| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.algorithm |
Algorithms suitable as a task for the
KDDTask main routine. |
| de.lmu.ifi.dbs.elki.algorithm.benchmark |
Benchmarking pseudo algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.classification |
Classification algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering |
Clustering algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation |
Affinity Propagation (AP) clustering.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering |
Biclustering algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
Correlation clustering algorithms
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash |
Helper classes for the
CASH algorithm. |
| de.lmu.ifi.dbs.elki.algorithm.clustering.em |
Expectation-Maximization clustering algorithm.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan |
Generalized DBSCAN.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical |
Hierarchical agglomerative clustering (HAC).
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization |
Initialization strategies for k-means.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel |
Parallelized implementations of k-means.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality |
Quality measures for k-Means results.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.meta |
Meta clustering algorithms, that get their result from other clusterings or external sources.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional |
Clustering algorithms for one-dimensional data.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.optics |
OPTICS family of clustering algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.subspace |
Axis-parallel subspace clustering algorithms
The clustering algorithms in this package are instances of both, projected clustering algorithms or
subspace clustering algorithms according to the classical but somewhat obsolete classification schema
of clustering algorithms for axis-parallel subspaces.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.trivial |
Trivial clustering algorithms: all in one, no clusters, label clusterings
These methods are mostly useful for providing a reference result in evaluation.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain |
Clustering algorithms for uncertain data.
|
| de.lmu.ifi.dbs.elki.algorithm.itemsetmining |
Algorithms for frequent itemset mining such as APRIORI.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier |
Outlier detection algorithms
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased |
Angle-based outlier detection algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.clustering |
Clustering based outlier detection.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.distance |
Distance-based outlier detection algorithms, such as DBOutlier and kNN.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel |
Parallel implementations of distance-based outlier detectors.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.lof |
LOF family of outlier detection algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel |
Parallelized variants of LOF.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.meta |
Meta outlier detection algorithms: external scores, score rescaling.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.spatial |
Spatial outlier detection algorithms
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood |
Spatial outlier neighborhood classes
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.weighted |
Weighted Neighborhood definitions.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.subspace |
Subspace outlier detection methods.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.svm |
Support-Vector-Machines for outlier detection.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.trivial |
Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
|
| de.lmu.ifi.dbs.elki.algorithm.statistics |
Statistical analysis algorithms
The algorithms in this package perform statistical analysis of the data
(e.g. compute distributions, distance distributions etc.)
|
| de.lmu.ifi.dbs.elki.application.greedyensemble |
Greedy ensembles for outlier detection.
|
| de.lmu.ifi.dbs.elki.data |
Basic classes for different data types, database object types and label types.
|
| de.lmu.ifi.dbs.elki.data.model |
Cluster models classes for various algorithms.
|
| de.lmu.ifi.dbs.elki.database |
ELKI database layer - loading, storing, indexing and accessing data
|
| de.lmu.ifi.dbs.elki.database.query.distance |
Prepared queries for distances.
|
| de.lmu.ifi.dbs.elki.database.query.knn |
Prepared queries for k nearest neighbor (kNN) queries.
|
| de.lmu.ifi.dbs.elki.database.query.range |
Prepared queries for ε-range queries, that return all objects within the radius ε.
|
| de.lmu.ifi.dbs.elki.database.query.rknn |
Prepared queries for reverse k nearest neighbor (rkNN) queries.
|
| de.lmu.ifi.dbs.elki.database.query.similarity |
Prepared queries for similarity functions.
|
| de.lmu.ifi.dbs.elki.database.relation |
Relations, materialized and virtual (views).
|
| de.lmu.ifi.dbs.elki.distance.distancefunction |
Distance functions for use within ELKI.
|
| de.lmu.ifi.dbs.elki.distance.distancefunction.adapter |
Distance functions deriving distances from e.g. similarity measures
|
| de.lmu.ifi.dbs.elki.distance.distancefunction.external |
Distance functions using external data sources.
|
| de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic |
Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence.
|
| de.lmu.ifi.dbs.elki.distance.distancefunction.set |
Distance functions for binary and set type data.
|
| de.lmu.ifi.dbs.elki.distance.distancefunction.subspace |
Distance functions based on subspaces.
|
| de.lmu.ifi.dbs.elki.distance.similarityfunction |
Similarity functions.
|
| de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster |
Similarity measures for comparing clusters.
|
| de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel |
Kernel functions.
|
| de.lmu.ifi.dbs.elki.evaluation.clustering.internal |
Internal evaluation measures for clusterings.
|
| de.lmu.ifi.dbs.elki.evaluation.similaritymatrix |
Render a distance matrix to visualize a clustering-distance-combination.
|
| de.lmu.ifi.dbs.elki.index |
Index structure implementations
|
| de.lmu.ifi.dbs.elki.index.distancematrix |
Precomputed distance matrix.
|
| de.lmu.ifi.dbs.elki.index.idistance |
iDistance is a distance based indexing technique, using a reference points embedding.
|
| de.lmu.ifi.dbs.elki.index.invertedlist |
Indexes using inverted lists.
|
| de.lmu.ifi.dbs.elki.index.lsh |
Locality Sensitive Hashing
|
| de.lmu.ifi.dbs.elki.index.lsh.hashfamilies |
Hash function families for LSH.
|
| de.lmu.ifi.dbs.elki.index.preprocessed |
Index structure based on preprocessors
|
| de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics |
Preprocessed index used by the FastOPTICS algorithm.
