatial,
Relation<O> attributes)
Run the algorithm
|
OutlierResult |
CTLuMeanMultipleAttributes.run(Database database,
Relation<N> spatial,
Relation<O> attributes)
Run the algorithm
|
OutlierResult |
CTLuGLSBackwardSearchAlgorithm.run(Database database,
Relation<V> relationx,
Relation<? extends NumberVector> relationy)
Run the algorithm
|
OutlierResult |
CTLuGLSBackwardSearchAlgorithm.run(Database database,
Relation<V> relationx,
Relation<? extends NumberVector> relationy)
Run the algorithm
|
OutlierResult |
CTLuRandomWalkEC.run(Relation<P> spatial,
Relation<? extends NumberVector> relation)
Run the algorithm.
|
OutlierResult |
CTLuRandomWalkEC.run(Relation<P> spatial,
Relation<? extends NumberVector> relation)
Run the algorithm.
|
private Pair<DBIDVar,Double> |
CTLuGLSBackwardSearchAlgorithm.singleIteration(Relation<V> relationx,
Relation<? extends NumberVector> relationy)
Run a single iteration of the GLS-SOD modeling step
|
private Pair<DBIDVar,Double> |
CTLuGLSBackwardSearchAlgorithm.singleIteration(Relation<V> relationx,
Relation<? extends NumberVector> relationy)
Run a single iteration of the GLS-SOD modeling step
|
| Modifier and Type | Method and Description |
|---|---|
private DataStore<DBIDs> |
ExtendedNeighborhood.Factory.extendNeighborhood(Database database,
Relation<? extends O> relation)
Method to load the external neighbors.
|
NeighborSetPredicate |
ExternalNeighborhood.Factory.instantiate(Database database,
Relation<?> relation) |
NeighborSetPredicate |
PrecomputedKNearestNeighborNeighborhood.Factory.instantiate(Database database,
Relation<? extends O> relation) |
NeighborSetPredicate |
NeighborSetPredicate.Factory.instantiate(Database database,
Relation<? extends O> relation)
Instantiation method.
|
NeighborSetPredicate |
ExtendedNeighborhood.Factory.instantiate(Database database,
Relation<? extends O> relation) |
private DataStore<DBIDs> |
ExternalNeighborhood.Factory.loadNeighbors(Database database,
Relation<?> relation)
Method to load the external neighbors.
|
| Modifier and Type | Method and Description |
|---|---|
WeightedNeighborSetPredicate |
WeightedNeighborSetPredicate.Factory.instantiate(Database database,
Relation<? extends O> relation)
Instantiation method.
|
UnweightedNeighborhoodAdapter |
UnweightedNeighborhoodAdapter.Factory.instantiate(Database database,
Relation<? extends O> relation) |
LinearWeightedExtendedNeighborhood |
LinearWeightedExtendedNeighborhood.Factory.instantiate(Database database,
Relation<? extends O> relation) |
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<V> |
OUTRES.KernelDensityEstimator.relation
Relation to retrieve data from
|
| Modifier and Type | Method and Description |
|---|---|
protected ArrayList<ArrayList<DBIDs>> |
AbstractAggarwalYuOutlier.buildRanges(Relation<V> relation)
Grid discretization of the data:
Each attribute of data is divided into phi equi-depth ranges. |
private static double[] |
SOD.computePerDimensionVariances(Relation<? extends NumberVector> relation,
Vector center,
DBIDs neighborhood)
Compute the per-dimension variances for the given neighborhood and center.
|
private DBIDs |
SOD.getNearestNeighbors(Relation<V> relation,
SimilarityQuery<V> simQ,
DBIDRef queryObject)
Get the k nearest neighbors in terms of the shared nearest neighbor
distance.
|
OutlierResult |
AggarwalYuEvolutionary.run(Database database,
Relation<V> relation)
Performs the evolutionary algorithm on the given database.
|
OutlierResult |
SOD.run(Relation<V> relation)
Performs the SOD algorithm on the given database.
|
OutlierResult |
OUTRES.run(Relation<V> relation)
Main loop for OUTRES
|
OutlierResult |
AggarwalYuNaive.run(Relation<V> relation)
Run the algorithm on the given relation.
|
| Constructor and Description |
|---|
AggarwalYuEvolutionary.EvolutionarySearch(Relation<V> relation,
ArrayList<ArrayList<DBIDs>> ranges,
int m,
Random random)
Constructor.
|
OUTRES.KernelDensityEstimator(Relation<V> relation)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
OutlierResult |
LibSVMOneClassOutlierDetection.run(Relation<V> relation)
Run one-class SVM.
|
| Modifier and Type | Method and Description |
|---|---|
OutlierResult |
TrivialNoOutlier.run(Relation<?> relation)
Run the actual algorithm.
|
OutlierResult |
TrivialAllOutlier.run(Relation<?> relation)
Run the actual algorithm.
|
OutlierResult |
ByLabelOutlier.run(Relation<?> relation)
Run the algorithm
|
OutlierResult |
TrivialAverageCoordinateOutlier.run(Relation<? extends NumberVector> relation)
Run the actual algorithm.
|
OutlierResult |
TrivialGeneratedOutlier.run(Relation<Model> models,
Relation<NumberVector> vecs,
Relation<?> labels)
Run the algorithm
|
OutlierResult |
TrivialGeneratedOutlier.run(Relation<Model> models,
Relation<NumberVector> vecs,
Relation<?> labels)
Run the algorithm
|
OutlierResult |
TrivialGeneratedOutlier.run(Relation<Model> models,
Relation<NumberVector> vecs,
Relation<?> labels)
Run the algorithm
|
| Modifier and Type | Method and Description |
|---|---|
private void |
EvaluateRetrievalPerformance.computeDistances(ModifiableDoubleDBIDList nlist,
DBIDIter query,
DistanceQuery<O> distQuery,
Relation<O> relation)
Compute the distances to the neighbor objects.
|
protected double |
HopkinsStatisticClusteringTendency.computeNNForRealData(KNNQuery<NumberVector> knnQuery,
Relation<NumberVector> relation,
int dim)
Search nearest neighbors for real data members.
|
void |
EvaluateRetrievalPerformance.KNNEvaluator.evaluateKNN(double[] knnperf,
ModifiableDoubleDBIDList nlist,
Relation<?> lrelation,
TObjectIntHashMap<Object> counters,
Object label)
Evaluate by simulating kNN classification for k=1...maxk
|
private DoubleMinMax |
DistanceStatisticsWithClasses.exactMinMax(Relation<O> relation,
DistanceQuery<O> distFunc)
Compute the exact maximum and minimum.
|
private void |
EvaluateRetrievalPerformance.findMatches(ModifiableDBIDs posn,
Relation<?> lrelation,
Object label)
Find all matching objects.
|
protected void |
HopkinsStatisticClusteringTendency.initializeDataExtends(Relation<NumberVector> relation,
int dim,
double[] min,
double[] extend)
Initialize the uniform sampling area.
|
Result |
HopkinsStatisticClusteringTendency.run(Database database,
Relation<NumberVector> relation)
Runs the algorithm in the timed evaluation part.
