| Modifier and Type | Class and Description |
|---|---|
class |
DependencyDerivator<V extends NumberVector>
Dependency derivator computes quantitatively linear dependencies among
attributes of a given dataset based on a linear correlation PCA.
|
static class |
DependencyDerivator.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
DummyAlgorithm<O extends NumberVector>
Dummy algorithm, which just iterates over all points once, doing a 10NN query
each.
|
class |
KNNJoin<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry>
Joins in a given spatial database to each object its k-nearest neighbors.
|
static class |
KNNJoin.Parameterizer<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry>
Parameterization class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
RangeQueryBenchmarkAlgorithm<O extends NumberVector>
Benchmarking algorithm that computes a range query for each point.
|
static class |
RangeQueryBenchmarkAlgorithm.Parameterizer<O extends NumberVector>
Parameterization class
|
| Modifier and Type | Method and Description |
|---|---|
Result |
RangeQueryBenchmarkAlgorithm.run(Database database,
Relation<O> relation,
Relation<NumberVector> radrel)
Run the algorithm, with separate radius relation
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractProjectedClustering<R extends Clustering<?>,V extends NumberVector>
|
class |
NaiveMeanShiftClustering<V extends NumberVector>
Mean-shift based clustering algorithm.
|
static class |
NaiveMeanShiftClustering.Parameterizer<V extends NumberVector>
Parameterizer.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractBiclustering<V extends NumberVector,M extends BiclusterModel>
Abstract class as a convenience for different biclustering approaches.
|
class |
ChengAndChurch<V extends NumberVector>
Perform Cheng and Church biclustering.
|
static class |
ChengAndChurch.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CASH<V extends NumberVector>
The CASH algorithm is a subspace clustering algorithm based on the Hough
transform.
|
class |
COPAC<V extends NumberVector>
COPAC is an algorithm to partition a database according to the correlation
dimension of its objects and to then perform an arbitrary clustering
algorithm over the partitions.
|
static class |
COPAC.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
ERiC<V extends NumberVector>
Performs correlation clustering on the data partitioned according to local
correlation dimensionality and builds a hierarchy of correlation clusters
that allows multiple inheritance from the clustering result.
|
static class |
ERiC.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
FourC<V extends NumberVector>
4C identifies local subgroups of data objects sharing a uniform correlation.
|
static class |
FourC.Parameterizer<O extends NumberVector>
Parameterization class.
|
class |
HiCO<V extends NumberVector>
Implementation of the HiCO algorithm, an algorithm for detecting hierarchies
of correlation clusters.
|
static class |
HiCO.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
ORCLUS<V extends NumberVector>
ORCLUS: Arbitrarily ORiented projected CLUSter generation.
|
static class |
ORCLUS.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) V |
ORCLUS.ORCLUSCluster.centroid
The centroid of this cluster.
|
| Modifier and Type | Field and Description |
|---|---|
private FilteredLocalPCAIndex<NumberVector> |
HiCO.Instance.index
Instantiated index.
|
private IndexFactory<V,FilteredLocalPCAIndex<NumberVector>> |
HiCO.indexfactory
Factory to produce
|
private IndexFactory<V,FilteredLocalPCAIndex<NumberVector>> |
HiCO.Parameterizer.indexfactory
Factory to produce
|
| Modifier and Type | Method and Description |
|---|---|
protected CASH<NumberVector> |
CASH.Parameterizer.makeInstance() |
| Modifier and Type | Method and Description |
|---|---|
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.
|
Clustering<Model> |
LMCLUS.run(Database database,
Relation<NumberVector> relation)
The main LMCLUS (Linear manifold clustering algorithm) is processed in this
method.
|
| Constructor and Description |
|---|
HiCO(IndexFactory<V,FilteredLocalPCAIndex<NumberVector>> indexfactory,
int mu,
double delta)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private NumberVector |
ParameterizationFunction.vec
The actual vector.
|
| Constructor and Description |
|---|
ParameterizationFunction(NumberVector vec)
Provides a new parameterization function describing all lines in a
d-dimensional feature space intersecting in one point p.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractEMModelFactory<V extends NumberVector,M extends MeanModel>
Abstract base class for initializing EM.
|
static class |
AbstractEMModelFactory.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
DiagonalGaussianModelFactory<V extends NumberVector>
Factory for EM with multivariate gaussian models using diagonal matrixes.
|
static class |
DiagonalGaussianModelFactory.Parameterizer<V extends NumberVector>
Parameterization class
|
class |
EM<V extends NumberVector,M extends MeanModel>
Clustering by expectation maximization (EM-Algorithm), also known as Gaussian
Mixture Modeling (GMM).
|
static class |
EM.Parameterizer<V extends NumberVector,M extends MeanModel>
Parameterization class.
|
interface |
EMClusterModelFactory<V extends NumberVector,M extends MeanModel>
Factory for initializing the EM models.
|
class |
MultivariateGaussianModelFactory<V extends NumberVector>
Factory for EM with multivariate gaussian models (with covariance; also known
as Gaussian Mixture Modeling, GMM).
|
static class |
MultivariateGaussianModelFactory.Parameterizer<V extends NumberVector>
Parameterization class
|
class |
SphericalGaussianModelFactory<V extends NumberVector>
Factory for EM with multivariate gaussian models using diagonal matrixes.
|
static class |
SphericalGaussianModelFactory.Parameterizer<V extends NumberVector>
Parameterization class
|
| Modifier and Type | Method and Description |
|---|---|
double |
SphericalGaussianModel.estimateDensity(NumberVector vec) |
double |
MultivariateGaussianModel.estimateDensity(NumberVector vec) |
double |
EMClusterModel.estimateDensity(NumberVector vec)
Estimate the likelihood of a vector.
|
double |
DiagonalGaussianModel.estimateDensity(NumberVector vec) |
double |
SphericalGaussianModel.mahalanobisDistance(NumberVector vec)
Compute the Mahalanobis distance from the centroid for a given vector.
|
double |
MultivariateGaussianModel.mahalanobisDistance(NumberVector vec)
Compute the Mahalanobis distance from the centroid for a given vector.
|
double |
DiagonalGaussianModel.mahalanobisDistance(NumberVector vec)
Compute the Mahalanobis distance from the centroid for a given vector.
|
void |
SphericalGaussianModel.updateE(NumberVector vec,
double wei) |
void |
MultivariateGaussianModel.updateE(NumberVector vec,
double wei) |
void |
EMClusterModel.updateE(NumberVector vec,
double weight)
Update the
|
void |
DiagonalGaussianModel.updateE(NumberVector vec,
double wei) |
| 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.
|
static void |
EM.recomputeCovarianceMatrices(Relation<? extends NumberVector> relation,
WritableDataStore<double[]> probClusterIGivenX,
List<? extends EMClusterModel<?>> models)
Recompute the covariance matrixes.
|
| Modifier and Type | Class and Description |
|---|---|
class |
COPACNeighborPredicate<V extends NumberVector>
COPAC neighborhood predicate.
|
static class |
COPACNeighborPredicate.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
ERiCNeighborPredicate<V extends NumberVector>
ERiC neighborhood predicate.
|
static class |
ERiCNeighborPredicate.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
FourCNeighborPredicate<V extends NumberVector>
4C identifies local subgroups of data objects sharing a uniform correlation.
|
static class |
FourCNeighborPredicate.Parameterizer<O extends NumberVector>
Parameterization class.
|
class |
LSDBC<O extends NumberVector>
Locally scaled Density Based Clustering.
|
static class |
LSDBC.Parameterizer<O extends NumberVector>
Parameterization class
|
class |
PreDeConNeighborPredicate<V extends NumberVector>
Neighborhood predicate used by PreDeCon.
|
static class |
PreDeConNeighborPredicate.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<? extends NumberVector> |
ERiCNeighborPredicate.Instance.relation
Vector data relation.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
ERiCNeighborPredicate.Instance.strongNeighbors(NumberVector v1,
NumberVector v2,
PCAFilteredResult pca1,
PCAFilteredResult pca2)
Computes the distance between two given DatabaseObjects according to this
distance function.
|
boolean |
ERiCNeighborPredicate.Instance.weakNeighbors(NumberVector v1,
NumberVector v2,
PCAFilteredResult pca1,
PCAFilteredResult pca2)
Computes the distance between two given DatabaseObjects according to this
distance function.
|
| Constructor and Description |
|---|
ERiCNeighborPredicate.Instance(DBIDs ids,
DataStore<PCAFilteredResult> storage,
Relation<? extends NumberVector> relation)
Constructor.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractKMeans<V extends NumberVector,M extends Model>
Abstract base class for k-means implementations.
|
static class |
AbstractKMeans.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel>
Run K-Means multiple times, and keep the best run.
|
static class |
BestOfMultipleKMeans.Parameterizer<V extends NumberVector,M extends MeanModel>
Parameterization class.
|
interface |
KMeans<V extends NumberVector,M extends Model>
Some constants and options shared among kmeans family algorithms.
|
class |
KMeansBatchedLloyd<V extends NumberVector>
An algorithm for k-means, using Lloyd-style bulk iterations.
|
static class |
KMeansBatchedLloyd.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
KMeansBisecting<V extends NumberVector,M extends MeanModel>
The bisecting k-means algorithm works by starting with an initial
partitioning into two clusters, then repeated splitting of the largest
cluster to get additional clusters.
|
static class |
KMeansBisecting.Parameterizer<V extends NumberVector,M extends MeanModel>
Parameterization class.
|
class |
KMeansElkan<V extends NumberVector>
Elkan's fast k-means by exploiting the triangle inequality.
|
static class |
KMeansElkan.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
KMeansHamerly<V extends NumberVector>
Hamerly's fast k-means by exploiting the triangle inequality.
|
static class |
KMeansHamerly.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
KMeansHybridLloydMacQueen<V extends NumberVector>
A hybrid k-means algorithm, alternating between MacQueen-style incremental
processing and Lloyd-Style batch steps.
|
static class |
KMeansHybridLloydMacQueen.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
KMeansLloyd<V extends NumberVector>
The standard k-means algorithm, using Lloyd-style bulk iterations.
|
static class |
KMeansLloyd.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
KMeansMacQueen<V extends NumberVector>
The original k-means algorithm, using MacQueen style incremental updates;
making this effectively an "online" (streaming) algorithm.
|
static class |
KMeansMacQueen.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
KMediansLloyd<V extends NumberVector>
k-medians clustering algorithm, but using Lloyd-style bulk iterations instead
of the more complicated approach suggested by Kaufman and Rousseeuw (see
KMedoidsPAM instead). |
static class |
KMediansLloyd.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
SingleAssignmentKMeans<V extends NumberVector>
Pseudo-k-Means variations, that assigns each object to the nearest center.