|
| de.lmu.ifi.dbs.elki.index.preprocessed.knn |
Indexes providing KNN and rKNN data.
|
| de.lmu.ifi.dbs.elki.index.preprocessed.localpca |
Index using a preprocessed local PCA.
|
| de.lmu.ifi.dbs.elki.index.preprocessed.preference |
Indexes storing preference vectors.
|
| de.lmu.ifi.dbs.elki.index.preprocessed.snn |
Indexes providing nearest neighbor sets
|
| de.lmu.ifi.dbs.elki.index.projected |
Projected indexes for data.
|
| de.lmu.ifi.dbs.elki.index.tree.metrical.covertree |
Cover-tree variations.
|
| de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees |
Metrical index structures based on the concepts of the M-Tree
supporting processing of reverse k nearest neighbor queries by
using the k-nn distances of the entries.
|
| de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp | |
| de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop | |
| de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax | |
| de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab | |
| de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree | |
| de.lmu.ifi.dbs.elki.index.tree.spatial.kd |
K-d-tree and variants.
|
| de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu | |
| de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.flat | |
| de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query |
Queries on the R-Tree family of indexes: kNN and range queries.
|
| de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn | |
| de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar | |
| de.lmu.ifi.dbs.elki.index.vafile |
Vector Approximation File
|
| de.lmu.ifi.dbs.elki.math.dimensionsimilarity |
Functions to compute the similarity of dimensions (or the interestingness of the combination).
|
| de.lmu.ifi.dbs.elki.math.linearalgebra |
Linear Algebra package provides classes and computational methods for operations on matrices.
|
| de.lmu.ifi.dbs.elki.math.linearalgebra.pca |
Principal Component Analysis (PCA) and Eigenvector processing.
|
| de.lmu.ifi.dbs.elki.math.scales |
Scales handling for plotting.
|
| de.lmu.ifi.dbs.elki.math.spacefillingcurves |
Space filling curves.
|
| de.lmu.ifi.dbs.elki.result |
Result types, representation and handling
|
| de.lmu.ifi.dbs.elki.result.textwriter |
Text serialization (CSV, Gnuplot, Console, ...)
|
| de.lmu.ifi.dbs.elki.utilities |
Utility and helper classes - commonly used data structures, output formatting, exceptions, ...
|
| de.lmu.ifi.dbs.elki.utilities.referencepoints |
Package containing strategies to obtain reference points
Shared code for various algorithms that use reference points.
|
| de.lmu.ifi.dbs.elki.visualization |
Visualization package of ELKI.
|
| de.lmu.ifi.dbs.elki.visualization.projector |
Projectors are responsible for finding appropriate projections for data relations.
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.histogram |
Visualizers based on 1D projected histograms.
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.parallel |
Visualizers based on parallel coordinates.
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot |
Visualizers based on scatterplots.
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.index |
Visualizers for index structures based on 2D projections.
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier |
Visualizers for outlier scores based on 2D projections.
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.uncertain |
Visualizers for uncertain data.
|
| tutorial.clustering |
Classes from the tutorial on implementing a custom k-means variation.
|
| tutorial.outlier |
| Modifier and Type | Method and Description |
|---|---|
CorrelationAnalysisSolution<V> |
DependencyDerivator.generateModel(Relation<V> db,
DBIDs ids)
Runs the pca on the given set of IDs.
|
CorrelationAnalysisSolution<V> |
DependencyDerivator.generateModel(Relation<V> db,
DBIDs ids,
Vector centroid)
Runs the pca on the given set of IDs and for the given centroid.
|
CollectionResult<MaterializeDistances.DistanceEntry> |
MaterializeDistances.run(Database database,
Relation<O> relation)
Iterates over all points in the database.
|
Result |
DummyAlgorithm.run(Database database,
Relation<O> relation)
Run the algorithm.
|
KNNDistancesSampler.KNNDistanceOrderResult |
KNNDistancesSampler.run(Database database,
Relation<O> relation)
Provides an order of the kNN-distances for all objects within the specified
database.
|
CorrelationAnalysisSolution<V> |
DependencyDerivator.run(Database database,
Relation<V> relation)
Computes quantitatively linear dependencies among the attributes of the
given database based on a linear correlation PCA.
|
WritableDataStore<KNNList> |
KNNJoin.run(Relation<V> relation)
Joins in the given spatial database to each object its k-nearest neighbors.
|
| Modifier and Type | Method and Description |
|---|---|
Result |
RangeQueryBenchmarkAlgorithm.run(Database database,
Relation<O> relation)
Run the algorithm, with a separate query set.
|
Result |
KNNBenchmarkAlgorithm.run(Database database,
Relation<O> relation)
Run the algorithm.
|
Result |
ValidateApproximativeKNNIndex.run(Database database,
Relation<O> relation)
Run the algorithm.
|
Result |
RangeQueryBenchmarkAlgorithm.run(Database database,
Relation<O> relation,
Relation<NumberVector> radrel)
Run the algorithm, with separate radius relation
|
Result |
RangeQueryBenchmarkAlgorithm.run(Database database,
Relation<O> relation,
Relation<NumberVector> radrel)
Run the algorithm, with separate radius relation
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends ClassLabel> |
KNNClassifier.labelrep
Class label representation.
|
| Modifier and Type | Method and Description |
|---|---|
void |
Classifier.buildClassifier(Database database,
Relation<? extends ClassLabel> classLabels)
Performs the training.