|
HistogramResult<DoubleVector> |
RankingQualityHistogram.run(Database database,
Relation<O> relation)
Process a database
|
Result |
EstimateIntrinsicDimensionality.run(Database database,
Relation<O> relation) |
Result |
DistanceQuantileSampler.run(Database database,
Relation<O> rel) |
EvaluateRetrievalPerformance.RetrievalPerformanceResult |
EvaluateRetrievalPerformance.run(Database database,
Relation<O> relation,
Relation<?> lrelation)
Run the algorithm
|
EvaluateRetrievalPerformance.RetrievalPerformanceResult |
EvaluateRetrievalPerformance.run(Database database,
Relation<O> relation,
Relation<?> lrelation)
Run the algorithm
|
CollectionResult<DoubleVector> |
AveragePrecisionAtK.run(Database database,
Relation<O> relation,
Relation<?> lrelation)
Run the algorithm
|
CollectionResult<DoubleVector> |
AveragePrecisionAtK.run(Database database,
Relation<O> relation,
Relation<?> lrelation)
Run the algorithm
|
Result |
RangeQuerySelectivity.run(Database database,
Relation<V> relation) |
private ScalesResult |
AddSingleScale.run(Relation<? extends NumberVector> rel)
Add scales to a single vector relation.
|
private DoubleMinMax |
DistanceStatisticsWithClasses.sampleMinMax(Relation<O> relation,
DistanceQuery<O> distFunc)
Estimate minimum and maximum via sampling.
|
| Modifier and Type | Method and Description |
|---|---|
static Relation<NumberVector> |
GreedyEnsembleExperiment.applyPrescaling(ScalingFunction scaling,
Relation<NumberVector> relation,
DBIDs skip)
Prescale each vector (except when in
skip) with the given scaling
function. |
| Modifier and Type | Method and Description |
|---|---|
static Relation<NumberVector> |
GreedyEnsembleExperiment.applyPrescaling(ScalingFunction scaling,
Relation<NumberVector> relation,
DBIDs skip)
Prescale each vector (except when in
skip) with the given scaling
function. |
| Modifier and Type | Field and Description |
|---|---|
private Relation<? extends NumberVector> |
VectorUtil.SortDBIDsBySingleDimension.data
The relation to sort.
|
| Modifier and Type | Method and Description |
|---|---|
static Vector |
VectorUtil.computeMedoid(Relation<? extends NumberVector> relation,
DBIDs sample)
Compute medoid for a given subset.
|
| Constructor and Description |
|---|
VectorUtil.SortDBIDsBySingleDimension(Relation<? extends NumberVector> data)
Constructor.
|
VectorUtil.SortDBIDsBySingleDimension(Relation<? extends NumberVector> data,
int dim)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
static NumberVector |
ModelUtil.getPrototype(Model model,
Relation<? extends NumberVector> relation)
Get the representative vector for a cluster model.
|
static <V extends NumberVector> |
ModelUtil.getPrototype(Model model,
Relation<? extends V> relation,
NumberVector.Factory<V> factory)
Get (and convert!)
|
static NumberVector |
ModelUtil.getPrototypeOrCentroid(Model model,
Relation<? extends NumberVector> relation,
DBIDs ids)
Get the representative vector for a cluster model, or compute the centroid.
|
static <V extends NumberVector> |
ModelUtil.getPrototypeOrCentroid(Model model,
Relation<? extends V> relation,
DBIDs ids,
NumberVector.Factory<V> factory)
Get the representative vector for a cluster model, or compute the centroid.
|
| Constructor and Description |
|---|
CorrelationAnalysisSolution(LinearEquationSystem solution,
Relation<V> db,
Matrix strongEigenvectors,
Matrix weakEigenvectors,
Matrix similarityMatrix,
Vector centroid)
Provides a new CorrelationAnalysisSolution holding the specified matrix.
|
CorrelationAnalysisSolution(LinearEquationSystem solution,
Relation<V> db,
Matrix strongEigenvectors,
Matrix weakEigenvectors,
Matrix similarityMatrix,
Vector centroid,
NumberFormat nf)
Provides a new CorrelationAnalysisSolution holding the specified matrix and
number format.
|
| Modifier and Type | Field and Description |
|---|---|
protected List<Relation<?>> |
AbstractDatabase.relations
The relations we manage.
|
| Modifier and Type | Method and Description |
|---|---|
private Relation<?> |
HashmapDatabase.addNewRelation(SimpleTypeInformation<?> meta)
Add a new representation for the given meta.
|
protected Relation<?>[] |
HashmapDatabase.alignColumns(ObjectBundle pack)
Find a mapping from package columns to database columns, eventually adding
new database columns when needed.
|
<O> Relation<O> |
Database.getRelation(TypeInformation restriction,
Object... hints)
Get an object representation.
|
<O> Relation<O> |
AbstractDatabase.getRelation(TypeInformation restriction,
Object... hints) |
| Modifier and Type | Method and Description |
|---|---|
Collection<Relation<?>> |
Database.getRelations()
Get all relations of a database.
|
Collection<Relation<?>> |
AbstractDatabase.getRelations() |
| Modifier and Type | Method and Description |
|---|---|
void |
ProxyDatabase.addRelation(Relation<?> relation)
Add a new representation.
|
<O> DistanceQuery<O> |
Database.getDistanceQuery(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
Object... hints)
Get the distance query for a particular distance function.
|
<O> DistanceQuery<O> |
AbstractDatabase.getDistanceQuery(Relation<O> objQuery,
DistanceFunction<? super O> distanceFunction,
Object... hints) |
static <O> KNNQuery<O> |
QueryUtil.getKNNQuery(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
Object... hints)
Get a KNN query object for the given distance function.
|
static <O> RangeQuery<O> |
QueryUtil.getRangeQuery(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
Object... hints)
Get a range query object for the given distance function for radius-based
neighbor search.
|
static <O> RKNNQuery<O> |
QueryUtil.getRKNNQuery(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
Object... hints)
Get a rKNN query object for the given distance function.
|
<O> SimilarityQuery<O> |
Database.getSimilarityQuery(Relation<O> relation,
SimilarityFunction<? super O> similarityFunction,
Object... hints)
Get the similarity query for a particular similarity function.
|
<O> SimilarityQuery<O> |
AbstractDatabase.getSimilarityQuery(Relation<O> objQuery,
SimilarityFunction<? super O> similarityFunction,
Object... hints) |
| Constructor and Description |
|---|
ProxyDatabase(DBIDs ids,
Relation<?>... relations)
Constructor.
|
| Constructor and Description |
|---|
ProxyDatabase(DBIDs ids,
Iterable<Relation<?>> relations)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends O> |
AbstractDistanceQuery.relation
The data to use for this query
|
| Modifier and Type | Method and Description |
|---|---|
Relation<? extends O> |
DistanceQuery.getRelation()
Access the underlying data query.
|
Relation<? extends O> |
AbstractDistanceQuery.getRelation() |
| Constructor and Description |
|---|
AbstractDatabaseDistanceQuery(Relation<? extends O> relation)
Constructor.
|
AbstractDistanceQuery(Relation<? extends O> relation)
Constructor.