|
static class |
SingleAssignmentKMeans.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
XMeans<V extends NumberVector,M extends MeanModel>
X-means: Extending K-means with Efficient Estimation on the Number of
Clusters.
|
static class |
XMeans.Parameterizer<V extends NumberVector,M extends MeanModel>
Parameterization class.
|
| Modifier and Type | Method and Description |
|---|---|
protected List<? extends NumberVector> |
XMeans.splitCentroid(Cluster<? extends MeanModel> parentCluster,
Relation<V> relation)
Split an existing centroid into two initial centers.
|
| 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.
|
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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractKMeansInitialization<V extends NumberVector>
Abstract base class for common k-means initializations.
|
static class |
FirstKInitialMeans.Parameterizer<V extends NumberVector>
Parameterization class.
|
interface |
KMeansInitialization<V extends NumberVector>
Interface for initializing K-Means
|
class |
SampleKMeansInitialization<V extends NumberVector>
Initialize k-means by running k-means on a sample of the data set only.
|
static class |
SampleKMeansInitialization.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) List<? extends NumberVector> |
PredefinedInitialMeans.initialMeans
Initial means to return.
|
| 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 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> |
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> |
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> |
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> |
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> |
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> |
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) |
<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 | Method and Description |
|---|---|
void |
PredefinedInitialMeans.setInitialMeans(List<? extends NumberVector> initialMeans)
Set the initial means.
|
| Constructor and Description |
|---|
PredefinedInitialMeans(List<? extends NumberVector> initialMeans)
Constructor.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KMeansProcessor<V extends NumberVector>
Parallel k-means implementation.
|
static class |
KMeansProcessor.Instance<V extends NumberVector>
Instance to process part of the data set, for a single iteration.
|
class |
ParallelLloydKMeans<V extends NumberVector>
Parallel implementation of k-Means clustering.
|
static class |
ParallelLloydKMeans.Parameterizer<V extends NumberVector>
Parameterization class
|
| Constructor and Description |
|---|
KMeansProcessor.Instance(Relation<V> relation,
NumberVectorDistanceFunction<? super V> distance,
WritableIntegerDataStore assignment,
List<? extends NumberVector> means)
Constructor.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractKMeansQualityMeasure<O extends NumberVector>
Base class for evaluating clusterings by information criteria (such as AIC or
BIC).
|
interface |
KMeansQualityMeasure<O extends NumberVector>
Interface for computing the quality of a K-Means clustering.
|
| 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.
|
<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) |
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 |
|---|---|
static int |
AbstractKMeansQualityMeasure.numberOfFreeParameters(Relation<? extends NumberVector> relation,
Clustering<? extends MeanModel> clustering)
Compute the number of free parameters.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KNNKernelDensityMinimaClustering<V extends NumberVector>
Cluster one-dimensional data by splitting the data set on local minima after
performing kernel density estimation.
|
static class |
KNNKernelDensityMinimaClustering.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DeLiClu<NV extends NumberVector>
DeliClu: Density-Based Hierarchical Clustering, a hierarchical algorithm to
find density-connected sets in a database.
|
static class |
DeLiClu.Parameterizer<NV extends NumberVector>
Parameterization class.
|
class |
FastOPTICS<V extends NumberVector>
FastOPTICS algorithm (Fast approximation of OPTICS)
Note that this is not FOPTICS as in "Fuzzy OPTICS"!
|
static class |
FastOPTICS.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CLIQUE<V extends NumberVector>
Implementation of the CLIQUE algorithm, a grid-based algorithm to identify
dense clusters in subspaces of maximum dimensionality.
|
static class |
CLIQUE.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
DiSH<V extends NumberVector>
Algorithm for detecting subspace hierarchies.
|
static class |
DiSH.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
DOC<V extends NumberVector>
The DOC algorithm, and it's heuristic variant, FastDOC.
|
static class |
DOC.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
HiSC<V extends NumberVector>
Implementation of the HiSC algorithm, an algorithm for detecting hierarchies
of subspace clusters.
|
static class |
HiSC.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
P3C<V extends NumberVector>
P3C: A Robust Projected Clustering Algorithm.
|
static class |
P3C.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
PreDeCon<V extends NumberVector>
PreDeCon computes clusters of subspace preference weighted connected points.
|
static class |
PreDeCon.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
PROCLUS<V extends NumberVector>
The PROCLUS algorithm, an algorithm to find subspace clusters in high
dimensional spaces.
|
static class |
PROCLUS.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
SUBCLU<V extends NumberVector>
Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily
shaped and positioned clusters in subspaces.
|
static class |
SUBCLU.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Field and Description |
|---|---|
private HiSCPreferenceVectorIndex<NumberVector> |
HiSC.Instance.index
Instantiated index.
|
private IndexFactory<V,HiSCPreferenceVectorIndex<NumberVector>> |
HiSC.indexfactory
Factory to produce
|
private IndexFactory<V,HiSCPreferenceVectorIndex<NumberVector>> |
HiSC.Parameterizer.indexfactory
Factory to produce the index.
|
| Modifier and Type | Method and Description |
|---|---|
private double |
PROCLUS.manhattanSegmentalDistance(NumberVector o1,
NumberVector o2,
long[] dimensions)
Returns the Manhattan segmental distance between o1 and o2 relative to the
specified dimensions.
|
private int |
DiSH.subspaceDimensionality(NumberVector v1,
NumberVector v2,
long[] pv1,
long[] pv2,
long[] commonPreferenceVector)
Compute the common subspace dimensionality of two vectors.
|
protected static double |
DiSH.weightedDistance(NumberVector v1,
NumberVector v2,
long[] weightVector)
Computes the weighted distance between the two specified vectors according
to the given preference vector.
|
| Constructor and Description |
|---|
HiSC(IndexFactory<V,HiSCPreferenceVectorIndex<NumberVector>> indexfactory,
double epsilon)
Constructor.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CLIQUESubspace<V extends NumberVector>
Represents a subspace of the original data space in the CLIQUE algorithm.
|
class |
CLIQUEUnit<V extends NumberVector>
Represents a unit in the CLIQUE algorithm.
|
| Modifier and Type | Method and Description |
|---|---|
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.
|
| Constructor and Description |
|---|
CKMeans(NumberVectorDistanceFunction<? super NumberVector> distanceFunction,
int k,
int maxiter,
KMeansInitialization<? super NumberVector> initializer)
Constructor that uses Lloyd's k-means algorithm.
|
CKMeans(NumberVectorDistanceFunction<? super NumberVector> distanceFunction,
int k,
int maxiter,
KMeansInitialization<? super NumberVector> initializer)
Constructor that uses Lloyd's k-means algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
COP<V extends NumberVector>
Correlation outlier probability: Outlier Detection in Arbitrarily Oriented
Subspaces
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
Outlier Detection in Arbitrarily Oriented Subspaces in: Proc. |
static class |
COP.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
GaussianModel<V extends NumberVector>
Outlier have smallest GMOD_PROB: the outlier scores is the
probability density of the assumed distribution.
|
static class |
GaussianModel.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
GaussianUniformMixture<V extends NumberVector>
Outlier detection algorithm using a mixture model approach.
|
static class |
GaussianUniformMixture.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
SimpleCOP<V extends NumberVector>
Algorithm to compute local correlation outlier probability.
|
static class |
SimpleCOP.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ABOD<V extends NumberVector>
Angle-Based Outlier Detection / Angle-Based Outlier Factor.
|
static class |
ABOD.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
FastABOD<V extends NumberVector>
Angle-Based Outlier Detection / Angle-Based Outlier Factor.
|
static class |
FastABOD.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
LBABOD<V extends NumberVector>
Angle-Based Outlier Detection / Angle-Based Outlier Factor.
|
static class |
LBABOD.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
EMOutlier<V extends NumberVector>
outlier detection algorithm using EM Clustering.
|
static class |
EMOutlier.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
KMeansOutlierDetection<O extends NumberVector>
Outlier detection by using k-means clustering.
|
static class |
KMeansOutlierDetection.Parameterizer<O extends NumberVector>
Parameterizer.
|
| Modifier and Type | Class and Description |
|---|---|
class |
HilOut<O extends NumberVector>
Fast Outlier Detection in High Dimensional Spaces
Outlier Detection using Hilbert space filling curves
Reference:
F.
|
static class |
HilOut.Parameterizer<O extends NumberVector>
Parameterization class
|
| 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.
|
private double |
HilOut.HilbertFeatures.getDimForObject(NumberVector obj,
int dim)
Get the (projected) position of the object in dimension dim.
|
| 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 DoubleDBIDList |
ReferenceBasedOutlierDetection.computeDistanceVector(NumberVector refPoint,
Relation<? extends NumberVector> database,
PrimitiveDistanceQuery<? super NumberVector> distFunc)
Computes for each object the distance to one reference point.
|
OutlierResult |
ReferenceBasedOutlierDetection.run(Database database,
Relation<? extends NumberVector> relation)
Run the algorithm on the given relation.
|
| Constructor and Description |
|---|
ReferenceBasedOutlierDetection(int k,
NumberVectorDistanceFunction<? super NumberVector> distanceFunction,
ReferencePointsHeuristic refp)
Constructor with parameters.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ALOCI<O extends NumberVector>
Fast Outlier Detection Using the "approximate Local Correlation Integral".
|
static class |
ALOCI.Parameterizer<O extends NumberVector>
Parameterization class.
|
class |
LDF<O extends NumberVector>
Outlier Detection with Kernel Density Functions.
|
static class |
LDF.Parameterizer<O extends NumberVector>
Parameterization class.
|
class |
SimpleKernelDensityLOF<O extends NumberVector>
A simple variant of the LOF algorithm, which uses a simple kernel density
estimation instead of the local reachability density.
|
static class |
SimpleKernelDensityLOF.Parameterizer<O extends NumberVector>
Parameterization class.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<? extends NumberVector> |
ALOCI.ALOCIQuadTree.relation
Relation indexed.
|
| Modifier and Type | Method and Description |
|---|---|
ALOCI.Node |
ALOCI.ALOCIQuadTree.findClosestNode(NumberVector vec,
int tlevel)
Find the closest node (of depth tlevel or above, if there is no node at
this depth) for the given vector.
|
private double |
ALOCI.ALOCIQuadTree.getShiftedDim(NumberVector obj,
int dim,
int level)
Shift and wrap a single dimension.
|
| Constructor and Description |
|---|
ALOCI.ALOCIQuadTree(double[] min,
double[] max,
double[] shift,
int nmin,
Relation<? extends NumberVector> relation)
Constructor.