|
void |
KNNClassifier.buildClassifier(Database database,
Relation<? extends ClassLabel> labels) |
void |
PriorProbabilityClassifier.buildClassifier(Database database,
Relation<? extends ClassLabel> labelrep)
Learns the prior probability for all classes.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
DBSCAN.expandCluster(Relation<O> relation,
RangeQuery<O> rangeQuery,
DBIDRef startObjectID,
FiniteProgress objprog,
IndefiniteProgress clusprog)
DBSCAN-function expandCluster.
|
Clustering<Model> |
SNNClustering.run(Database database,
Relation<O> relation)
Perform SNN clustering
|
Clustering<PrototypeModel<O>> |
CanopyPreClustering.run(Database database,
Relation<O> relation)
Run the algorithm
|
Clustering<MeanModel> |
NaiveMeanShiftClustering.run(Database database,
Relation<V> relation)
Run the mean-shift clustering algorithm.
|
Clustering<Model> |
DBSCAN.run(Relation<O> relation)
Performs the DBSCAN algorithm on the given database.
|
protected void |
DBSCAN.runDBSCAN(Relation<O> relation,
RangeQuery<O> rangeQuery)
Run the DBSCAN algorithm
|
| Modifier and Type | Method and Description |
|---|---|
double[][] |
DistanceBasedInitializationWithMedian.getSimilarityMatrix(Database db,
Relation<O> relation,
ArrayDBIDs ids) |
double[][] |
AffinityPropagationInitialization.getSimilarityMatrix(Database db,
Relation<O> relation,
ArrayDBIDs ids)
Compute the initial similarity matrix.
|
double[][] |
SimilarityBasedInitializationWithMedian.getSimilarityMatrix(Database db,
Relation<O> relation,
ArrayDBIDs ids) |
Clustering<MedoidModel> |
AffinityPropagationClusteringAlgorithm.run(Database db,
Relation<O> relation)
Perform affinity propagation clustering.
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<V> |
AbstractBiclustering.relation
Relation we use.
|
| Modifier and Type | Method and Description |
|---|---|
Relation<V> |
AbstractBiclustering.getRelation()
Getter for the relation.
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<M> |
AbstractBiclustering.run(Relation<V> relation)
Prepares the algorithm for running on a specific database.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<ParameterizationFunction> |
CASH.fulldatabase
The entire relation.
|
private Relation<V> |
HiCO.Instance.relation
Data relation.
|
| Modifier and Type | Method and Description |
|---|---|
private Relation<ParameterizationFunction> |
CASH.preprocess(Database db,
Relation<V> vrel)
Preprocess the dataset, precomputing the parameterization functions.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
ORCLUS.assign(Relation<V> database,
DistanceQuery<V> distFunc,
List<ORCLUS.ORCLUSCluster> clusters)
Creates a partitioning of the database by assigning each object to its
closest seed.
|
private MaterializedRelation<ParameterizationFunction> |
CASH.buildDB(int dim,
Matrix basis,
DBIDs ids,
Relation<ParameterizationFunction> relation)
Builds a dim-1 dimensional database where the objects are projected into
the specified subspace.
|
private Database |
CASH.buildDerivatorDB(Relation<ParameterizationFunction> relation,
CASHInterval interval)
Builds a database for the derivator consisting of the ids in the specified
interval.
|
private Database |
CASH.buildDerivatorDB(Relation<ParameterizationFunction> relation,
DBIDs ids)
Builds a database for the derivator consisting of the ids in the specified
interval.
|
private double[] |
CASH.determineMinMaxDistance(Relation<ParameterizationFunction> relation,
int dimensionality)
Determines the minimum and maximum function value of all parameterization
functions stored in the specified database.
|
private static int |
CASH.dimensionality(Relation<ParameterizationFunction> relation)
Get the dimensionality of a vector field.
|
private Clustering<Model> |
CASH.doRun(Relation<ParameterizationFunction> relation,
FiniteProgress progress)
Runs the CASH algorithm on the specified database, this method is
recursively called until only noise is left.
|
private List<List<Cluster<CorrelationModel<V>>>> |
ERiC.extractCorrelationClusters(Clustering<Model> dbscanResult,
Relation<V> database,
int dimensionality,
ERiCNeighborPredicate.Instance npred)
Extracts the correlation clusters and noise from the copac result and
returns a mapping of correlation dimension to maps of clusters within this
correlation dimension.
|
private Matrix |
ORCLUS.findBasis(Relation<V> database,
DistanceQuery<V> distFunc,
ORCLUS.ORCLUSCluster cluster,
int dim)
Finds the basis of the subspace of dimensionality
dim for the
specified cluster. |
private LMCLUS.Separation |
LMCLUS.findSeparation(Relation<NumberVector> relation,
DBIDs currentids,
int dimension,
Random r)
This method samples a number of linear manifolds an tries to determine
which the one with the best cluster is.
|
private void |
CASH.initHeap(ObjectHeap<IntegerPriorityObject<CASHInterval>> heap,
Relation<ParameterizationFunction> relation,
int dim,
DBIDs ids)
Initializes the heap with the root intervals.
|
private List<ORCLUS.ORCLUSCluster> |
ORCLUS.initialSeeds(Relation<V> database,
int k)
Initializes the list of seeds wit a random sample of size k.
|
private void |
ORCLUS.merge(Relation<V> database,
DistanceQuery<V> distFunc,
List<ORCLUS.ORCLUSCluster> clusters,
int k_new,
int d_new,
IndefiniteProgress cprogress)
Reduces the number of seeds to k_new
|
private Relation<ParameterizationFunction> |
CASH.preprocess(Database db,
Relation<V> vrel)
Preprocess the dataset, precomputing the parameterization functions.