|
DBIDDistanceQuery(Relation<DBID> relation,
DBIDDistanceFunction distanceFunction)
Constructor.
|
DBIDRangeDistanceQuery(Relation<DBID> relation,
DBIDRangeDistanceFunction distanceFunction)
Constructor.
|
PrimitiveDistanceQuery(Relation<? extends O> relation,
PrimitiveDistanceFunction<? super O> distanceFunction)
Constructor.
|
PrimitiveDistanceSimilarityQuery(Relation<? extends O> relation,
PrimitiveDistanceFunction<? super O> distanceFunction,
PrimitiveSimilarityFunction<? super O> similarityFunction)
Constructor.
|
SpatialPrimitiveDistanceQuery(Relation<? extends V> relation,
SpatialPrimitiveDistanceFunction<? super V> distanceFunction) |
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends O> |
PreprocessorKNNQuery.relation
The data to use for this query
|
protected Relation<? extends O> |
AbstractDistanceKNNQuery.relation
The data to use for this query
|
| Modifier and Type | Method and Description |
|---|---|
private KNNHeap |
LinearScanPrimitiveDistanceKNNQuery.linearScan(Relation<? extends O> relation,
DBIDIter iter,
O obj,
KNNHeap heap)
Main loop of the linear scan.
|
private KNNHeap |
LinearScanEuclideanDistanceKNNQuery.linearScan(Relation<? extends O> relation,
DBIDIter iter,
O obj,
KNNHeap heap)
Main loop of the linear scan.
|
| Constructor and Description |
|---|
PreprocessorKNNQuery(Relation<O> relation,
AbstractMaterializeKNNPreprocessor<O> preprocessor)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends O> |
AbstractDistanceRangeQuery.relation
The data to use for this query
|
| Modifier and Type | Method and Description |
|---|---|
private void |
LinearScanPrimitiveDistanceRangeQuery.linearScan(Relation<? extends O> relation,
DBIDIter iter,
O obj,
double range,
ModifiableDoubleDBIDList result)
Main loop for linear scan,
|
private void |
LinearScanEuclideanDistanceRangeQuery.linearScan(Relation<? extends O> relation,
DBIDIter iter,
O obj,
double range,
ModifiableDoubleDBIDList result)
Main loop for linear scan,
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends O> |
PreprocessorRKNNQuery.relation
The data to use for this query
|
protected Relation<? extends O> |
AbstractRKNNQuery.relation
The data to use for this query
|
| Constructor and Description |
|---|
PreprocessorRKNNQuery(Relation<O> database,
MaterializeKNNAndRKNNPreprocessor.Factory<O> preprocessor)
Constructor.
|
PreprocessorRKNNQuery(Relation<O> relation,
MaterializeKNNAndRKNNPreprocessor<O> preprocessor)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends O> |
AbstractSimilarityQuery.relation
The data to use for this query
|
| Modifier and Type | Method and Description |
|---|---|
Relation<? extends O> |
SimilarityQuery.getRelation()
Access the underlying data query.
|
Relation<? extends O> |
AbstractSimilarityQuery.getRelation() |
| Constructor and Description |
|---|
AbstractDBIDSimilarityQuery(Relation<? extends O> relation)
Constructor.
|
AbstractSimilarityQuery(Relation<? extends O> relation)
Constructor.
|
PrimitiveSimilarityQuery(Relation<? extends O> relation,
PrimitiveSimilarityFunction<? super O> similarityFunction)
Constructor.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
DoubleRelation
Interface for double-valued relations.
|
interface |
ModifiableRelation<O>
Relations that allow modification.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractRelation<O>
Abstract base class for relations.
|
class |
ConvertToStringView
Representation adapter that uses toString() to produce a string
representation.
|
class |
DBIDView
Pseudo-representation that is the object ID itself.
|
class |
MaterializedDoubleRelation
Represents a single representation.
|
class |
MaterializedRelation<O>
Represents a single representation.
|
class |
ProjectedView<IN,OUT>
Projected relation view (non-materialized)
|
class |
ProxyView<O>
A virtual partitioning of the database.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<? extends O> |
RelationUtil.RelationObjectIterator.database
The database we use.
|
(package private) Relation<? extends O> |
RelationUtil.CollectionFromRelation.db
The database we query.
|
(package private) Relation<?> |
ConvertToStringView.existing
The database we use
|
private Relation<O> |
ProxyView.inner
The wrapped representation where we get the IDs from.
|
private Relation<IN> |
ProjectedView.inner
The wrapped representation where we get the IDs from.
|
| Modifier and Type | Method and Description |
|---|---|
static <V extends NumberVector,T extends NumberVector> |
RelationUtil.relationUglyVectorCast(Relation<T> database)
An ugly vector type cast unavoidable in some situations due to Generics.
|
| Modifier and Type | Method and Description |
|---|---|
static <V extends FeatureVector<?>> |
RelationUtil.assumeVectorField(Relation<V> relation)
Get the vector field type information from a relation.
|
static double[][] |
RelationUtil.computeMinMax(Relation<? extends NumberVector> relation)
Determines the minimum and maximum values in each dimension of all objects
stored in the given database.
|
static int |
RelationUtil.dimensionality(Relation<? extends SpatialComparable> relation)
Get the dimensionality of a database relation.
|
static <V extends SpatialComparable> |
RelationUtil.getColumnLabel(Relation<? extends V> rel,
int col)
Get the column name or produce a generic label "Column XY".
|
static <V extends NumberVector> |
RelationUtil.getNumberVectorFactory(Relation<V> relation)
Get the number vector factory of a database relation.
|
static double[][] |
RelationUtil.relationAsMatrix(Relation<? extends NumberVector> relation,
ArrayDBIDs ids)
Copy a relation into a double matrix.
|
static <V extends NumberVector,T extends NumberVector> |
RelationUtil.relationUglyVectorCast(Relation<T> database)
An ugly vector type cast unavoidable in some situations due to Generics.
|
static double[] |
RelationUtil.variances(Relation<? extends NumberVector> database,
NumberVector centroid,
DBIDs ids)
Determines the variances in each dimension of the specified objects stored
in the given database.
|
| Constructor and Description |
|---|
ConvertToStringView(Relation<?> existing)
Constructor.
|
ProjectedView(Relation<IN> inner,
Projection<IN,OUT> projection)
Constructor.
|
ProxyView(DBIDs idview,
Relation<O> inner)
Constructor.
|
RelationUtil.CollectionFromRelation(Relation<? extends O> db)
Constructor.
|
RelationUtil.RelationObjectIterator(DBIDIter iter,
Relation<? extends O> database)
Full Constructor.
|
RelationUtil.RelationObjectIterator(Relation<? extends O> database)
Simplified constructor.
|
| Modifier and Type | Method and Description |
|---|---|
<O extends DBID> |
AbstractDBIDRangeDistanceFunction.instantiate(Relation<O> database) |
<T extends NumberVector> |
AbstractSpatialNorm.instantiate(Relation<T> relation) |
<T extends NumberVector> |
AbstractSpatialDistanceFunction.instantiate(Relation<T> relation) |
<T extends DBID> |
RandomStableDistanceFunction.instantiate(Relation<T> relation) |
<T extends O> |
SharedNearestNeighborJaccardDistanceFunction.instantiate(Relation<T> database) |
<T extends O> |
DistanceFunction.instantiate(Relation<T> relation)
Instantiate with a database to get the actual distance query.