|
| Modifier and Type | Class and Description |
|---|---|
class |
HiCS<V extends NumberVector>
Algorithm to compute High Contrast Subspaces for Density-Based Outlier
Ranking.
|
static class |
HiCS.Parameterizer<V extends NumberVector>
Parameterization class.
|
| 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 |
FeatureBagging.run(Database database,
Relation<NumberVector> relation)
Run the algorithm on a data set.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CTLuGLSBackwardSearchAlgorithm<V extends NumberVector>
GLS-Backward Search is a statistical approach to detecting spatial outliers.
|
static class |
CTLuGLSBackwardSearchAlgorithm.Parameterizer<V extends NumberVector>
Parameterization class
|
class |
CTLuMeanMultipleAttributes<N,O extends NumberVector>
Mean Approach is used to discover spatial outliers with multiple attributes.
|
static class |
CTLuMeanMultipleAttributes.Parameterizer<N,O extends NumberVector>
Parameterization class.
|
class |
CTLuMedianMultipleAttributes<N,O extends NumberVector>
Median Approach is used to discover spatial outliers with multiple
attributes.
|
static class |
CTLuMedianMultipleAttributes.Parameterizer<N,O extends NumberVector>
Parameterization class.
|
| Modifier and Type | Method and Description |
|---|---|
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 |
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 |
CTLuMedianAlgorithm.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
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.
|
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 | Class and Description |
|---|---|
class |
AbstractAggarwalYuOutlier<V extends NumberVector>
Abstract base class for the sparse-grid-cell based outlier detection of
Aggarwal and Yu.
|
class |
AggarwalYuEvolutionary<V extends NumberVector>
Evolutionary variant (EAFOD) of the high-dimensional outlier detection
algorithm by Aggarwal and Yu.
|
static class |
AggarwalYuEvolutionary.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
AggarwalYuNaive<V extends NumberVector>
BruteForce variant of the high-dimensional outlier detection algorithm by
Aggarwal and Yu.
|
static class |
AggarwalYuNaive.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
OUTRES<V extends NumberVector>
Adaptive outlierness for subspace outlier ranking (OUTRES).
|
static class |
OUTRES.Parameterizer<O extends NumberVector>
Parameterization class.
|
class |
SOD<V extends NumberVector>
Subspace Outlier Degree.
|
static class |
SOD.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Method and Description |
|---|---|
private static double[] |
SOD.computePerDimensionVariances(Relation<? extends NumberVector> relation,
Vector center,
DBIDs neighborhood)
Compute the per-dimension variances for the given neighborhood and center.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LibSVMOneClassOutlierDetection<V extends NumberVector>
Outlier-detection using one-class support vector machines.
|
static class |
LibSVMOneClassOutlierDetection.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Method and Description |
|---|---|
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
|
| Modifier and Type | Class and Description |
|---|---|
class |
EvaluateRankingQuality<V extends NumberVector>
Evaluate a distance function with respect to kNN queries.
|
static class |
EvaluateRankingQuality.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
RangeQuerySelectivity<V extends NumberVector>
Evaluate the range query selectivity.
|
static class |
RangeQuerySelectivity.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Method and Description |
|---|---|
protected double |
HopkinsStatisticClusteringTendency.computeNNForRealData(KNNQuery<NumberVector> knnQuery,
Relation<NumberVector> relation,
int dim)
Search nearest neighbors for real data members.
|
protected double |
HopkinsStatisticClusteringTendency.computeNNForRealData(KNNQuery<NumberVector> knnQuery,
Relation<NumberVector> relation,
int dim)
Search nearest neighbors for real data members.
|
protected double |
HopkinsStatisticClusteringTendency.computeNNForUniformData(KNNQuery<NumberVector> knnQuery,
double[] min,
double[] extend)
Search nearest neighbors for artificial, uniform data.
|
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.
|
private ScalesResult |
AddSingleScale.run(Relation<? extends NumberVector> rel)
Add scales to a single vector relation.
|
| Constructor and Description |
|---|
HopkinsStatisticClusteringTendency(NumberVectorDistanceFunction<? super NumberVector> distanceFunction,
int samplesize,
RandomFactory random,
int rep,
int k,
double[] minima,
double[] maxima)
Constructor.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ComputeKNNOutlierScores<O extends NumberVector>
Application that runs a series of kNN-based algorithms on a data set, for
building an ensemble in a second step.
|
static class |
ComputeKNNOutlierScores.Parameterizer<O extends NumberVector>
Parameterization class.
|
| 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. |
private PrimitiveDistanceFunction<NumberVector> |
GreedyEnsembleExperiment.getDistanceFunction(double[] estimated_weights) |
| Modifier and Type | Method and Description |
|---|---|
private boolean |
EvaluatePrecomputedOutlierScores.checkForNaNs(NumberVector vec)
Check for NaN values.
|
protected void |
GreedyEnsembleExperiment.singleEnsemble(double[] ensemble,
NumberVector vec)
Build a single-element "ensemble".
|
| 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 | Interface and Description |
|---|---|
static interface |
NumberVector.Factory<V extends NumberVector>
Factory API for this feature vector.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
SparseNumberVector
Combines the SparseFeatureVector and NumberVector.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractNumberVector
AbstractNumberVector is an abstract implementation of FeatureVector.
|
class |
BitVector
Vector using a dense bit set encoding, based on
long[] storage. |
class |
ByteVector
Vector using
byte[] storage. |
class |
DoubleVector
Vector type using
double[] storage for real numbers. |
class |
FloatVector
Vector type using
float[] storage, thus needing approximately half as
much memory as DoubleVector. |
class |
IntegerVector
Vector type using
int[] storage. |
class |
OneDimensionalDoubleVector
Specialized class implementing a one-dimensional double vector without using
an array.
|
class |
ShortVector
Vector type using
short[] storage. |
class |
SparseByteVector
Sparse vector type, using
byte[] for storing the values, and
int[] for storing the indexes, approximately 5 bytes per non-zero
value (limited to -128..+127). |
class |
SparseDoubleVector
Sparse vector type, using
double[] for storing the values, and
int[] for storing the indexes, approximately 12 bytes per non-zero
value. |
class |
SparseFloatVector
Sparse vector type, using
float[] for storing the values, and
int[] for storing the indexes, approximately 8 bytes per non-zero
value. |
class |
SparseIntegerVector
Sparse vector type, using
int[] for storing the values, and
int[] for storing the indexes, approximately 8 bytes per non-zero
integer value. |
class |
SparseShortVector
Sparse vector type, using
short[] for storing the values, and
int[] for storing the indexes, approximately 6 bytes per non-zero
value. |
| Modifier and Type | Field and Description |
|---|---|
private Relation<? extends NumberVector> |
VectorUtil.SortDBIDsBySingleDimension.data
The relation to sort.
|
| Modifier and Type | Method and Description |
|---|---|
static <V extends NumberVector> |
VectorUtil.project(V v,
long[] selectedAttributes,
NumberVector.Factory<V> factory)
Project a number vector to the specified attributes.
|
static <V extends NumberVector> |
VectorUtil.randomVector(NumberVector.Factory<V> factory,
int dim)
Produce a new vector based on random numbers in [0:1].
|
static <V extends NumberVector> |
VectorUtil.randomVector(NumberVector.Factory<V> factory,
int dim,
Random r)
Produce a new vector based on random numbers in [0:1].
|
| Modifier and Type | Method and Description |
|---|---|
static double |
VectorUtil.angle(NumberVector v1,
NumberVector v2,
NumberVector o)
Compute the angle between two vectors.
|
static double |
VectorUtil.angle(NumberVector v1,
NumberVector v2,
Vector o)
Compute the angle between two vectors.
|
int |
VectorUtil.SortVectorsBySingleDimension.compare(NumberVector o1,
NumberVector o2) |
static double |
VectorUtil.cosAngle(NumberVector v1,
NumberVector v2)
Compute the absolute cosine of the angle between two vectors.
|
static double[] |
VectorUtil.fastTimes(Matrix mat,
NumberVector v)
This is an ugly hack, but we don't want to have the
Matrix class
depend on NumberVector. |
V |
NumberVector.Factory.newNumberVector(NumberVector values)
Returns a new NumberVector of N for the given values.
|
V |
AbstractNumberVector.Factory.newNumberVector(NumberVector values) |
static double |
VectorUtil.scalarProduct(NumberVector d1,
NumberVector d2)
Compute the scalar product (inner product) of this and the given
DoubleVector.
|
| 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 | Class and Description |
|---|---|
class |
CorrelationAnalysisSolution<V extends NumberVector>
A solution of correlation analysis is a matrix of equations describing the
dependencies.
|
| Modifier and Type | Method and Description |
|---|---|
static <V extends NumberVector> |
ModelUtil.getPrototype(Model model,
Relation<? extends V> relation,
NumberVector.Factory<V> factory)
Get (and convert!)
|
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.
|
| 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 NumberVector |
ModelUtil.getPrototypeOrCentroid(Model model,
Relation<? extends NumberVector> relation,
DBIDs ids)
Get the representative vector for a cluster model, or compute the centroid.
|
| 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 NumberVector |
ModelUtil.getPrototypeOrCentroid(Model model,
Relation<? extends NumberVector> relation,
DBIDs ids)
Get the representative vector for a cluster model, or compute the centroid.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LatLngToECEFProjection<V extends NumberVector>
Project (Latitude, Longitude) vectors to (X, Y, Z), from spherical
coordinates to ECEF (earth-centered earth-fixed).
|
class |
LngLatToECEFProjection<V extends NumberVector>
Project (Longitude, Latitude) vectors to (X, Y, Z), from spherical
coordinates to ECEF (earth-centered earth-fixed).
|
class |
NumericalFeatureSelection<V extends NumberVector>
Projection class for number vectors.
|
static class |
NumericalFeatureSelection.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
RandomProjection<V extends NumberVector>
Randomized projections of the data.
|
| Modifier and Type | Method and Description |
|---|---|
protected RandomProjection<NumberVector> |
RandomProjection.Parameterizer.makeInstance() |
protected LngLatToECEFProjection<NumberVector> |
LngLatToECEFProjection.Parameterizer.makeInstance() |
protected LatLngToECEFProjection<NumberVector> |
LatLngToECEFProjection.Parameterizer.makeInstance() |
| Modifier and Type | Field and Description |
|---|---|
static MultivariateSeriesTypeInformation<NumberVector> |
TypeUtil.MULTIVARIATE_SERIES
Type request for multivariate time series.
|
static VectorFieldTypeInformation<NumberVector> |
TypeUtil.NUMBER_VECTOR_FIELD
Input type for algorithms that require number vector fields.