|
private ORCLUS.ProjectedEnergy |
ORCLUS.projectedEnergy(Relation<V> database,
DistanceQuery<V> distFunc,
ORCLUS.ORCLUSCluster c_i,
ORCLUS.ORCLUSCluster c_j,
int i,
int j,
int dim)
Computes the projected energy of the specified clusters.
|
Clustering<Model> |
LMCLUS.run(Database database,
Relation<NumberVector> relation)
The main LMCLUS (Linear manifold clustering algorithm) is processed in this
method.
|
Clustering<Model> |
CASH.run(Database database,
Relation<V> vrel)
Run CASH on the relation.
|
Clustering<DimensionModel> |
COPAC.run(Database database,
Relation<V> relation)
Run the COPAC algorithm.
|
Clustering<Model> |
ORCLUS.run(Database database,
Relation<V> relation)
Performs the ORCLUS algorithm on the given database.
|
Clustering<CorrelationModel<V>> |
ERiC.run(Database database,
Relation<V> relation)
Performs the ERiC algorithm on the given database.
|
CorrelationClusterOrder |
HiCO.run(Database db,
Relation<V> relation) |
private Matrix |
CASH.runDerivator(Relation<ParameterizationFunction> relation,
int dim,
CASHInterval interval,
ModifiableDBIDs ids)
Runs the derivator on the specified interval and assigns all points having
a distance less then the standard deviation of the derivator model to the
model to this model.
|
private LinearEquationSystem |
CASH.runDerivator(Relation<ParameterizationFunction> relation,
int dimensionality,
DBIDs ids)
Runs the derivator on the specified interval and assigns all points having
a distance less then the standard deviation of the derivator model to the
model to this model.
|
private ORCLUS.ORCLUSCluster |
ORCLUS.union(Relation<V> relation,
DistanceQuery<V> distFunc,
ORCLUS.ORCLUSCluster c1,
ORCLUS.ORCLUSCluster c2,
int dim)
Returns the union of the two specified clusters.
|
| Constructor and Description |
|---|
Instance(Database db,
Relation<V> relation)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<ParameterizationFunction> |
CASHIntervalSplit.database
The database storing the parameterization functions.
|
| Constructor and Description |
|---|
CASHIntervalSplit(Relation<ParameterizationFunction> database,
int minPts)
Initializes the logger and sets the debug status to the given value.
|
| Modifier and Type | Method and Description |
|---|---|
static double |
EM.assignProbabilitiesToInstances(Relation<? extends NumberVector> relation,
List<? extends EMClusterModel<?>> models,
WritableDataStore<double[]> probClusterIGivenX)
Assigns the current probability values to the instances in the database and
compute the expectation value of the current mixture of distributions.
|
List<SphericalGaussianModel> |
SphericalGaussianModelFactory.buildInitialModels(Database database,
Relation<V> relation,
int k,
NumberVectorDistanceFunction<? super V> df) |
List<DiagonalGaussianModel> |
DiagonalGaussianModelFactory.buildInitialModels(Database database,
Relation<V> relation,
int k,
NumberVectorDistanceFunction<? super V> df) |
List<MultivariateGaussianModel> |
MultivariateGaussianModelFactory.buildInitialModels(Database database,
Relation<V> relation,
int k,
NumberVectorDistanceFunction<? super V> df) |
List<? extends EMClusterModel<M>> |
EMClusterModelFactory.buildInitialModels(Database database,
Relation<V> relation,
int k,
NumberVectorDistanceFunction<? super V> df)
Build the initial models
|
static void |
EM.recomputeCovarianceMatrices(Relation<? extends NumberVector> relation,
WritableDataStore<double[]> probClusterIGivenX,
List<? extends EMClusterModel<?>> models)
Recompute the covariance matrixes.
|
Clustering<M> |
EM.run(Database database,
Relation<V> relation)
Performs the EM clustering algorithm on the given database.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<? extends NumberVector> |
ERiCNeighborPredicate.Instance.relation
Vector data relation.
|
| Modifier and Type | Method and Description |
|---|---|
protected abstract M |
AbstractRangeQueryNeighborPredicate.computeLocalModel(DBIDRef id,
DoubleDBIDList neighbors,
Relation<O> relation)
Method to compute the actual data model.
|
protected PreDeConNeighborPredicate.PreDeConModel |
FourCNeighborPredicate.computeLocalModel(DBIDRef id,
DoubleDBIDList neighbors,
Relation<V> relation) |
protected PreDeConNeighborPredicate.PreDeConModel |
PreDeConNeighborPredicate.computeLocalModel(DBIDRef id,
DoubleDBIDList neighbors,
Relation<V> relation) |
protected COPACNeighborPredicate.COPACModel |
COPACNeighborPredicate.computeLocalModel(DBIDRef id,
DoubleDBIDList knnneighbors,
Relation<V> relation)
COPAC model computation
|
ERiCNeighborPredicate.Instance |
ERiCNeighborPredicate.instantiate(Database database,
Relation<V> relation)
Full instantiation interface.
|
COPACNeighborPredicate.Instance |
COPACNeighborPredicate.instantiate(Database database,
Relation<V> relation)
Full instantiation method.
|
DataStore<M> |
AbstractRangeQueryNeighborPredicate.preprocess(Class<? super M> modelcls,
Relation<O> relation,
RangeQuery<O> query)
Perform the preprocessing step.