|
<T extends O> |
AbstractPrimitiveDistanceFunction.instantiate(Relation<T> relation)
Instantiate with a database to get the actual distance query.
|
<T extends V> |
SpatialPrimitiveDistanceFunction.instantiate(Relation<T> relation) |
| Constructor and Description |
|---|
AbstractDatabaseDistanceFunction.Instance(Relation<O> database,
DistanceFunction<? super O> parent)
Constructor.
|
AbstractIndexBasedDistanceFunction.Instance(Relation<O> database,
I index,
F parent)
Constructor.
|
SharedNearestNeighborJaccardDistanceFunction.Instance(Relation<T> database,
SharedNearestNeighborIndex<T> preprocessor,
SharedNearestNeighborJaccardDistanceFunction<T> parent)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
<T extends O> |
LnSimilarityAdapter.instantiate(Relation<T> database) |
<T extends O> |
LinearAdapterLinear.instantiate(Relation<T> database) |
<T extends O> |
ArccosSimilarityAdapter.instantiate(Relation<T> database) |
abstract <T extends O> |
AbstractSimilarityAdapter.instantiate(Relation<T> database) |
| Constructor and Description |
|---|
AbstractSimilarityAdapter.Instance(Relation<O> database,
DistanceFunction<? super O> parent,
SimilarityQuery<? super O> similarityQuery)
Constructor.
|
ArccosSimilarityAdapter.Instance(Relation<O> database,
DistanceFunction<? super O> parent,
SimilarityQuery<O> similarityQuery)
Constructor.
|
LinearAdapterLinear.Instance(Relation<O> database,
DistanceFunction<? super O> parent,
SimilarityQuery<? super O> similarityQuery)
Constructor.
|
LnSimilarityAdapter.Instance(Relation<O> database,
DistanceFunction<? super O> parent,
SimilarityQuery<O> similarityQuery)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
<O extends DBID> |
FileBasedFloatDistanceFunction.instantiate(Relation<O> database) |
<O extends DBID> |
FileBasedDoubleDistanceFunction.instantiate(Relation<O> database) |
| Modifier and Type | Method and Description |
|---|---|
<T extends NumberVector> |
HellingerDistanceFunction.instantiate(Relation<T> database) |
| Modifier and Type | Method and Description |
|---|---|
<T extends O> |
JaccardSimilarityDistanceFunction.instantiate(Relation<T> relation) |
| Modifier and Type | Method and Description |
|---|---|
<T extends NumberVector> |
SubspaceLPNormDistanceFunction.instantiate(Relation<T> database) |
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends DBID> |
AbstractDBIDSimilarityFunction.database
The database we work on
|
| Modifier and Type | Method and Description |
|---|---|
<T extends O> |
SimilarityFunction.instantiate(Relation<T> relation)
Instantiate with a representation to get the actual similarity query.
|
<T extends O> |
SharedNearestNeighborSimilarityFunction.instantiate(Relation<T> database) |
<T extends O> |
IndexBasedSimilarityFunction.instantiate(Relation<T> database)
Preprocess the database to get the actual distance function.
|
<T extends O> |
FractionalSharedNearestNeighborSimilarityFunction.instantiate(Relation<T> database) |
<T extends O> |
AbstractPrimitiveSimilarityFunction.instantiate(Relation<T> relation) |
abstract <T extends O> |
AbstractIndexBasedSimilarityFunction.instantiate(Relation<T> database) |
| Constructor and Description |
|---|
AbstractDBIDSimilarityFunction(Relation<? extends DBID> database)
Constructor.
|
AbstractIndexBasedSimilarityFunction.Instance(Relation<O> database,
I index)
Constructor.
|
FractionalSharedNearestNeighborSimilarityFunction.Instance(Relation<T> database,
SharedNearestNeighborIndex<T> preprocessor,
FractionalSharedNearestNeighborSimilarityFunction<? super T> similarityFunction)
Constructor.
|
SharedNearestNeighborSimilarityFunction.Instance(Relation<O> database,
SharedNearestNeighborIndex<O> preprocessor,
SharedNearestNeighborSimilarityFunction<? super O> similarityFunction)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
<T extends Cluster<?>> |
ClusterJaccardSimilarityFunction.instantiate(Relation<T> relation) |
<T extends Cluster<?>> |
ClusterIntersectionSimilarityFunction.instantiate(Relation<T> relation) |
<T extends Clustering<?>> |
ClusteringRandIndexSimilarityFunction.instantiate(Relation<T> relation) |
<T extends Clustering<?>> |
ClusteringFowlkesMallowsSimilarityFunction.instantiate(Relation<T> relation) |
<T extends Clustering<?>> |
ClusteringDistanceSimilarityFunction.instantiate(Relation<T> relation) |
<T extends Clustering<?>> |
ClusteringBCubedF1SimilarityFunction.instantiate(Relation<T> relation) |
<T extends Clustering<?>> |
ClusteringAdjustedRandIndexSimilarityFunction.instantiate(Relation<T> relation) |
| Modifier and Type | Method and Description |
|---|---|
<T extends NumberVector> |
PolynomialKernelFunction.instantiate(Relation<T> database) |
| Constructor and Description |
|---|
KernelMatrix(PrimitiveSimilarityFunction<? super O> kernelFunction,
Relation<? extends O> relation,
DBIDs ids)
Provides a new kernel matrix.
|
KernelMatrix(SimilarityQuery<? super O> kernelFunction,
Relation<? extends O> relation,
DBIDs ids)
Provides a new kernel matrix.
|
| Modifier and Type | Method and Description |
|---|---|
static int |
EvaluateSimplifiedSilhouette.centroids(Relation<? extends NumberVector> rel,
List<? extends Cluster<?>> clusters,
NumberVector[] centroids,
NoiseHandling noiseOption)
Compute centroids.
|
protected double[] |
EvaluateConcordantPairs.computeWithinDistances(Relation<? extends NumberVector> rel,
List<? extends Cluster<?>> clusters,
int withinPairs) |
double |
EvaluateVarianceRatioCriteria.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateSquaredErrors.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateSimplifiedSilhouette.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluatePBMIndex.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateDaviesBouldin.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateConcordantPairs.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateCIndex.evaluateClustering(Database db,
Relation<? extends O> rel,
DistanceQuery<O> dq,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateSilhouette.evaluateClustering(Database db,
Relation<O> rel,
DistanceQuery<O> dq,
Clustering<?> c)
Evaluate a single clustering.
|
static int |
EvaluateVarianceRatioCriteria.globalCentroid(Centroid overallCentroid,
Relation<? extends NumberVector> rel,
List<? extends Cluster<?>> clusters,
NumberVector[] centroids,
NoiseHandling noiseOption)
Update the global centroid.