|
static VectorFieldTypeInformation<? super NumberVector> |
TypeUtil.NUMBER_VECTOR_FIELD_1D
Type request for two-dimensional number vectors
|
static VectorFieldTypeInformation<? super NumberVector> |
TypeUtil.NUMBER_VECTOR_FIELD_2D
Type request for two-dimensional number vectors
|
static VectorTypeInformation<NumberVector> |
TypeUtil.NUMBER_VECTOR_VARIABLE_LENGTH
Number vectors of variable length.
|
| Modifier and Type | Method and Description |
|---|---|
protected static HyperBoundingBox |
AbstractUncertainObject.computeBounds(NumberVector[] samples)
Compute the bounding box for some samples.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LinearScanEuclideanDistanceKNNQuery<O extends NumberVector>
Instance of this query for a particular database.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LinearScanEuclideanDistanceRangeQuery<O extends NumberVector>
Optimized linear scan for Euclidean distance range queries.
|
| Modifier and Type | Method and Description |
|---|---|
static <V extends NumberVector> |
RelationUtil.getNumberVectorFactory(Relation<V> relation)
Get the number vector factory of a database relation.
|
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 <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 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.
|
| Modifier and Type | Method and Description |
|---|---|
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 double[][] |
RelationUtil.relationAsMatrix(Relation<? extends NumberVector> relation,
ArrayDBIDs ids)
Copy a relation into a double matrix.
|
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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractVectorConversionFilter<I,O extends NumberVector>
Abstract class for filters that produce number vectors.
|
class |
AbstractVectorStreamConversionFilter<I,O extends NumberVector>
Abstract base class for streaming filters that produce vectors.
|
| Modifier and Type | Method and Description |
|---|---|
static <V extends NumberVector> |
FilterUtil.guessFactory(SimpleTypeInformation<V> in)
Try to guess the appropriate factory.
|
| Modifier and Type | Class and Description |
|---|---|
class |
VectorDimensionalityFilter<V extends NumberVector>
Filter to remove all vectors that do not have the desired dimensionality.
|
static class |
VectorDimensionalityFilter.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractNormalization<V extends NumberVector>
Abstract super class for all normalizations.
|
class |
AbstractStreamNormalization<V extends NumberVector>
Abstract super class for all normalizations.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AttributeWiseBetaNormalization<V extends NumberVector>
Project the data using a Beta distribution.
|
static class |
AttributeWiseBetaNormalization.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
AttributeWiseCDFNormalization<V extends NumberVector>
Class to perform and undo a normalization on real vectors by estimating the
distribution of values along each dimension independently, then rescaling
objects to the cumulative density function (CDF) value at the original
coordinate.
|
static class |
AttributeWiseCDFNormalization.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
AttributeWiseErfNormalization<V extends NumberVector>
Attribute-wise Normalization using the error function.
|
class |
AttributeWiseMADNormalization<V extends NumberVector>
Median Absolute Deviation is used for scaling the data set as follows:
First, the median, and median absolute deviation are computed in each axis.
|
class |
AttributeWiseMeanNormalization<V extends NumberVector>
Normalization designed for data with a meaningful zero: Each
attribute is scaled to have the same mean (but 0 is not changed).
|
class |
AttributeWiseMinMaxNormalization<V extends NumberVector>
Class to perform and undo a normalization on real vectors with respect to
given minimum and maximum in each dimension.
|
static class |
AttributeWiseMinMaxNormalization.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
AttributeWiseVarianceNormalization<V extends NumberVector>
Class to perform and undo a normalization on real vectors with respect to
given mean and standard deviation in each dimension.
|
static class |
AttributeWiseVarianceNormalization.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) List<? extends NumberVector> |
IntegerRankTieNormalization.Sorter.col
Column to use for sorting.
|
| Modifier and Type | Method and Description |
|---|---|
Double |
AttributeWiseCDFNormalization.Adapter.get(List<? extends NumberVector> array,
int off) |
Double |
AttributeWiseBetaNormalization.Adapter.get(List<? extends NumberVector> array,
int off) |
byte |
AttributeWiseCDFNormalization.Adapter.getByte(List<? extends NumberVector> array,
int off) |
byte |
AttributeWiseBetaNormalization.Adapter.getByte(List<? extends NumberVector> array,
int off) |
double |
AttributeWiseCDFNormalization.Adapter.getDouble(List<? extends NumberVector> array,
int off) |
double |
AttributeWiseBetaNormalization.Adapter.getDouble(List<? extends NumberVector> array,
int off) |
float |
AttributeWiseCDFNormalization.Adapter.getFloat(List<? extends NumberVector> array,
int off) |
float |
AttributeWiseBetaNormalization.Adapter.getFloat(List<? extends NumberVector> array,
int off) |
int |
AttributeWiseCDFNormalization.Adapter.getInteger(List<? extends NumberVector> array,
int off) |
int |
AttributeWiseBetaNormalization.Adapter.getInteger(List<? extends NumberVector> array,
int off) |
long |
AttributeWiseCDFNormalization.Adapter.getLong(List<? extends NumberVector> array,
int off) |
long |
AttributeWiseBetaNormalization.Adapter.getLong(List<? extends NumberVector> array,
int off) |
short |
AttributeWiseCDFNormalization.Adapter.getShort(List<? extends NumberVector> array,
int off) |
short |
AttributeWiseBetaNormalization.Adapter.getShort(List<? extends NumberVector> array,
int off) |
void |
IntegerRankTieNormalization.Sorter.setup(List<? extends NumberVector> col,
int dim)
Configure the sorting class.
|
int |
AttributeWiseCDFNormalization.Adapter.size(List<? extends NumberVector> array) |
int |
AttributeWiseBetaNormalization.Adapter.size(List<? extends NumberVector> array) |
| Modifier and Type | Class and Description |
|---|---|
class |
HellingerHistogramNormalization<V extends NumberVector>
Normalize histograms by scaling them to L1 norm 1, then taking the square
root in each attribute.
|
class |
InstanceLogRankNormalization<V extends NumberVector>
Normalize vectors such that the smallest value of each instance is 0, the
largest is 1, but using log_2(1+x).
|
class |
InstanceMeanVarianceNormalization<V extends NumberVector>
Normalize vectors such that they have zero mean and unit variance.
|
static class |
InstanceMeanVarianceNormalization.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
InstanceMinMaxNormalization<V extends NumberVector>
Normalize vectors such that the smallest attribute is 0, the largest is 1.
|
static class |
InstanceMinMaxNormalization.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
InstanceRankNormalization<V extends NumberVector>
Normalize vectors such that the smallest value of each instance is 0, the
largest is 1.
|
class |
LengthNormalization<V extends NumberVector>
Class to perform a normalization on vectors to norm 1.
|
static class |
LengthNormalization.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
Log1PlusNormalization<V extends NumberVector>
Normalize the data set by applying log(1+|x|*b)/log(b+1) to any value.
|
static class |
Log1PlusNormalization.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Field and Description |
|---|---|
static Log1PlusNormalization<NumberVector> |
Log1PlusNormalization.STATIC
Static instance.
|
static HellingerHistogramNormalization<NumberVector> |
HellingerHistogramNormalization.STATIC
Static instance.
|
| Modifier and Type | Method and Description |
|---|---|
protected InstanceRankNormalization<NumberVector> |
InstanceRankNormalization.Parameterizer.makeInstance() |
protected InstanceLogRankNormalization<NumberVector> |
InstanceLogRankNormalization.Parameterizer.makeInstance() |
protected HellingerHistogramNormalization<NumberVector> |
HellingerHistogramNormalization.Parameterizer.makeInstance() |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractSupervisedProjectionVectorFilter<V extends NumberVector>
Base class for supervised projection methods.
|
static class |
AbstractSupervisedProjectionVectorFilter.Parameterizer<V extends NumberVector>
Parameterization class.
|
static class |
ClassicMultidimensionalScalingTransform.Parameterizer<O extends NumberVector>
Parameterization class.
|
static class |
FastMultidimensionalScalingTransform.Parameterizer<O extends NumberVector>
Parameterization class.
|
class |
GlobalPrincipalComponentAnalysisTransform<O extends NumberVector>
Apply principal component analysis to the data set.
|
static class |
GlobalPrincipalComponentAnalysisTransform.Parameterizer<O extends NumberVector>
Parameterization class.
|
class |
HistogramJitterFilter<V extends NumberVector>
Add Jitter, preserving the histogram properties (same sum, nonnegative).
|
class |
LatLngToECEFFilter<V extends NumberVector>
Project a 2D data set (latitude, longitude) to a 3D coordinate system (X, Y,
Z), such that Euclidean distance is line-of-sight.
|
static class |
LatLngToECEFFilter.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
LinearDiscriminantAnalysisFilter<V extends NumberVector>
Linear Discriminant Analysis (LDA) / Fisher's linear discriminant.
|
static class |
LinearDiscriminantAnalysisFilter.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
LngLatToECEFFilter<V extends NumberVector>
Project a 2D data set (longitude, latitude) to a 3D coordinate system (X, Y,
Z), such that Euclidean distance is line-of-sight.
|
static class |
LngLatToECEFFilter.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
NumberVectorFeatureSelectionFilter<V extends NumberVector>
Parser to project the ParsingResult obtained by a suitable base parser onto a
selected subset of attributes.
|
class |
NumberVectorRandomFeatureSelectionFilter<V extends NumberVector>
Parser to project the ParsingResult obtained by a suitable base parser onto a
randomly selected subset of attributes.
|
class |
PerturbationFilter<V extends NumberVector>
A filter to perturb the values by adding micro-noise.
|
static class |
PerturbationFilter.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SplitNumberVectorFilter<V extends NumberVector>
Split an existing column into two types.
|
static class |
SplitNumberVectorFilter.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Method and Description |
|---|---|
protected SimpleTypeInformation<? super NumberVector> |
WeightedUncertainSplitFilter.getInputTypeRestriction() |
protected SimpleTypeInformation<? super NumberVector> |
UncertainSplitFilter.getInputTypeRestriction() |
| Modifier and Type | Method and Description |
|---|---|
protected WeightedDiscreteUncertainObject |
WeightedUncertainSplitFilter.filterSingleObject(NumberVector vec) |
protected UnweightedDiscreteUncertainObject |
UncertainSplitFilter.filterSingleObject(NumberVector vec) |
| Modifier and Type | Method and Description |
|---|---|
protected SimpleTypeInformation<WeightedDiscreteUncertainObject> |
WeightedUncertainSplitFilter.convertedType(SimpleTypeInformation<NumberVector> in) |
protected SimpleTypeInformation<UnweightedDiscreteUncertainObject> |
UncertainSplitFilter.convertedType(SimpleTypeInformation<NumberVector> in) |
| Modifier and Type | Class and Description |
|---|---|
class |
CategorialDataAsNumberVectorParser<V extends NumberVector>
A very simple parser for categorial data, which will then be encoded as
numbers.