|
Clustering<Model> |
LSDBC.run(Database database,
Relation<O> relation)
Run the LSDBC algorithm
|
| Constructor and Description |
|---|
Instance(DBIDs ids,
DataStore<PCAFilteredResult> storage,
Relation<? extends NumberVector> relation)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
PointerHierarchyRepresentationResult |
SLINK.run(Database database,
Relation<O> relation)
Performs the SLINK algorithm on the given database.
|
PointerHierarchyRepresentationResult |
AnderbergHierarchicalClustering.run(Database db,
Relation<O> relation)
Run the algorithm
|
PointerHierarchyRepresentationResult |
AGNES.run(Database db,
Relation<O> relation)
Run the algorithm
|
PointerDensityHierarchyRepresentationResult |
SLINKHDBSCANLinearMemory.run(Database db,
Relation<O> relation)
Run the algorithm
|
PointerDensityHierarchyRepresentationResult |
HDBSCANLinearMemory.run(Database db,
Relation<O> relation)
Run the algorithm
|
private void |
SLINK.step2primitive(DBIDRef id,
DBIDArrayIter it,
int n,
Relation<? extends O> relation,
PrimitiveDistanceFunction<? super O> distFunc,
WritableDoubleDataStore m)
Second step: Determine the pairwise distances from all objects in the
pointer representation to the new object with the specified id.
|
| Modifier and Type | Method and Description |
|---|---|
protected boolean |
AbstractKMeans.assignToNearestCluster(Relation<? extends V> relation,
List<? extends NumberVector> means,
List<? extends ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
double[] varsum)
Returns a list of clusters.
|
protected boolean |
KMeansBatchedLloyd.assignToNearestCluster(Relation<V> relation,
DBIDs ids,
List<? extends NumberVector> oldmeans,
double[][] meanshift,
int[] changesize,
List<? extends ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
double[] varsum)
Returns a list of clusters.
|
private int |
KMeansElkan.assignToNearestCluster(Relation<V> relation,
List<Vector> means,
List<Vector> sums,
List<ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
double[] sep,
double[][] cdist,
WritableDoubleDataStore upper,
WritableDataStore<double[]> lower)
Reassign objects, but only if their bounds indicate it is necessary to do
so.
|
private int |
KMeansHamerly.assignToNearestCluster(Relation<V> relation,
List<Vector> means,
List<Vector> sums,
List<ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
double[] sep,
WritableDoubleDataStore upper,
WritableDoubleDataStore lower)
Reassign objects, but only if their bounds indicate it is necessary to do
so.
|
private int |
KMeansElkan.initialAssignToNearestCluster(Relation<V> relation,
List<Vector> means,
List<Vector> sums,
List<ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
WritableDoubleDataStore upper,
WritableDataStore<double[]> lower)
Reassign objects, but only if their bounds indicate it is necessary to do
so.
|
private int |
KMeansHamerly.initialAssignToNearestCluster(Relation<V> relation,
List<Vector> means,
List<Vector> sums,
List<ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
WritableDoubleDataStore upper,
WritableDoubleDataStore lower)
Reassign objects, but only if their bounds indicate it is necessary to do
so.
|
protected boolean |
AbstractKMeans.macQueenIterate(Relation<V> relation,
List<Vector> means,
List<ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
double[] varsum)
Perform a MacQueen style iteration.
|
protected List<Vector> |
AbstractKMeans.means(List<? extends ModifiableDBIDs> clusters,
List<? extends NumberVector> means,
Relation<V> database)
Returns the mean vectors of the given clusters in the given database.
|
protected List<Vector> |
AbstractKMeans.medians(List<? extends ModifiableDBIDs> clusters,
List<Vector> medians,
Relation<V> database)
Returns the median vectors of the given clusters in the given database.
|
Clustering<KMeansModel> |
KMeansBatchedLloyd.run(Database database,
Relation<V> relation) |
Clustering<MedoidModel> |
KMedoidsPAM.run(Database database,
Relation<V> relation)
Run k-medoids
|
Clustering<M> |
KMeansBisecting.run(Database database,
Relation<V> relation) |
Clustering<M> |
KMeans.run(Database database,
Relation<V> rel)
Run the clustering algorithm.
|
Clustering<KMeansModel> |
SingleAssignmentKMeans.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansMacQueen.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansHamerly.run(Database database,
Relation<V> relation) |
Clustering<MedoidModel> |
KMedoidsEM.run(Database database,
Relation<V> relation)
Run k-medoids
|
Clustering<M> |
XMeans.run(Database database,
Relation<V> relation)
Run the algorithm on a database and relation.
|
Clustering<KMeansModel> |
KMeansHybridLloydMacQueen.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansElkan.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansLloyd.run(Database database,
Relation<V> relation) |
Clustering<M> |
BestOfMultipleKMeans.run(Database database,
Relation<V> relation) |
Clustering<MeanModel> |
KMediansLloyd.run(Database database,
Relation<V> relation) |
Clustering<MedoidModel> |
CLARA.run(Database database,
Relation<V> relation) |
protected List<? extends NumberVector> |
XMeans.splitCentroid(Cluster<? extends MeanModel> parentCluster,
Relation<V> relation)
Split an existing centroid into two initial centers.
|
protected List<Cluster<M>> |
XMeans.splitCluster(Cluster<M> parentCluster,
Database database,
Relation<V> relation)
Conditionally splits the clusters based on the information criterion.
|
private void |
KMeansElkan.updateBounds(Relation<V> relation,
WritableIntegerDataStore assignment,
WritableDoubleDataStore upper,
WritableDataStore<double[]> lower,
double[] move)
Update the bounds for k-means.