|
double[] |
EvaluateDaviesBouldin.withinGroupDistances(Relation<? extends NumberVector> rel,
List<? extends Cluster<?>> clusters,
NumberVector[] centroids) |
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<?> |
ComputeSimilarityMatrixImage.SimilarityMatrix.relation
The database
|
| Modifier and Type | Method and Description |
|---|---|
Relation<?> |
ComputeSimilarityMatrixImage.SimilarityMatrix.getRelation()
Get the relation
|
| Modifier and Type | Method and Description |
|---|---|
private ComputeSimilarityMatrixImage.SimilarityMatrix |
ComputeSimilarityMatrixImage.computeSimilarityMatrixImage(Relation<O> relation,
DBIDIter iter)
Compute the actual similarity image.
|
| Constructor and Description |
|---|
ComputeSimilarityMatrixImage.SimilarityMatrix(RenderedImage img,
Relation<?> relation,
ArrayDBIDs ids)
Constructor
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<O> |
AbstractIndex.relation
The representation we are bound to.
|
| Modifier and Type | Method and Description |
|---|---|
I |
IndexFactory.instantiate(Relation<V> relation)
Sets the database in the distance function of this index (if existing).
|
| Constructor and Description |
|---|
AbstractIndex(Relation<O> relation)
Constructor.
|
AbstractRefiningIndex(Relation<O> relation)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
Relation<? extends O> |
PrecomputedDistanceMatrix.PrecomputedDistanceQuery.getRelation() |
| Modifier and Type | Method and Description |
|---|---|
PrecomputedDistanceMatrix<O> |
PrecomputedDistanceMatrix.Factory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
PrecomputedDistanceMatrix(Relation<O> relation,
DistanceFunction<? super O> distanceFunction)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
InMemoryIDistanceIndex<V> |
InMemoryIDistanceIndex.Factory.instantiate(Relation<V> relation) |
| Constructor and Description |
|---|
InMemoryIDistanceIndex(Relation<O> relation,
DistanceQuery<O> distance,
KMedoidsInitialization<O> initialization,
int numref)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
InMemoryInvertedIndex<V> |
InMemoryInvertedIndex.Factory.instantiate(Relation<V> relation) |
| Constructor and Description |
|---|
InMemoryInvertedIndex(Relation<V> relation)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
InMemoryLSHIndex.Instance |
InMemoryLSHIndex.instantiate(Relation<V> relation) |
| Constructor and Description |
|---|
InMemoryLSHIndex.Instance(Relation<V> relation,
ArrayList<? extends LocalitySensitiveHashFunction<? super V>> hashfunctions,
int numberOfBuckets)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
ArrayList<? extends LocalitySensitiveHashFunction<? super NumberVector>> |
CosineHashFunctionFamily.generateHashFunctions(Relation<? extends NumberVector> relation,
int l) |
ArrayList<? extends LocalitySensitiveHashFunction<? super NumberVector>> |
AbstractProjectedHashFunctionFamily.generateHashFunctions(Relation<? extends NumberVector> relation,
int l) |
ArrayList<? extends LocalitySensitiveHashFunction<? super V>> |
LocalitySensitiveHashFunctionFamily.generateHashFunctions(Relation<? extends V> relation,
int l)
Generate hash functions for the given relation.
|
| Modifier and Type | Method and Description |
|---|---|
I |
LocalProjectionIndex.Factory.instantiate(Relation<V> relation)
Instantiate the index for a given database.
|
| Constructor and Description |
|---|
AbstractPreprocessorIndex(Relation<O> relation)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<V> |
RandomProjectedNeighborsAndDensities.points
entire point set
|
| Modifier and Type | Method and Description |
|---|---|
void |
RandomProjectedNeighborsAndDensities.computeSetsBounds(Relation<V> points,
int minSplitSize,
DBIDs ptList)
Create random projections, project points and put points into sets of size
about minSplitSize/2
|
| Modifier and Type | Method and Description |
|---|---|
private MetricalIndexTree<O,N,E> |
MetricalIndexApproximationMaterializeKNNPreprocessor.getMetricalIndex(Relation<O> relation)
Do some (limited) type checking, then cast the database into a spatial
database.
|
SpatialApproximationMaterializeKNNPreprocessor<NumberVector,N,E> |
SpatialApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<NumberVector> relation) |
RandomSampleKNNPreprocessor<O> |
RandomSampleKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
PartitionApproximationMaterializeKNNPreprocessor<O> |
PartitionApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
MetricalIndexApproximationMaterializeKNNPreprocessor<O,N,E> |
MetricalIndexApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
MaterializeKNNPreprocessor<O> |
MaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
MaterializeKNNAndRKNNPreprocessor<O> |
MaterializeKNNAndRKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
KNNJoinMaterializeKNNPreprocessor<O> |
KNNJoinMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
CachedDoubleDistanceKNNPreprocessor<O> |
CachedDoubleDistanceKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
abstract AbstractMaterializeKNNPreprocessor<O> |
AbstractMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
SpacefillingMaterializeKNNPreprocessor<V> |
SpacefillingMaterializeKNNPreprocessor.Factory.instantiate(Relation<V> relation) |
SpacefillingKNNPreprocessor<V> |
SpacefillingKNNPreprocessor.Factory.instantiate(Relation<V> relation) |
NaiveProjectedKNNPreprocessor<V> |
NaiveProjectedKNNPreprocessor.Factory.instantiate(Relation<V> relation) |
| Constructor and Description |
|---|
AbstractMaterializeKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k)
Constructor.
|
CachedDoubleDistanceKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k,
File file)
Constructor.
|
KNNJoinMaterializeKNNPreprocessor(Relation<V> relation,
DistanceFunction<? super V> distanceFunction,
int k)
Constructor.
|
MaterializeKNNAndRKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k)
Constructor.
|
MaterializeKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k)
Constructor with preprocessing step.
|
MetricalIndexApproximationMaterializeKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k)
Constructor
|
NaiveProjectedKNNPreprocessor(Relation<O> relation,
double window,
int projections,
RandomProjectionFamily proj,
Random random)
Constructor.
|
PartitionApproximationMaterializeKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k,
int partitions,
RandomFactory rnd)
Constructor
|
RandomSampleKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k,
double share,
RandomFactory rnd)
Constructor.
|
SpacefillingKNNPreprocessor(Relation<O> relation,
List<SpatialSorter> curvegen,
double window,
int variants,
int odim,
RandomProjectionFamily proj,
Random random)
Constructor.
|
SpacefillingMaterializeKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k,
List<SpatialSorter> curvegen,
double window,
int variants,
Random random)
Constructor.
|
SpatialApproximationMaterializeKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k)
Constructor
|
| Modifier and Type | Method and Description |
|---|---|
I |
FilteredLocalPCAIndex.Factory.instantiate(Relation<NV> relation)
Instantiate the index for a given database.
|
abstract I |
AbstractFilteredPCAIndex.Factory.instantiate(Relation<NV> relation) |
KNNQueryFilteredPCAIndex<V> |
KNNQueryFilteredPCAIndex.Factory.instantiate(Relation<V> relation) |
| Constructor and Description |
|---|
AbstractFilteredPCAIndex(Relation<NV> relation,
PCAFilteredRunner pca)
Constructor.