|
static class |
CategorialDataAsNumberVectorParser.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
NumberVectorLabelParser<V extends NumberVector>
Parser for a simple CSV type of format, with columns separated by the given
pattern (default: whitespace).
|
static class |
NumberVectorLabelParser.Parameterizer<V extends NumberVector>
Parameterization class.
|
| Modifier and Type | Field and Description |
|---|---|
protected V |
NumberVectorLabelParser.curvec
Current vector.
|
| Modifier and Type | Method and Description |
|---|---|
<T extends NumberVector> |
AbstractSpatialNorm.instantiate(Relation<T> relation) |
<T extends NumberVector> |
AbstractSpatialDistanceFunction.instantiate(Relation<T> relation) |
| Modifier and Type | Method and Description |
|---|---|
VectorFieldTypeInformation<? super NumberVector> |
MatrixWeightedDistanceFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super NumberVector> |
CosineDistanceFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super NumberVector> |
ClarkDistanceFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super NumberVector> |
CanberraDistanceFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super NumberVector> |
ArcCosineDistanceFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super NumberVector> |
AbstractNumberVectorDistanceFunction.getInputTypeRestriction() |
| Modifier and Type | Method and Description |
|---|---|
static int |
AbstractNumberVectorDistanceFunction.dimensionality(NumberVector o1,
NumberVector o2)
Get the common dimensionality of the two objects.
|
static int |
AbstractNumberVectorDistanceFunction.dimensionality(NumberVector o1,
NumberVector o2,
int expect)
Get the common dimensionality of the two objects.
|
double |
WeightedCanberraDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
NumberVectorDistanceFunction.distance(NumberVector o1,
NumberVector o2)
Computes the distance between two given vectors according to this distance
function.
|
double |
MatrixWeightedDistanceFunction.distance(NumberVector o1,
NumberVector o2) |
double |
LorentzianDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
Kulczynski1DistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
CosineDistanceFunction.distance(NumberVector v1,
NumberVector v2)
Computes the cosine distance for two given feature vectors.
|
double |
ClarkDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
CanberraDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
BrayCurtisDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
ArcCosineDistanceFunction.distance(NumberVector v1,
NumberVector v2)
Computes the cosine distance for two given feature vectors.
|
double |
LorentzianDistanceFunction.norm(NumberVector v1) |
| Modifier and Type | Method and Description |
|---|---|
double |
HistogramIntersectionDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
| Modifier and Type | Method and Description |
|---|---|
double |
WeightedSquaredPearsonCorrelationDistanceFunction.distance(NumberVector v1,
NumberVector v2)
Computes the squared Pearson correlation distance for two given feature
vectors.
|
double |
WeightedPearsonCorrelationDistanceFunction.distance(NumberVector v1,
NumberVector v2)
Computes the Pearson correlation distance for two given feature vectors.
|
double |
UncenteredCorrelationDistanceFunction.distance(NumberVector v1,
NumberVector v2)
Computes the Pearson correlation distance for two given feature vectors.
|
double |
SquaredUncenteredCorrelationDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
SquaredPearsonCorrelationDistanceFunction.distance(NumberVector v1,
NumberVector v2)
Computes the squared Pearson correlation distance for two given feature
vectors.
|
double |
PearsonCorrelationDistanceFunction.distance(NumberVector v1,
NumberVector v2)
Computes the Pearson correlation distance for two given feature vectors.
|
double |
AbsoluteUncenteredCorrelationDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
AbsolutePearsonCorrelationDistanceFunction.distance(NumberVector v1,
NumberVector v2)
Computes the absolute Pearson correlation distance for two given feature
vectors.
|
static double |
UncenteredCorrelationDistanceFunction.uncenteredCorrelation(NumberVector x,
NumberVector y)
Compute the uncentered correlation of two vectors.
|
| Modifier and Type | Method and Description |
|---|---|
SimpleTypeInformation<? super NumberVector> |
LngLatDistanceFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super NumberVector> |
LatLngDistanceFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super NumberVector> |
DimensionSelectingLatLngDistanceFunction.getInputTypeRestriction() |
| Modifier and Type | Method and Description |
|---|---|
double |
LngLatDistanceFunction.distance(NumberVector o1,
NumberVector o2) |
double |
LatLngDistanceFunction.distance(NumberVector o1,
NumberVector o2) |
double |
DimensionSelectingLatLngDistanceFunction.distance(NumberVector o1,
NumberVector o2) |
| Modifier and Type | Method and Description |
|---|---|
double |
KolmogorovSmirnovDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
HistogramMatchDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
| Modifier and Type | Method and Description |
|---|---|
SimpleTypeInformation<? super NumberVector> |
WeightedLPNormDistanceFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super NumberVector> |
SquaredEuclideanDistanceFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super NumberVector> |
LPNormDistanceFunction.getInputTypeRestriction() |
| Modifier and Type | Method and Description |
|---|---|
double |
WeightedSquaredEuclideanDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
WeightedMaximumDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
WeightedManhattanDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
WeightedLPNormDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
WeightedEuclideanDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
SquaredEuclideanDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
MinimumDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
MaximumDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
ManhattanDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
LPNormDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
LPIntegerNormDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
EuclideanDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
WeightedSquaredEuclideanDistanceFunction.norm(NumberVector obj) |
double |
WeightedMaximumDistanceFunction.norm(NumberVector v) |
double |
WeightedManhattanDistanceFunction.norm(NumberVector v) |
double |
WeightedLPNormDistanceFunction.norm(NumberVector v) |
double |
WeightedEuclideanDistanceFunction.norm(NumberVector v) |
double |
SquaredEuclideanDistanceFunction.norm(NumberVector v) |
double |
MinimumDistanceFunction.norm(NumberVector v) |
double |
MaximumDistanceFunction.norm(NumberVector v) |
double |
ManhattanDistanceFunction.norm(NumberVector v) |
double |
LPNormDistanceFunction.norm(NumberVector v) |
double |
LPIntegerNormDistanceFunction.norm(NumberVector v) |
double |
EuclideanDistanceFunction.norm(NumberVector v) |
private double |
WeightedMaximumDistanceFunction.preDistance(NumberVector v1,
NumberVector v2,
int start,
int end,
double agg) |
private double |
WeightedManhattanDistanceFunction.preDistance(NumberVector v1,
NumberVector v2,
int start,
int end,
double agg) |
private double |
WeightedLPNormDistanceFunction.preDistance(NumberVector v1,
NumberVector v2,
int start,
int end,
double agg) |
private double |
WeightedEuclideanDistanceFunction.preDistance(NumberVector v1,
NumberVector v2,
int start,
int end,
double agg) |
private double |
SquaredEuclideanDistanceFunction.preDistance(NumberVector v1,
NumberVector v2,
int start,
int end,
double agg) |
private double |
MaximumDistanceFunction.preDistance(NumberVector v1,
NumberVector v2,
int start,
int end,
double agg) |
private double |
ManhattanDistanceFunction.preDistance(NumberVector v1,
NumberVector v2,
int start,
int end,
double agg) |
private double |
LPNormDistanceFunction.preDistance(NumberVector v1,
NumberVector v2,
int start,
int end,
double agg)
Compute unscaled distance in a range of dimensions.
|
private double |
LPIntegerNormDistanceFunction.preDistance(NumberVector v1,
NumberVector v2,
int start,
int end,
double agg) |
private double |
EuclideanDistanceFunction.preDistance(NumberVector v1,
NumberVector v2,
int start,
int end,
double agg) |
private double |
WeightedMaximumDistanceFunction.preDistanceVM(NumberVector v,
SpatialComparable mbr,
int start,
int end,
double agg) |
private double |
WeightedManhattanDistanceFunction.preDistanceVM(NumberVector v,
SpatialComparable mbr,
int start,
int end,
double agg) |
private double |
WeightedLPNormDistanceFunction.preDistanceVM(NumberVector v,
SpatialComparable mbr,
int start,
int end,
double agg) |
private double |
WeightedEuclideanDistanceFunction.preDistanceVM(NumberVector v,
SpatialComparable mbr,
int start,
int end,
double agg) |
private double |
SquaredEuclideanDistanceFunction.preDistanceVM(NumberVector v,
SpatialComparable mbr,
int start,
int end,
double agg) |
private double |
MaximumDistanceFunction.preDistanceVM(NumberVector v,
SpatialComparable mbr,
int start,
int end,
double agg) |
private double |
ManhattanDistanceFunction.preDistanceVM(NumberVector v,
SpatialComparable mbr,
int start,
int end,
double agg) |
private double |
LPNormDistanceFunction.preDistanceVM(NumberVector v,
SpatialComparable mbr,
int start,
int end,
double agg)
Compute unscaled distance in a range of dimensions.
|
private double |
LPIntegerNormDistanceFunction.preDistanceVM(NumberVector v,
SpatialComparable mbr,
int start,
int end,
double agg) |
private double |
EuclideanDistanceFunction.preDistanceVM(NumberVector v,
SpatialComparable mbr,
int start,
int end,
double agg) |
private double |
WeightedMaximumDistanceFunction.preNorm(NumberVector v,
int start,
int end,
double agg) |
private double |
WeightedManhattanDistanceFunction.preNorm(NumberVector v,
int start,
int end,
double agg) |
private double |
WeightedLPNormDistanceFunction.preNorm(NumberVector v,
int start,
int end,
double agg) |
private double |
WeightedEuclideanDistanceFunction.preNorm(NumberVector v,
int start,
int end,
double agg) |
private double |
SquaredEuclideanDistanceFunction.preNorm(NumberVector v,
int start,
int end,
double agg) |
private double |
MaximumDistanceFunction.preNorm(NumberVector v,
int start,
int end,
double agg) |
private double |
ManhattanDistanceFunction.preNorm(NumberVector v,
int start,
int end,
double agg) |
private double |
LPNormDistanceFunction.preNorm(NumberVector v,
int start,
int end,
double agg)
Compute unscaled norm in a range of dimensions.