|
private void |
KMeansHamerly.updateBounds(Relation<V> relation,
WritableIntegerDataStore assignment,
WritableDoubleDataStore upper,
WritableDoubleDataStore lower,
double[] move,
double delta)
Update the bounds for k-means.
|
| Modifier and Type | Method and Description |
|---|---|
<T extends NumberVector,O extends NumberVector> |
PredefinedInitialMeans.chooseInitialMeans(Database database,
Relation<T> relation,
int k,
NumberVectorDistanceFunction<? super T> distanceFunction,
NumberVector.Factory<O> factory) |
<T extends V,O extends NumberVector> |
KMeansInitialization.chooseInitialMeans(Database database,
Relation<T> relation,
int k,
NumberVectorDistanceFunction<? super T> distanceFunction,
NumberVector.Factory<O> factory)
Choose initial means
|
<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 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> |
RandomlyChosenInitialMeans.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> |
FirstKInitialMeans.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> |
PAMInitialMeans.chooseInitialMeans(Database database,
Relation<T> relation,
int k,
NumberVectorDistanceFunction<? super T> distanceFunction,
NumberVector.Factory<V> factory) |
<T extends NumberVector,V extends NumberVector> |
FarthestPointsInitialMeans.chooseInitialMeans(Database database,
Relation<T> relation,
int k,
NumberVectorDistanceFunction<? super T> distanceFunction,
NumberVector.Factory<V> factory) |
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<V> |
KMeansProcessor.relation
Data relation.
|
private Relation<V> |
KMeansProcessor.Instance.relation
Data relation.
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<KMeansModel> |
ParallelLloydKMeans.run(Database database,
Relation<V> relation) |
| Constructor and Description |
|---|
Instance(Relation<V> relation,
NumberVectorDistanceFunction<? super V> distance,
WritableIntegerDataStore assignment,
List<? extends NumberVector> means)
Constructor.
|
KMeansProcessor(Relation<V> relation,
NumberVectorDistanceFunction<? super V> distance,
WritableIntegerDataStore assignment,
double[] varsum)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
static <V extends NumberVector> |
AbstractKMeansQualityMeasure.logLikelihood(Relation<V> relation,
Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction)
Computes log likelihood of an entire clustering.
|
static <V extends NumberVector> |
AbstractKMeansQualityMeasure.logLikelihoodAlternate(Relation<V> relation,
Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction)
Computes log likelihood of an entire clustering.
|
static int |
AbstractKMeansQualityMeasure.numberOfFreeParameters(Relation<? extends NumberVector> relation,
Clustering<? extends MeanModel> clustering)
Compute the number of free parameters.
|
<V extends NumberVector> |
WithinClusterVarianceQualityMeasure.quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation) |
<V extends NumberVector> |
BayesianInformationCriterion.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> |
AkaikeInformationCriterion.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 O> |
KMeansQualityMeasure.quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation)
Calculates and returns the quality measure.
|
static <V extends NumberVector> |
AbstractKMeansQualityMeasure.varianceOfCluster(Cluster<? extends MeanModel> cluster,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation)
Variance contribution of a single cluster.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
ExternalClustering.attachToRelation(Database database,
Relation<?> r,
gnu.trove.list.array.TIntArrayList assignment,
ArrayList<String> name)
Build a clustering from the file result.
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<ClusterModel> |
KNNKernelDensityMinimaClustering.run(Relation<V> relation)
Run the clustering algorithm on a data relation.
|
| Modifier and Type | Method and Description |
|---|---|
private Clustering<OPTICSModel> |
OPTICSXi.extractClusters(ClusterOrder clusterOrderResult,
Relation<?> relation,
double ixi,
int minpts)
Extract clusters from a cluster order result.
|
Clustering<OPTICSModel> |
OPTICSXi.run(Database database,
Relation<?> relation) |
ClusterOrder |
DeLiClu.run(Database database,
Relation<NV> relation) |
abstract ClusterOrder |
AbstractOPTICS.run(Database db,
Relation<O> relation)
Run OPTICS on the database.
|
ClusterOrder |
OPTICSList.run(Database db,
Relation<O> relation) |
abstract ClusterOrder |
GeneralizedOPTICS.run(Database db,
Relation<O> relation)
Run OPTICS on the database.
|
ClusterOrder |
OPTICSHeap.run(Database db,
Relation<O> relation) |
ClusterOrder |
FastOPTICS.run(Database db,
Relation<V> rel)
Run the algorithm.
|
| Constructor and Description |
|---|
Instance(Database db,
Relation<O> relation)
Constructor for a single data set.
|
Instance(Database db,
Relation<O> relation)
Constructor for a single data set.
|
Instance(Database db,
Relation<O> relation)
Constructor for a single data set.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<V> |
DiSH.Instance.relation
Data relation.
|
private Relation<V> |
HiSC.Instance.relation
Data relation.
|
| Modifier and Type | Method and Description |
|---|---|
private ArrayList<PROCLUS.PROCLUSCluster> |
PROCLUS.assignPoints(ArrayDBIDs m_current,
long[][] dimensions,
Relation<V> database)
Assigns the objects to the clusters.
|
private void |
P3C.assignUnassigned(Relation<V> relation,
WritableDataStore<double[]> probClusterIGivenX,
List<MultivariateGaussianModel> models,
ModifiableDBIDs unassigned)
Assign unassigned objects to best candidate based on shortest Mahalanobis
distance.
|
private double |
PROCLUS.avgDistance(Vector centroid,
DBIDs objectIDs,
Relation<V> database,
int dimension)
Computes the average distance of the objects to the centroid along the
specified dimension.