|
KNNQueryFilteredPCAIndex(Relation<NV> relation,
PCAFilteredRunner pca,
KNNQuery<NV> knnQuery,
int k)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
private long[] |
HiSCPreferenceVectorIndex.determinePreferenceVector(Relation<V> relation,
DBIDRef id,
DBIDs neighborIDs,
StringBuilder msg)
Determines the preference vector according to the specified neighbor ids.
|
private long[] |
DiSHPreferenceVectorIndex.determinePreferenceVector(Relation<V> relation,
ModifiableDBIDs[] neighborIDs,
StringBuilder msg)
Determines the preference vector according to the specified neighbor ids.
|
private long[] |
DiSHPreferenceVectorIndex.determinePreferenceVectorByApriori(Relation<V> relation,
ModifiableDBIDs[] neighborIDs,
StringBuilder msg)
Determines the preference vector with the apriori strategy.
|
private RangeQuery<V>[] |
DiSHPreferenceVectorIndex.initRangeQueries(Relation<V> relation,
int dimensionality)
Initializes the dimension selecting distancefunctions to determine the
preference vectors.
|
I |
PreferenceVectorIndex.Factory.instantiate(Relation<V> relation)
Instantiate the index for a given database.
|
HiSCPreferenceVectorIndex<V> |
HiSCPreferenceVectorIndex.Factory.instantiate(Relation<V> relation) |
DiSHPreferenceVectorIndex<V> |
DiSHPreferenceVectorIndex.Factory.instantiate(Relation<V> relation) |
abstract I |
AbstractPreferenceVectorIndex.Factory.instantiate(Relation<V> relation) |
| Constructor and Description |
|---|
AbstractPreferenceVectorIndex(Relation<NV> relation)
Constructor.
|
DiSHPreferenceVectorIndex(Relation<V> relation,
double[] epsilon,
int minpts,
DiSHPreferenceVectorIndex.Strategy strategy)
Constructor.
|
HiSCPreferenceVectorIndex(Relation<V> relation,
double alpha,
int k)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
SharedNearestNeighborPreprocessor<O> |
SharedNearestNeighborPreprocessor.Factory.instantiate(Relation<O> relation) |
I |
SharedNearestNeighborIndex.Factory.instantiate(Relation<O> database)
Instantiate the index for a given database.
|
| Constructor and Description |
|---|
SharedNearestNeighborPreprocessor(Relation<O> relation,
int numberOfNeighbors,
DistanceFunction<O> distanceFunction)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<O> |
ProjectedIndex.relation
The relation we predend to index.
|
(package private) Relation<I> |
ProjectedIndex.view
The view that we really index.
|
| Modifier and Type | Method and Description |
|---|---|
ProjectedIndex<O,I> |
ProjectedIndex.Factory.instantiate(Relation<O> relation) |
ProjectedIndex<O,O> |
LngLatAsECEFIndex.Factory.instantiate(Relation<O> relation) |
ProjectedIndex<O,O> |
LatLngAsECEFIndex.Factory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
LatLngAsECEFIndex(Relation<O> relation,
Projection<O,O> proj,
Relation<O> view,
Index inner,
boolean norefine)
Constructor.
|
LatLngAsECEFIndex(Relation<O> relation,
Projection<O,O> proj,
Relation<O> view,
Index inner,
boolean norefine)
Constructor.
|
LngLatAsECEFIndex(Relation<O> relation,
Projection<O,O> proj,
Relation<O> view,
Index inner,
boolean norefine)
Constructor.
|
LngLatAsECEFIndex(Relation<O> relation,
Projection<O,O> proj,
Relation<O> view,
Index inner,
boolean norefine)
Constructor.
|
ProjectedIndex(Relation<O> relation,
Projection<O,I> proj,
Relation<I> view,
Index inner,
boolean norefine,
double kmulti)
Constructor.
|
ProjectedIndex(Relation<O> relation,
Projection<O,I> proj,
Relation<I> view,
Index inner,
boolean norefine,
double kmulti)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
SimplifiedCoverTree<O> |
SimplifiedCoverTree.Factory.instantiate(Relation<O> relation) |
CoverTree<O> |
CoverTree.Factory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
AbstractCoverTree(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
double expansion,
int truncate)
Constructor.
|
CoverTree(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
double expansion,
int truncate)
Constructor.
|
SimplifiedCoverTree(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
double expansion,
int truncate)
Constructor.
|
| Constructor and Description |
|---|
AbstractMkTree(Relation<O> relation,
PageFile<N> pagefile,
S settings)
Constructor.
|
AbstractMkTreeUnified(Relation<O> relation,
PageFile<N> pagefile,
S settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
MkAppTreeIndex.relation
The relation indexed
|
| Modifier and Type | Method and Description |
|---|---|
MkAppTreeIndex<O> |
MkAppTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
MkAppTree(Relation<O> relation,
PageFile<MkAppTreeNode<O>> pageFile,
MkAppTreeSettings<O> settings)
Constructor.
|
MkAppTreeIndex(Relation<O> relation,
PageFile<MkAppTreeNode<O>> pageFile,
MkAppTreeSettings<O> settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
MkCoPTreeIndex.relation
Relation indexed
|
| Modifier and Type | Method and Description |
|---|---|
MkCoPTreeIndex<O> |
MkCopTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
MkCoPTree(Relation<O> relation,
PageFile<MkCoPTreeNode<O>> pagefile,
MkTreeSettings<O,MkCoPTreeNode<O>,MkCoPEntry> settings)
Constructor.
|
MkCoPTreeIndex(Relation<O> relation,
PageFile<MkCoPTreeNode<O>> pageFile,
MkTreeSettings<O,MkCoPTreeNode<O>,MkCoPEntry> settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
MkMaxTreeIndex.relation
Relation indexed.
|
| Modifier and Type | Method and Description |
|---|---|
MkMaxTreeIndex<O> |
MkMaxTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
MkMaxTree(Relation<O> relation,
PageFile<MkMaxTreeNode<O>> pagefile,
MkTreeSettings<O,MkMaxTreeNode<O>,MkMaxEntry> settings)
Constructor.
|
MkMaxTreeIndex(Relation<O> relation,
PageFile<MkMaxTreeNode<O>> pagefile,
MkTreeSettings<O,MkMaxTreeNode<O>,MkMaxEntry> settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
MkTabTreeIndex.relation
The relation indexed.
|
| Modifier and Type | Method and Description |
|---|---|
MkTabTreeIndex<O> |
MkTabTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
MkTabTree(Relation<O> relation,
PageFile<MkTabTreeNode<O>> pagefile,
MkTreeSettings<O,MkTabTreeNode<O>,MkTabEntry> settings)
Constructor.
|
MkTabTreeIndex(Relation<O> relation,
PageFile<MkTabTreeNode<O>> pagefile,
MkTreeSettings<O,MkTabTreeNode<O>,MkTabEntry> settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
MTreeIndex.relation
The relation indexed.