|
private double |
LPIntegerNormDistanceFunction.preNorm(NumberVector v,
int start,
int end,
double agg) |
private double |
EuclideanDistanceFunction.preNorm(NumberVector v,
int start,
int end,
double agg) |
| Modifier and Type | Method and Description |
|---|---|
<T extends NumberVector> |
HellingerDistanceFunction.instantiate(Relation<T> database) |
| Modifier and Type | Method and Description |
|---|---|
SimpleTypeInformation<? super NumberVector> |
HellingerDistanceFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super NumberVector> |
ChiSquaredDistanceFunction.getInputTypeRestriction() |
| Modifier and Type | Method and Description |
|---|---|
double |
SqrtJensenShannonDivergenceDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
KullbackLeiblerDivergenceAsymmetricDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
JensenShannonDivergenceDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
JeffreyDivergenceDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
HellingerDistanceFunction.distance(NumberVector fv1,
NumberVector fv2) |
double |
ChiSquaredDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
HellingerDistanceFunction.similarity(NumberVector o1,
NumberVector o2) |
| Modifier and Type | Method and Description |
|---|---|
double |
JaccardSimilarityDistanceFunction.distance(NumberVector o1,
NumberVector o2) |
double |
HammingDistanceFunction.distance(NumberVector o1,
NumberVector o2) |
private double |
HammingDistanceFunction.hammingDistanceNumberVector(NumberVector o1,
NumberVector o2)
Version for number vectors.
|
static double |
JaccardSimilarityDistanceFunction.similarityNumberVector(NumberVector o1,
NumberVector o2)
Compute Jaccard similarity for two number vectors.
|
| Modifier and Type | Method and Description |
|---|---|
<T extends NumberVector> |
SubspaceLPNormDistanceFunction.instantiate(Relation<T> database) |
| Modifier and Type | Method and Description |
|---|---|
VectorFieldTypeInformation<? super NumberVector> |
SubspaceLPNormDistanceFunction.getInputTypeRestriction() |
VectorTypeInformation<? super NumberVector> |
OnedimensionalDistanceFunction.getInputTypeRestriction() |
| Modifier and Type | Method and Description |
|---|---|
double |
SubspaceMaximumDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
SubspaceManhattanDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
SubspaceLPNormDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
SubspaceEuclideanDistanceFunction.distance(NumberVector v1,
NumberVector v2)
Constructor.
|
double |
OnedimensionalDistanceFunction.distance(NumberVector v1,
NumberVector v2)
Computes the distance between two given DatabaseObjects according to this
distance function.
|
protected double |
SubspaceMaximumDistanceFunction.minDistObject(SpatialComparable mbr,
NumberVector v) |
protected double |
SubspaceManhattanDistanceFunction.minDistObject(SpatialComparable mbr,
NumberVector v) |
protected double |
SubspaceLPNormDistanceFunction.minDistObject(SpatialComparable mbr,
NumberVector v) |
protected double |
SubspaceEuclideanDistanceFunction.minDistObject(SpatialComparable mbr,
NumberVector v) |
double |
SubspaceMaximumDistanceFunction.norm(NumberVector obj) |
double |
SubspaceManhattanDistanceFunction.norm(NumberVector obj) |
double |
SubspaceLPNormDistanceFunction.norm(NumberVector obj) |
double |
SubspaceEuclideanDistanceFunction.norm(NumberVector obj) |
double |
OnedimensionalDistanceFunction.norm(NumberVector obj) |
| Modifier and Type | Method and Description |
|---|---|
VectorTypeInformation<? super NumberVector> |
LCSSDistanceFunction.getInputTypeRestriction() |
VectorTypeInformation<? super NumberVector> |
AbstractEditDistanceFunction.getInputTypeRestriction() |
| Modifier and Type | Method and Description |
|---|---|
double |
DerivativeDTWDistanceFunction.derivative(int i,
NumberVector v)
Given a NumberVector and the position of an element, approximates the
gradient of given element.
|
double |
LCSSDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
ERPDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
EDRDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
DerivativeDTWDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
double |
DTWDistanceFunction.distance(NumberVector v1,
NumberVector v2) |
protected void |
ERPDistanceFunction.firstRow(double[] buf,
int band,
NumberVector v1,
NumberVector v2,
int dim2) |
protected void |
EDRDistanceFunction.firstRow(double[] buf,
int band,
NumberVector v1,
NumberVector v2,
int dim2) |
protected void |
DerivativeDTWDistanceFunction.firstRow(double[] buf,
int band,
NumberVector v1,
NumberVector v2,
int dim2) |
protected void |
DTWDistanceFunction.firstRow(double[] buf,
int band,
NumberVector v1,
NumberVector v2,
int dim2)
Fill the first row.
|
double |
LCSSDistanceFunction.getRange(NumberVector v1,
int dim1,
NumberVector v2,
int dim2) |
| Modifier and Type | Method and Description |
|---|---|
SimpleTypeInformation<? super NumberVector> |
AbstractVectorSimilarityFunction.getInputTypeRestriction() |
| Modifier and Type | Method and Description |
|---|---|
double |
Kulczynski2SimilarityFunction.similarity(NumberVector v1,
NumberVector v2) |
double |
Kulczynski1SimilarityFunction.similarity(NumberVector v1,
NumberVector v2) |
| Modifier and Type | Method and Description |
|---|---|
<T extends NumberVector> |
PolynomialKernelFunction.instantiate(Relation<T> database) |
| Modifier and Type | Method and Description |
|---|---|
double |
PolynomialKernelFunction.distance(NumberVector fv1,
NumberVector fv2) |
double |
LinearKernelFunction.distance(NumberVector fv1,
NumberVector fv2) |
double |
SigmoidKernelFunction.similarity(NumberVector o1,
NumberVector o2) |
double |
RationalQuadraticKernelFunction.similarity(NumberVector o1,
NumberVector o2) |
double |
RadialBasisFunctionKernelFunction.similarity(NumberVector o1,
NumberVector o2) |
double |
PolynomialKernelFunction.similarity(NumberVector o1,
NumberVector o2) |
double |
LinearKernelFunction.similarity(NumberVector o1,
NumberVector o2) |
double |
LaplaceKernelFunction.similarity(NumberVector o1,
NumberVector o2) |
| Modifier and Type | Field and Description |
|---|---|
private PrimitiveDistanceFunction<NumberVector> |
EvaluateConcordantPairs.Parameterizer.distance
Distance function to use.
|
private PrimitiveDistanceFunction<? super NumberVector> |
EvaluateConcordantPairs.distanceFunction
Distance function to use.
|
| Modifier and Type | Method and Description |
|---|---|
protected EvaluateVarianceRatioCriteria<? extends NumberVector> |
EvaluateVarianceRatioCriteria.Parameterizer.makeInstance() |
| Modifier and Type | Method and Description |
|---|---|
static int |
EvaluateSimplifiedSilhouette.centroids(Relation<? extends NumberVector> rel,
List<? extends Cluster<?>> clusters,
NumberVector[] centroids,
NoiseHandling noiseOption)
Compute centroids.
|
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 | 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.
|
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) |
| Constructor and Description |
|---|
EvaluateConcordantPairs(PrimitiveDistanceFunction<? super NumberVector> distance,
NoiseHandling noiseHandling)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) NumberVector |
VectorZero.vec
Vector to use as reference
|
(package private) NumberVector |
VectorOverThreshold.vec
Vector to use as reference
|
protected NumberVector |
AbstractVectorIter.vec
Data vector.
|
| Constructor and Description |
|---|
AbstractVectorIter(NumberVector vec)
Constructor.
|
DecreasingVectorIter(NumberVector vec)
Constructor.
|
IncreasingVectorIter(NumberVector vec)
Constructor.
|
VectorNonZero(NumberVector vec)
Constructor.
|
VectorOverThreshold(NumberVector vec,
double threshold)
Constructor.
|
VectorZero(NumberVector vec)
Constructor.
|
| Modifier and Type | Class and Description |
|---|---|
class |
InMemoryInvertedIndex<V extends NumberVector>
Simple index using inverted lists.
|
static class |
InMemoryInvertedIndex.Factory<V extends NumberVector>
Index factory
|
static class |
InMemoryInvertedIndex.Factory.Parameterizer<V extends NumberVector>
Parameterizer for inverted list index.
|
| Modifier and Type | Method and Description |
|---|---|
private double |
InMemoryInvertedIndex.naiveQueryDense(NumberVector obj,
WritableDoubleDataStore scores,
HashSetModifiableDBIDs cands)
Query the most similar objects, dense version.
|
| 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) |
| 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) |
| Modifier and Type | Method and Description |
|---|---|
int |
MultipleProjectionsLocalitySensitiveHashFunction.hashObject(NumberVector vec) |
int |
CosineLocalitySensitiveHashFunction.hashObject(NumberVector obj) |
int |
MultipleProjectionsLocalitySensitiveHashFunction.hashObject(NumberVector vec,
double[] buf) |
int |
CosineLocalitySensitiveHashFunction.hashObject(NumberVector obj,
double[] buf) |
| Modifier and Type | Interface and Description |
|---|---|
interface |
LocalProjectionIndex<V extends NumberVector,P extends ProjectionResult>
Abstract index interface for local projections
|
static interface |
LocalProjectionIndex.Factory<V extends NumberVector,I extends LocalProjectionIndex<V,?>>
Factory
|
| Modifier and Type | Class and Description |
|---|---|
class |
RandomProjectedNeighborsAndDensities<V extends NumberVector>
Random Projections used for computing neighbors and density estimates.
|
| Modifier and Type | Method and Description |
|---|---|
protected RandomProjectedNeighborsAndDensities<NumberVector> |
RandomProjectedNeighborsAndDensities.Parameterizer.makeInstance() |
| Modifier and Type | Class and Description |
|---|---|
class |
KNNJoinMaterializeKNNPreprocessor<V extends NumberVector>
Class to materialize the kNN using a spatial join on an R-tree.
|
static class |
KNNJoinMaterializeKNNPreprocessor.Factory<O extends NumberVector>
The parameterizable factory.
|
static class |
KNNJoinMaterializeKNNPreprocessor.Factory.Parameterizer<O extends NumberVector>
Parameterization class
|
class |
MetricalIndexApproximationMaterializeKNNPreprocessor<O extends NumberVector,N extends Node<E>,E extends MTreeEntry>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
static class |
MetricalIndexApproximationMaterializeKNNPreprocessor.Factory<O extends NumberVector,N extends Node<E>,E extends MTreeEntry>
The parameterizable factory.
|
static class |
MetricalIndexApproximationMaterializeKNNPreprocessor.Factory.Parameterizer<O extends NumberVector,N extends Node<E>,E extends MTreeEntry>
Parameterization class.
|
class |
NaiveProjectedKNNPreprocessor<O extends NumberVector>
Compute the approximate k nearest neighbors using 1 dimensional projections.
|
static class |
NaiveProjectedKNNPreprocessor.Factory<V extends NumberVector>
Index factory class
|
class |
SpacefillingKNNPreprocessor<O extends NumberVector>
Compute the nearest neighbors approximatively using space filling curves.