|
private void |
DiSH.buildHierarchy(Relation<V> database,
Clustering<SubspaceModel> clustering,
List<Cluster<SubspaceModel>> clusters,
int dimensionality)
Builds the cluster hierarchy.
|
private void |
DiSH.checkClusters(Relation<V> relation,
gnu.trove.map.hash.TCustomHashMap<long[],List<ArrayModifiableDBIDs>> clustersMap)
Removes the clusters with size < minpts from the cluster map and adds them
to their parents.
|
private Clustering<SubspaceModel> |
DiSH.computeClusters(Relation<V> database,
DiSH.DiSHClusterOrder clusterOrder)
Computes the hierarchical clusters according to the cluster order.
|
private void |
P3C.computeFuzzyMembership(Relation<V> relation,
ArrayList<P3C.Signature> clusterCores,
ModifiableDBIDs unassigned,
WritableDataStore<double[]> probClusterIGivenX,
List<MultivariateGaussianModel> models,
int dim)
Computes a fuzzy membership with the weights based on which cluster cores
each data point is part of.
|
private boolean |
DOC.dimensionIsRelevant(int dimension,
Relation<V> relation,
DBIDs points)
Utility method to test if a given dimension is relevant as determined via a
set of reference points (i.e. if the variance along the attribute is lower
than the threshold).
|
private double |
PROCLUS.evaluateClusters(ArrayList<PROCLUS.PROCLUSCluster> clusters,
long[][] dimensions,
Relation<V> database)
Evaluates the quality of the clusters.
|
private gnu.trove.map.hash.TCustomHashMap<long[],List<ArrayModifiableDBIDs>> |
DiSH.extractClusters(Relation<V> relation,
DiSH.DiSHClusterOrder clusterOrder)
Extracts the clusters from the cluster order.
|
private List<PROCLUS.PROCLUSCluster> |
PROCLUS.finalAssignment(List<Pair<Vector,long[]>> dimensions,
Relation<V> database)
Refinement step to assign the objects to the final clusters.
|
private List<CLIQUESubspace<V>> |
CLIQUE.findDenseSubspaceCandidates(Relation<V> database,
List<CLIQUESubspace<V>> denseSubspaces)
Determines the
k-dimensional dense subspace candidates from the
specified (k-1)-dimensional dense subspaces. |
private List<CLIQUESubspace<V>> |
CLIQUE.findDenseSubspaces(Relation<V> database,
List<CLIQUESubspace<V>> denseSubspaces)
Determines the
k-dimensional dense subspaces and performs a pruning
if this option is chosen. |
private long[][] |
PROCLUS.findDimensions(ArrayDBIDs medoids,
Relation<V> database,
DistanceQuery<V> distFunc,
RangeQuery<V> rangeQuery)
Determines the set of correlated dimensions for each medoid in the
specified medoid set.
|
private List<Pair<Vector,long[]>> |
PROCLUS.findDimensions(ArrayList<PROCLUS.PROCLUSCluster> clusters,
Relation<V> database)
Refinement step that determines the set of correlated dimensions for each
cluster centroid.
|
private List<CLIQUESubspace<V>> |
CLIQUE.findOneDimensionalDenseSubspaceCandidates(Relation<V> database)
Determines the one-dimensional dense subspace candidates by making a pass
over the database.
|
private List<CLIQUESubspace<V>> |
CLIQUE.findOneDimensionalDenseSubspaces(Relation<V> database)
Determines the one dimensional dense subspaces and performs a pruning if
this option is chosen.
|
private void |
P3C.findOutliers(Relation<V> relation,
List<MultivariateGaussianModel> models,
ArrayList<P3C.ClusterCandidate> clusterCandidates,
ModifiableDBIDs noise)
Performs outlier detection by testing the Mahalanobis distance of each
point in a cluster against the critical value of the ChiSquared
distribution with as many degrees of freedom as the cluster has relevant
attributes.
|
private Pair<long[],ArrayModifiableDBIDs> |
DiSH.findParent(Relation<V> relation,
Pair<long[],ArrayModifiableDBIDs> child,
gnu.trove.map.hash.TCustomHashMap<long[],List<ArrayModifiableDBIDs>> clustersMap)
Returns the parent of the specified cluster
|
private DataStore<DoubleDBIDList> |
PROCLUS.getLocalities(DBIDs medoids,
Relation<V> database,
DistanceQuery<V> distFunc,
RangeQuery<V> rangeQuery)
Computes the localities of the specified medoids: for each medoid m the
objects in the sphere centered at m with radius minDist are determined,
where minDist is the minimum distance between medoid m and any other medoid
m_i.
|
private Collection<CLIQUEUnit<V>> |
CLIQUE.initOneDimensionalUnits(Relation<V> database)
Initializes and returns the one dimensional units.
|
private boolean |
DiSH.isParent(Relation<V> relation,
Cluster<SubspaceModel> parent,
Hierarchy.Iter<Cluster<SubspaceModel>> iter,
int db_dim)
Returns true, if the specified parent cluster is a parent of one child of
the children clusters.
|
private Cluster<SubspaceModel> |
DOC.makeCluster(Relation<V> relation,
DBIDs C,
long[] D)
Utility method to create a subspace cluster from a list of DBIDs and the
relevant attributes.
|
private SetDBIDs[][] |
P3C.partitionData(Relation<V> relation,
int bins)
Partition the data set into
bins bins in each dimension
independently. |
Clustering<SubspaceModel> |
DOC.run(Database database,
Relation<V> relation)
Performs the DOC or FastDOC (as configured) algorithm on the given
Database.