|
| Modifier and Type | Method and Description |
|---|---|
MTreeIndex<O> |
MTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
MTreeIndex(Relation<O> relation,
PageFile<MTreeNode<O>> pagefile,
MTreeSettings<O,MTreeNode<O>,MTreeEntry> settings)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
SmallMemoryKDTree<O> |
SmallMemoryKDTree.Factory.instantiate(Relation<O> relation) |
MinimalisticMemoryKDTree<O> |
MinimalisticMemoryKDTree.Factory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
MinimalisticMemoryKDTree.CountSortAccesses(Counter objaccess,
Relation<? extends NumberVector> data)
Constructor.
|
MinimalisticMemoryKDTree(Relation<O> relation,
int leafsize)
Constructor.
|
SmallMemoryKDTree(Relation<O> relation,
int leafsize)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
DeLiCluTreeIndex.relation
The relation we index.
|
| Modifier and Type | Method and Description |
|---|---|
DeLiCluTreeIndex<O> |
DeLiCluTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
DeLiCluTreeIndex(Relation<O> relation,
PageFile<DeLiCluNode> pagefile,
AbstractRTreeSettings settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
FlatRStarTreeIndex.relation
The relation we index
|
| Modifier and Type | Method and Description |
|---|---|
FlatRStarTreeIndex<O> |
FlatRStarTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
FlatRStarTreeIndex(Relation<O> relation,
PageFile<FlatRStarTreeNode> pagefile,
AbstractRTreeSettings settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends O> |
RStarTreeRangeQuery.relation
Relation we query.
|
protected Relation<? extends O> |
RStarTreeKNNQuery.relation
Relation we query.
|
| Constructor and Description |
|---|
EuclideanRStarTreeKNNQuery(AbstractRStarTree<?,?,?> tree,
Relation<? extends O> relation)
Constructor.
|
EuclideanRStarTreeRangeQuery(AbstractRStarTree<?,?,?> tree,
Relation<? extends O> relation)
Constructor.
|
RStarTreeKNNQuery(AbstractRStarTree<?,?,?> tree,
Relation<? extends O> relation,
SpatialPrimitiveDistanceFunction<? super O> distanceFunction)
Constructor.
|
RStarTreeRangeQuery(AbstractRStarTree<?,?,?> tree,
Relation<? extends O> relation,
SpatialPrimitiveDistanceFunction<? super O> distanceFunction)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
RdKNNTree.relation
The relation we query.
|
| Modifier and Type | Method and Description |
|---|---|
RdKNNTree<O> |
RdKNNTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
RdKNNTree(Relation<O> relation,
PageFile<RdKNNNode> pagefile,
RdkNNSettings<O> settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
RStarTreeIndex.relation
Relation
|
| Modifier and Type | Method and Description |
|---|---|
RStarTreeIndex<O> |
RStarTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
RStarTreeIndex(Relation<O> relation,
PageFile<RStarTreeNode> pagefile,
AbstractRTreeSettings settings)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
VAFile<V> |
VAFile.Factory.instantiate(Relation<V> relation) |
PartialVAFile<V> |
PartialVAFile.Factory.instantiate(Relation<V> relation) |
void |
VAFile.setPartitions(Relation<V> relation)
Initialize the data set grid by computing quantiles.
|
| Constructor and Description |
|---|
DAFile(Relation<? extends NumberVector> relation,
int dimension,
int partitions)
Constructor.
|
PartialVAFile(int pageSize,
Relation<V> relation,
int partitions)
Constructor.
|
VAFile(int pageSize,
Relation<V> relation,
int partitions)
Constructor.
|
| 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
|
private ArrayList<ArrayList<DBIDs>> |
MCEDimensionSimilarity.buildPartitions(Relation<? extends NumberVector> relation,
DBIDs ids,
int depth,
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.
|
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
|
void |
SlopeInversionDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix) |
void |
SlopeDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix) |
void |
SURFINGDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix) |
void |
MCEDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix) |
void |
HiCSDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix) |
void |
HSMDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix) |
void |
CovarianceDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix) |
void |
DimensionSimilarity.computeDimensionSimilarites(Relation<? extends V> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix)
Compute the dimension similarity matrix
|
| Modifier and Type | Method and Description |
|---|---|
<F extends NumberVector> |
CovarianceMatrix.getMeanVector(Relation<? extends F> relation)
Get the mean as vector.
|
static ProjectedCentroid |
ProjectedCentroid.make(long[] dims,
Relation<? extends NumberVector> relation)
Static Constructor from a relation.
|
static ProjectedCentroid |
ProjectedCentroid.make(long[] dims,
Relation<? extends NumberVector> relation,
DBIDs ids)
Static Constructor from a relation.
|
static CovarianceMatrix |
CovarianceMatrix.make(Relation<? extends NumberVector> relation)
Static Constructor from a full relation.
|
static Centroid |
Centroid.make(Relation<? extends NumberVector> relation)
Static constructor from an existing relation.
|
static CovarianceMatrix |
CovarianceMatrix.make(Relation<? extends NumberVector> relation,
DBIDs ids)
Static Constructor from a full relation.
|
static Centroid |
Centroid.make(Relation<? extends NumberVector> relation,
DBIDs ids)
Static constructor from an existing relation.
|
<F extends NumberVector> |
Centroid.toVector(Relation<? extends F> relation)
Get the data as vector.
|
| Modifier and Type | Method and Description |
|---|---|
Matrix |
StandardCovarianceMatrixBuilder.processDatabase(Relation<? extends NumberVector> database)
Compute Covariance Matrix for a complete database.
|
PCAResult |
PCARunner.processDatabase(Relation<? extends NumberVector> database)
Run PCA on the complete database.
|
Matrix |
CovarianceMatrixBuilder.processDatabase(Relation<? extends NumberVector> database)
Compute Covariance Matrix for a complete database.
|
Matrix |
AbstractCovarianceMatrixBuilder.processDatabase(Relation<? extends NumberVector> database) |
Matrix |
WeightedCovarianceMatrixBuilder.processIds(DBIDs ids,
Relation<? extends NumberVector> relation)
Weighted Covariance Matrix for a set of IDs.
|
Matrix |
StandardCovarianceMatrixBuilder.processIds(DBIDs ids,
Relation<? extends NumberVector> database)
Compute Covariance Matrix for a collection of database IDs.
|
Matrix |
RANSACCovarianceMatrixBuilder.processIds(DBIDs ids,
Relation<? extends NumberVector> relation) |
PCAResult |
PCARunner.processIds(DBIDs ids,
Relation<? extends NumberVector> database)
Run PCA on a collection of database IDs.
|
PCAFilteredResult |
PCAFilteredRunner.processIds(DBIDs ids,
Relation<? extends NumberVector> database)
Run PCA on a collection of database IDs.
|
PCAFilteredResult |
PCAFilteredAutotuningRunner.processIds(DBIDs ids,
Relation<? extends NumberVector> database) |
Matrix |
CovarianceMatrixBuilder.processIds(DBIDs ids,
Relation<? extends NumberVector> database)
Compute Covariance Matrix for a collection of database IDs.
|
abstract Matrix |
AbstractCovarianceMatrixBuilder.processIds(DBIDs ids,
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.