|
static class |
SpacefillingKNNPreprocessor.Factory<V extends NumberVector>
Index factory class
|
class |
SpacefillingMaterializeKNNPreprocessor<O extends NumberVector>
Compute the nearest neighbors approximatively using space filling curves.
|
static class |
SpacefillingMaterializeKNNPreprocessor.Factory<V extends NumberVector>
Index factory class
|
static class |
SpacefillingMaterializeKNNPreprocessor.Factory.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) List<List<SpatialPair<DBID,NumberVector>>> |
SpacefillingKNNPreprocessor.curves
Curve storage
|
| Modifier and Type | Method and Description |
|---|---|
SpatialApproximationMaterializeKNNPreprocessor<NumberVector,N,E> |
SpatialApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<NumberVector> relation) |
| Modifier and Type | Method and Description |
|---|---|
SpatialApproximationMaterializeKNNPreprocessor<NumberVector,N,E> |
SpatialApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<NumberVector> relation) |
| Constructor and Description |
|---|
SpatialApproximationMaterializeKNNPreprocessor.Factory(int k,
DistanceFunction<? super NumberVector> distanceFunction)
Constructor.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractFilteredPCAIndex<NV extends NumberVector>
Abstract base class for a local PCA based index.
|
static class |
AbstractFilteredPCAIndex.Factory<NV extends NumberVector,I extends AbstractFilteredPCAIndex<NV>>
Factory class.
|
static class |
AbstractFilteredPCAIndex.Factory.Parameterizer<NV extends NumberVector,I extends AbstractFilteredPCAIndex<NV>>
Parameterization class.
|
interface |
FilteredLocalPCAIndex<NV extends NumberVector>
Interface for an index providing local PCA results.
|
static interface |
FilteredLocalPCAIndex.Factory<NV extends NumberVector,I extends FilteredLocalPCAIndex<NV>>
Factory interface
|
class |
KNNQueryFilteredPCAIndex<NV extends NumberVector>
Provides the local neighborhood to be considered in the PCA as the k nearest
neighbors of an object.
|
static class |
KNNQueryFilteredPCAIndex.Factory<V extends NumberVector>
Factory class.
|
static class |
KNNQueryFilteredPCAIndex.Factory.Parameterizer<NV extends NumberVector>
Parameterization class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractPreferenceVectorIndex<NV extends NumberVector>
Abstract base class for preference vector based algorithms.
|
static class |
AbstractPreferenceVectorIndex.Factory<V extends NumberVector,I extends PreferenceVectorIndex<V>>
Factory class.
|
class |
DiSHPreferenceVectorIndex<V extends NumberVector>
Preprocessor for DiSH preference vector assignment to objects of a certain
database.
|
static class |
DiSHPreferenceVectorIndex.Factory<V extends NumberVector>
Factory class.
|
static class |
DiSHPreferenceVectorIndex.Factory.Parameterizer<V extends NumberVector>
Parameterization class.
|
class |
HiSCPreferenceVectorIndex<V extends NumberVector>
Preprocessor for HiSC preference vector assignment to objects of a certain
database.
|
static class |
HiSCPreferenceVectorIndex.Factory<V extends NumberVector>
Factory class.
|
static class |
HiSCPreferenceVectorIndex.Factory.Parameterizer<V extends NumberVector>
Parameterization class.
|
interface |
PreferenceVectorIndex<NV extends NumberVector>
Interface for an index providing preference vectors.
|
static interface |
PreferenceVectorIndex.Factory<V extends NumberVector,I extends PreferenceVectorIndex<V>>
Factory interface
|
| Modifier and Type | Class and Description |
|---|---|
class |
LatLngAsECEFIndex<O extends NumberVector>
Index a 2d data set (consisting of Lat/Lng pairs) by using a projection to 3D
coordinates (WGS-86 to ECEF).
|
static class |
LatLngAsECEFIndex.Factory<O extends NumberVector>
Index factory.
|
static class |
LatLngAsECEFIndex.Factory.Parameterizer<O extends NumberVector>
Parameterization class.
|
class |
LngLatAsECEFIndex<O extends NumberVector>
Index a 2d data set (consisting of Lng/Lat pairs) by using a projection to 3D
coordinates (WGS-86 to ECEF).
|
static class |
LngLatAsECEFIndex.Factory<O extends NumberVector>
Index factory.
|
static class |
LngLatAsECEFIndex.Factory.Parameterizer<O extends NumberVector>
Parameterization class.
|
class |
PINN<O extends NumberVector>
Projection-Indexed nearest-neighbors (PINN) is an index to retrieve the
nearest neighbors in high dimensional spaces by using a random projection
based index.
|
static class |
PINN.Parameterizer<O extends NumberVector>
Parameterization class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SpatialPointLeafEntry
Represents an entry in a leaf node of a spatial index.
|
| Constructor and Description |
|---|
SpatialPointLeafEntry(DBID id,
NumberVector vector)
Constructor from number vector.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MinimalisticMemoryKDTree<O extends NumberVector>
Simple implementation of a static in-memory K-D-tree.
|
static class |
MinimalisticMemoryKDTree.Factory<O extends NumberVector>
Factory class
|
static class |
MinimalisticMemoryKDTree.Factory.Parameterizer<O extends NumberVector>
Parameterization class.
|
class |
SmallMemoryKDTree<O extends NumberVector>
Simple implementation of a static in-memory K-D-tree.
|
static class |
SmallMemoryKDTree.Factory<O extends NumberVector>
Factory class
|
static class |
SmallMemoryKDTree.Factory.Parameterizer<O extends NumberVector>
Parameterization class.
|
| Constructor and Description |
|---|
MinimalisticMemoryKDTree.CountSortAccesses(Counter objaccess,
Relation<? extends NumberVector> data)
Constructor.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractRStarTreeFactory<O extends NumberVector,N extends AbstractRStarTreeNode<N,E>,E extends SpatialEntry,I extends AbstractRStarTree<N,E,S> & Index,S extends AbstractRTreeSettings>
Abstract factory for R*-Tree based trees.
|
static class |
AbstractRStarTreeFactory.Parameterizer<O extends NumberVector,S extends AbstractRTreeSettings>
Parameterization class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DeLiCluTreeFactory<O extends NumberVector>
Factory for DeLiClu R*-Trees.
|
static class |
DeLiCluTreeFactory.Parameterizer<O extends NumberVector>
Parameterization class.
|
class |
DeLiCluTreeIndex<O extends NumberVector>
The common use of the DeLiClu tree: indexing number vectors.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DeLiCluLeafEntry
Defines the requirements for a leaf entry in an DeLiClu-Tree node.
|
| Constructor and Description |
|---|
DeLiCluLeafEntry(DBID id,
NumberVector vector)
Constructs a new LeafEntry object with the given parameters.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FlatRStarTreeFactory<O extends NumberVector>
Factory for flat R*-Trees.
|
static class |
FlatRStarTreeFactory.Parameterizer<O extends NumberVector>
Parameterization class.
|
class |
FlatRStarTreeIndex<O extends NumberVector>
The common use of the flat rstar tree: indexing number vectors.
|
| Modifier and Type | Class and Description |
|---|---|
class |
EuclideanRStarTreeKNNQuery<O extends NumberVector>
Instance of a KNN query for a particular spatial index.
|
class |
EuclideanRStarTreeRangeQuery<O extends NumberVector>
Instance of a range query for a particular spatial index.
|
| Modifier and Type | Class and Description |
|---|---|
class |
RdkNNSettings<O extends NumberVector>
Settings for the RdKNN Tree.
|
class |
RdKNNTree<O extends NumberVector>
RDkNNTree is a spatial index structure based on the concepts of the R*-Tree
supporting efficient processing of reverse k nearest neighbor queries.
|
class |
RdKNNTreeFactory<O extends NumberVector>
Factory for RdKNN R*-Trees.
|
static class |
RdKNNTreeFactory.Parameterizer<O extends NumberVector>
Parameterization class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
RdKNNLeafEntry
Represents an entry in a leaf node of an RdKNN-Tree.
|
| Constructor and Description |
|---|
RdKNNLeafEntry(DBID id,
NumberVector vector,
double knnDistance)
Constructs a new RDkNNLeafEntry object with the given parameters.
|
| Modifier and Type | Class and Description |
|---|---|
class |
RStarTreeFactory<O extends NumberVector>
Factory for regular R*-Trees.
|
static class |
RStarTreeFactory.Parameterizer<O extends NumberVector>
Parameterization class.
|
class |
RStarTreeIndex<O extends NumberVector>
The common use of the rstar tree: indexing number vectors.
|
| Modifier and Type | Class and Description |
|---|---|
class |
PartialVAFile<V extends NumberVector>
PartialVAFile.
|
static class |
PartialVAFile.Factory<V extends NumberVector>
Index factory class.
|
class |
VAFile<V extends NumberVector>
Vector-approximation file (VAFile)
Reference:
Weber, R. and Blott, S.
|
static class |
VAFile.Factory<V extends NumberVector>
Index factory class.
|
| Modifier and Type | Method and Description |
|---|---|
protected static VectorApproximation |
PartialVAFile.calculatePartialApproximation(DBID id,
NumberVector dv,
List<DoubleObjPair<DAFile>> daFiles)
Calculate partial vector approximation.
|
protected static void |
PartialVAFile.calculateSelectivityCoeffs(List<DoubleObjPair<DAFile>> daFiles,
NumberVector query,
double epsilon)
Calculate selectivity coefficients.
|
private void |
VALPNormDistance.initializeLookupTable(double[][] splitPositions,
NumberVector query,
double p)
Initialize the lookup table.
|
| Constructor and Description |
|---|
VALPNormDistance(double p,
double[][] splitPositions,
NumberVector query,
VectorApproximation queryApprox)
Constructor.
|
| Constructor and Description |
|---|
DAFile(Relation<? extends NumberVector> relation,
int dimension,
int partitions)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
static double |
MathUtil.pearsonCorrelationCoefficient(NumberVector x,
NumberVector y)
Compute the Pearson product-moment correlation coefficient for two
NumberVectors.
|
static double |
MathUtil.weightedPearsonCorrelationCoefficient(NumberVector x,
NumberVector y,
double[] weights)
Compute the Pearson product-moment correlation coefficient for two
NumberVectors.
|
static double |
MathUtil.weightedPearsonCorrelationCoefficient(NumberVector x,
NumberVector y,
NumberVector weights)
Compute the Pearson product-moment correlation coefficient for two
FeatureVectors.
|
| 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) |
| Modifier and Type | Class and Description |
|---|---|
class |
Centroid
Class to compute the centroid of some data.