|
PCAFilteredResult |
PCAFilteredAutotuningRunner.processQueryResult(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) |
Matrix |
WeightedCovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results,
Relation<? extends NumberVector> database,
int k)
Compute Covariance Matrix for a QueryResult Collection.
|
Matrix |
CovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results,
Relation<? extends NumberVector> database,
int k)
Compute Covariance Matrix for a QueryResult Collection.
|
Matrix |
AbstractCovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results,
Relation<? extends NumberVector> database,
int k) |
| Modifier and Type | Method and Description |
|---|---|
static LinearScale[] |
Scales.calcScales(Relation<? extends SpatialComparable> db)
Compute a linear scale for each dimension.
|
| Constructor and Description |
|---|
ZCurveTransformer(Relation<? extends NumberVector> relation,
DBIDs ids)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
static List<Relation<?>> |
ResultUtil.getRelations(Result r)
Collect all Annotation results from a Result
|
| Modifier and Type | Method and Description |
|---|---|
private DoubleObjPair<Polygon> |
KMLOutputHandler.buildHullsRecursively(Cluster<Model> clu,
Hierarchy<Cluster<Model>> hier,
Map<Object,DoubleObjPair<Polygon>> hulls,
Relation<? extends NumberVector> coords)
Recursively step through the clusters to build the hulls.
|
static SamplingResult |
ResultUtil.getSamplingResult(Relation<?> rel)
Get the sampling result attached to a relation
|
static ScalesResult |
ResultUtil.getScalesResult(Relation<? extends SpatialComparable> rel)
Get (or create) a scales result for a relation.
|
| Modifier and Type | Method and Description |
|---|---|
private StringBuilder |
KMLOutputHandler.makeDescription(Collection<Relation<?>> relations,
DBIDRef id)
Make an HTML description.
|
| Constructor and Description |
|---|
SamplingResult(Relation<?> rel)
Constructor.
|
ScalesResult(Relation<? extends SpatialComparable> relation)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
TextWriter.printObject(TextWriterStream out,
Database db,
DBIDRef objID,
List<Relation<?>> ra) |
private void |
TextWriter.writeClusterResult(Database db,
StreamFactory streamOpener,
Clustering<Model> clustering,
Cluster<Model> clus,
List<Relation<?>> ra,
NamingScheme naming) |
private void |
TextWriter.writeOrderingResult(Database db,
StreamFactory streamOpener,
OrderingResult or,
List<Relation<?>> ra) |
| Modifier and Type | Method and Description |
|---|---|
static Relation<String> |
DatabaseUtil.guessLabelRepresentation(Database database)
Guess a potentially label-like representation, preferring class labels.
|
static Relation<String> |
DatabaseUtil.guessObjectLabelRepresentation(Database database)
Guess a potentially object label-like representation.
|
| Modifier and Type | Method and Description |
|---|---|
static SortedSet<ClassLabel> |
DatabaseUtil.getClassLabels(Relation<? extends ClassLabel> database)
Retrieves all class labels within the database.
|
static <O> KNNQuery<O> |
DatabaseUtil.precomputedKNNQuery(Database database,
Relation<O> relation,
DistanceFunction<? super O> distf,
int k)
Get (or create) a precomputed kNN query for the database.
|
static <O> KNNQuery<O> |
DatabaseUtil.precomputedKNNQuery(Database database,
Relation<O> relation,
DistanceQuery<O> dq,
int k)
Get (or create) a precomputed kNN query for the database.
|
| Modifier and Type | Method and Description |
|---|---|
Collection<? extends NumberVector> |
StarBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db) |
Collection<? extends NumberVector> |
ReferencePointsHeuristic.getReferencePoints(Relation<? extends NumberVector> db)
Get the reference points for the given database.
|
Collection<? extends NumberVector> |
RandomSampleReferencePoints.getReferencePoints(Relation<? extends NumberVector> db) |
Collection<? extends NumberVector> |
RandomGeneratedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db) |
Collection<? extends NumberVector> |
GridBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db) |
Collection<? extends NumberVector> |
FullDatabaseReferencePoints.getReferencePoints(Relation<? extends NumberVector> db) |
Collection<? extends NumberVector> |
AxisBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db) |
| Modifier and Type | Field and Description |
|---|---|
private Relation<?> |
VisualizerContext.relation
Relation currently visualized.
|
(package private) Relation<?> |
VisualizationTask.relation
The main representation
|
| Modifier and Type | Method and Description |
|---|---|
<R extends Relation<?>> |
VisualizationTask.getRelation() |
| Modifier and Type | Method and Description |
|---|---|
Relation<?> |
VisualizerContext.getRelation()
Current relation.
|
| Modifier and Type | Method and Description |
|---|---|
void |
VisualizerContext.setRelation(Relation<?> rel)
Set the current relation.
|
| Constructor and Description |
|---|
VisualizationTask(String name,
VisualizerContext context,
Object result,
Relation<?> relation,
VisFactory factory)
Visualization task.
|
VisualizerContext(ResultHierarchy hier,
Result start,
Relation<?> relation,
StyleLibrary stylelib,
Collection<VisualizationProcessor> factories)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<V> |
ScatterPlotProjector.rel
Relation we project.
|
(package private) Relation<V> |
ParallelPlotProjector.rel
Relation we project.
|
(package private) Relation<V> |
HistogramProjector.rel
Relation we project.
|
| Modifier and Type | Method and Description |
|---|---|
Relation<V> |
ScatterPlotProjector.getRelation()
The relation we project.
|
Relation<V> |
ParallelPlotProjector.getRelation()
The relation we project.
|
Relation<V> |
HistogramProjector.getRelation()
Get the relation we project.
|
| Modifier and Type | Method and Description |
|---|---|
private int |
ScatterPlotFactory.dimensionality(Relation<?> rel) |
private int |
ParallelPlotFactory.dimensionality(Relation<?> rel) |
| Constructor and Description |
|---|
HistogramProjector(Relation<V> rel,
int maxdim)
Constructor.
|
ParallelPlotProjector(Relation<V> rel)
Constructor.
|
ScatterPlotProjector(Relation<V> rel,
int maxdim)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<NV> |
ColoredHistogramVisualizer.Instance.relation
The database we visualize
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<NV> |
AbstractParallelVisualization.relation
The representation we visualize
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends NumberVector> |
AbstractScatterplotVisualization.rel
The representation we visualize
|
protected Relation<PolygonsObject> |
PolygonVisualization.Instance.rep
The representation we visualize
|
private Relation<?> |
TooltipStringVisualization.Instance.result
Number value to visualize
|
private Relation<? extends Number> |
TooltipScoreVisualization.Instance.result
Number value to visualize
|
| Modifier and Type | Method and Description |
|---|---|
static boolean |
TreeSphereVisualization.canVisualize(Relation<?> rel,
AbstractMTree<?,?,?,?> tree)
Test for a visualizable index in the context's database.
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<Vector> |
COPVectorVisualization.Instance.result
The outlier result to visualize
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends UncertainObject> |
UncertainSamplesVisualization.Instance.rel
The representation we visualize
|
protected Relation<? extends UncertainObject> |
UncertainBoundingBoxVisualization.Instance.rel
The representation we visualize
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.