|
class |
ProjectedCentroid
Centroid only using a subset of dimensions.
|
class |
Vector
A mathematical vector object, along with mathematical operations.
|
| Modifier and Type | Method and Description |
|---|---|
<F extends NumberVector> |
CovarianceMatrix.getMeanVector(Relation<? extends F> relation)
Get the mean as vector.
|
<F extends NumberVector> |
Centroid.toVector(Relation<? extends F> relation)
Get the data as vector.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
Vector.Factory.newNumberVector(NumberVector values) |
void |
ProjectedCentroid.put(NumberVector val)
Add a single value with weight 1.0.
|
void |
CovarianceMatrix.put(NumberVector val)
Add a single value with weight 1.0.
|
void |
Centroid.put(NumberVector val)
Add a single value with weight 1.0.
|
void |
ProjectedCentroid.put(NumberVector val,
double weight)
Add data with a given weight.
|
void |
CovarianceMatrix.put(NumberVector val,
double weight)
Add data with a given weight.
|
void |
Centroid.put(NumberVector val,
double weight)
Add data with a given weight.
|
| Modifier and Type | Method and Description |
|---|---|
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.
|
| Modifier and Type | Field and Description |
|---|---|
private PrimitiveDistanceFunction<? super NumberVector> |
WeightedCovarianceMatrixBuilder.weightDistance
Holds the distance function used for weight calculation.
|
| 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 |
|---|---|
double[] |
SimplifiedRandomHyperplaneProjectionFamily.SignedProjection.project(NumberVector in) |
double[] |
RandomSubsetProjectionFamily.SubsetProjection.project(NumberVector in) |
double[] |
RandomProjectionFamily.Projection.project(NumberVector in)
Project a single vector.
|
double[] |
AbstractRandomProjectionFamily.MatrixProjection.project(NumberVector in) |
double[] |
SimplifiedRandomHyperplaneProjectionFamily.SignedProjection.project(NumberVector vec,
double[] ret) |
double[] |
RandomSubsetProjectionFamily.SubsetProjection.project(NumberVector in,
double[] buffer) |
double[] |
RandomProjectionFamily.Projection.project(NumberVector in,
double[] buffer)
Project a single vector, into the given buffer.
|
double[] |
AbstractRandomProjectionFamily.MatrixProjection.project(NumberVector in,
double[] ret) |
private double[] |
SimplifiedRandomHyperplaneProjectionFamily.SignedProjection.projectDense(NumberVector in,
double[] ret)
Slower version, for dense multiplication.
|
| Modifier and Type | Method and Description |
|---|---|
byte[] |
ZCurveTransformer.asByteArray(NumberVector vector)
Transform a single vector.
|
| Constructor and Description |
|---|
ZCurveTransformer(Relation<? extends NumberVector> relation,
DBIDs ids)
Constructor.
|
| 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.
|
| Modifier and Type | Method and Description |
|---|---|
Number |
NumberVectorAdapter.get(NumberVector array,
int off)
Deprecated.
|
byte |
NumberVectorAdapter.getByte(NumberVector array,
int off) |
double |
NumberVectorAdapter.getDouble(NumberVector array,
int off) |
float |
NumberVectorAdapter.getFloat(NumberVector array,
int off) |
int |
NumberVectorAdapter.getInteger(NumberVector array,
int off) |
long |
NumberVectorAdapter.getLong(NumberVector array,
int off) |
short |
NumberVectorAdapter.getShort(NumberVector array,
int off) |
static NumberVectorAdapter |
ArrayLikeUtil.numberVectorAdapter(NumberVector prototype)
Get the static instance.
|
int |
NumberVectorAdapter.size(NumberVector array) |
static double[] |
ArrayLikeUtil.toPrimitiveDoubleArray(NumberVector obj)
Convert a number vector to
double[]. |
static float[] |
ArrayLikeUtil.toPrimitiveFloatArray(NumberVector obj)
Convert a number vector to
float[]. |
static int[] |
ArrayLikeUtil.toPrimitiveIntegerArray(NumberVector obj)
Convert a number vector to
int[]. |
| 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 | 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 | Method and Description |
|---|---|
<NV extends NumberVector> |
FullProjection.projectRelativeRenderToDataSpace(double[] v,
NumberVector.Factory<NV> prototype)
Project a relative vector from rendering space to data space.
|
<NV extends NumberVector> |
AbstractFullProjection.projectRelativeRenderToDataSpace(double[] v,
NumberVector.Factory<NV> prototype)
Project a relative vector from rendering space to data space.
|
<NV extends NumberVector> |
FullProjection.projectRelativeScaledToDataSpace(double[] v,
NumberVector.Factory<NV> prototype)
Project a relative vector from scaled space to data space.
|
<NV extends NumberVector> |
AbstractFullProjection.projectRelativeScaledToDataSpace(double[] v,
NumberVector.Factory<NV> prototype)
Project a relative vector from scaled space to data space.
|
<NV extends NumberVector> |
FullProjection.projectRenderToDataSpace(double[] v,
NumberVector.Factory<NV> prototype)
Project a vector from rendering space to data space.
|
<NV extends NumberVector> |
AbstractFullProjection.projectRenderToDataSpace(double[] v,
NumberVector.Factory<NV> prototype)
Project a vector from rendering space to data space.
|
<NV extends NumberVector> |
FullProjection.projectScaledToDataSpace(double[] v,
NumberVector.Factory<NV> factory)
Project a vector from scaled space to data space.
|
<NV extends NumberVector> |
AbstractFullProjection.projectScaledToDataSpace(double[] v,
NumberVector.Factory<NV> factory)
Project a vector from scaled space to data space.
|
| Modifier and Type | Method and Description |
|---|---|
double[] |
SimpleParallel.fastProjectDataToRenderSpace(NumberVector data) |
double[] |
Simple2D.fastProjectDataToRenderSpace(NumberVector data) |
double |
Simple1D.fastProjectDataToRenderSpace(NumberVector data) |
double[] |
ProjectionParallel.fastProjectDataToRenderSpace(NumberVector v)
Fast project a vector from data to render space
|
double[] |
Projection2D.fastProjectDataToRenderSpace(NumberVector data)
Project a data vector from data space to rendering space.
|
double |
Projection1D.fastProjectDataToRenderSpace(NumberVector data)
Project a data vector from data space to rendering space.
|
double[] |
AffineProjection.fastProjectDataToRenderSpace(NumberVector data) |
double[] |
Simple2D.fastProjectDataToScaledSpace(NumberVector data) |
double[] |
Projection2D.fastProjectDataToScaledSpace(NumberVector data)
Project a data vector from data space to scaled space.
|
double[] |
AffineProjection.fastProjectDataToScaledSpace(NumberVector data) |
double[] |
Simple2D.fastProjectRelativeDataToRenderSpace(NumberVector data) |
double |
Simple1D.fastProjectRelativeDataToRenderSpace(NumberVector data) |
double[] |
Projection2D.fastProjectRelativeDataToRenderSpace(NumberVector data)
Project a data vector from data space to rendering space.
|
double |
Projection1D.fastProjectRelativeDataToRenderSpace(NumberVector data)
Project a data vector from data space to rendering space.
|
double[] |
AffineProjection.fastProjectRelativeDataToRenderSpace(NumberVector data) |
double[] |
FullProjection.projectDataToRenderSpace(NumberVector data)
Project a data vector from data space to rendering space.
|
double[] |
AbstractFullProjection.projectDataToRenderSpace(NumberVector data)
Project a data vector from data space to rendering space.
|
double[] |
FullProjection.projectDataToScaledSpace(NumberVector data)
Project a data vector from data space to scaled space.
|
double[] |
AbstractFullProjection.projectDataToScaledSpace(NumberVector data)
Project a data vector from data space to scaled space.
|
double[] |
FullProjection.projectRelativeDataToRenderSpace(NumberVector data)
Project a relative data vector from data space to rendering space.
|
double[] |
AbstractFullProjection.projectRelativeDataToRenderSpace(NumberVector data)
Project a relative data vector from data space to rendering space.
|
double[] |
FullProjection.projectRelativeDataToScaledSpace(NumberVector data)
Project a relative data vector from data space to scaled space.
|
double[] |
AbstractFullProjection.projectRelativeDataToScaledSpace(NumberVector data)
Project a relative data vector from data space to scaled space.
|
| Modifier and Type | Class and Description |
|---|---|
class |
HistogramProjector<V extends NumberVector>
ScatterPlotProjector is responsible for producing a set of scatterplot
visualizations.
|
| Modifier and Type | Method and Description |
|---|---|
static Element |
SVGHyperSphere.drawCross(SVGPlot svgp,
Projection2D proj,
NumberVector mid,
double radius)
Wireframe "cross" hypersphere
|
static Element |
SVGHyperSphere.drawEuclidean(SVGPlot svgp,
Projection2D proj,
NumberVector mid,
double radius)
Wireframe "euclidean" hypersphere
|
static Element |
SVGHyperCube.drawFilled(SVGPlot svgp,
String cls,
Projection2D proj,
NumberVector min,
NumberVector max)
Filled hypercube.
|
static Element |
SVGHyperCube.drawFrame(SVGPlot svgp,
Projection2D proj,
NumberVector min,
NumberVector max)
Wireframe hypercube.
|
static Element |
SVGHyperSphere.drawLp(SVGPlot svgp,
Projection2D proj,
NumberVector mid,
double radius,
double p)
Wireframe "Lp" hypersphere
|
static Element |
SVGHyperSphere.drawManhattan(SVGPlot svgp,
Projection2D proj,
NumberVector mid,
double radius)
Wireframe "manhattan" hypersphere
|
private static ArrayList<double[]> |
SVGHyperCube.getVisibleEdges(Projection2D proj,
NumberVector s_min,
NumberVector s_max)
Get the visible (non-0) edges of a hypercube
|
| Modifier and Type | Class and Description |
|---|---|
class |
ColoredHistogramVisualizer.Instance<NV extends NumberVector>
Instance
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends NumberVector> |
AbstractScatterplotVisualization.rel
The representation we visualize
|
protected ReferencePointsResult<? extends NumberVector> |
ReferencePointsVisualization.Instance.result
Serves reference points.
|
| Modifier and Type | Field and Description |
|---|---|
private AbstractMaterializeKNNPreprocessor<? extends NumberVector> |
DistanceFunctionVisualization.Instance.result
The selection result we work on
|
| Modifier and Type | Method and Description |
|---|---|
static Element |
DistanceFunctionVisualization.drawCosine(SVGPlot svgp,
Projection2D proj,
NumberVector mid,
double angle)
Visualizes Cosine and ArcCosine distance functions
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.
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