|
Clustering<SubspaceModel> |
DiSH.run(Database db,
Relation<V> relation)
Performs the DiSH algorithm on the given database.
|
ClusterOrder |
HiSC.run(Database db,
Relation<V> relation) |
Clustering<SubspaceModel> |
PROCLUS.run(Database database,
Relation<V> relation)
Performs the PROCLUS algorithm on the given database.
|
Clustering<SubspaceModel> |
P3C.run(Database database,
Relation<V> relation)
Performs the P3C algorithm on the given Database.
|
Clustering<SubspaceModel> |
SUBCLU.run(Relation<V> relation)
Performs the SUBCLU algorithm on the given database.
|
Clustering<SubspaceModel> |
CLIQUE.run(Relation<V> relation)
Performs the CLIQUE algorithm on the given database.
|
private List<Cluster<Model>> |
SUBCLU.runDBSCAN(Relation<V> relation,
DBIDs ids,
Subspace subspace)
Runs the DBSCAN algorithm on the specified partition of the database in the
given subspace.
|
private Cluster<SubspaceModel> |
DOC.runDOC(Database database,
Relation<V> relation,
ArrayModifiableDBIDs S,
int d,
int n,
int m,
int r,
int minClusterSize)
Performs a single run of DOC, finding a single cluster.
|
private Cluster<SubspaceModel> |
DOC.runFastDOC(Database database,
Relation<V> relation,
ArrayModifiableDBIDs S,
int d,
int n,
int m,
int r)
Performs a single run of FastDOC, finding a single cluster.
|
private List<Cluster<SubspaceModel>> |
DiSH.sortClusters(Relation<V> relation,
gnu.trove.map.hash.TCustomHashMap<long[],List<ArrayModifiableDBIDs>> clustersMap)
Returns a sorted list of the clusters w.r.t. the subspace dimensionality in
descending order.
|
| Constructor and Description |
|---|
Instance(Database db,
Relation<V> relation)
Constructor.
|
Instance(Database db,
Relation<V> relation)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
private HashMap<String,DBIDs> |
ByLabelClustering.multipleAssignment(Relation<?> data)
Assigns the objects of the database to multiple clusters according to their
labels.
|
Clustering<Model> |
ByLabelHierarchicalClustering.run(Relation<?> relation)
Run the actual clustering algorithm.
|
Clustering<Model> |
TrivialAllNoise.run(Relation<?> relation) |
Clustering<Model> |
ByLabelClustering.run(Relation<?> relation)
Run the actual clustering algorithm.
|
Clustering<Model> |
TrivialAllInOne.run(Relation<?> relation) |
Clustering<Model> |
ByModelClustering.run(Relation<Model> relation)
Run the actual clustering algorithm.
|
private HashMap<String,DBIDs> |
ByLabelClustering.singleAssignment(Relation<?> data)
Assigns the objects of the database to single clusters according to their
labels.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<? extends UncertainObject> |
FDBSCANNeighborPredicate.Instance.relation
The relation holding the uncertain objects.
|
| Modifier and Type | Method and Description |
|---|---|
protected boolean |
UKMeans.assignToNearestCluster(Relation<DiscreteUncertainObject> relation,
List<Vector> means,
List<? extends ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
double[] varsum)
Returns a list of clusters.
|
protected List<Vector> |
UKMeans.means(List<? extends ModifiableDBIDs> clusters,
List<? extends NumberVector> means,
Relation<DiscreteUncertainObject> database)
Returns the mean vectors of the given clusters in the given database.
|
C |
CenterOfMassMetaClustering.run(Database database,
Relation<? extends UncertainObject> relation)
This run method will do the wrapping.
|
Clustering<?> |
RepresentativeUncertainClustering.run(Database database,
Relation<? extends UncertainObject> relation)
This run method will do the wrapping.
|
Clustering<?> |
UKMeans.run(Database database,
Relation<DiscreteUncertainObject> relation)
Run the clustering.
|
| Constructor and Description |
|---|
Instance(double epsilon,
int sampleSize,
double threshold,
Relation<? extends UncertainObject> relation,
RandomFactory rand)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
private FPGrowth.FPTree |
FPGrowth.buildFPTree(Relation<BitVector> relation,
int[] iidx,
int items)
Build the actual FP-tree structure.
|
protected List<OneItemset> |
APRIORI.buildFrequentOneItemsets(Relation<? extends SparseFeatureVector<?>> relation,
int dim,
int needed)
Build the 1-itemsets.
|
protected List<SparseItemset> |
APRIORI.buildFrequentTwoItemsets(List<OneItemset> oneitems,
Relation<BitVector> relation,
int dim,
int needed,
DBIDs ids,
ArrayModifiableDBIDs survivors)
Build the 2-itemsets.
|
private DBIDs[] |
Eclat.buildIndex(Relation<BitVector> relation,
int dim,
int minsupp) |
private int[] |
FPGrowth.countItemSupport(Relation<BitVector> relation,
int dim)
Count the support of each 1-item.
|
protected List<? extends Itemset> |
APRIORI.frequentItemsets(List<? extends Itemset> candidates,
Relation<BitVector> relation,
int needed,
DBIDs ids,
ArrayModifiableDBIDs survivors,
int length)
Returns the frequent BitSets out of the given BitSets with respect to the
given database.
|
protected List<SparseItemset> |
APRIORI.frequentItemsetsSparse(List<SparseItemset> candidates,
Relation<BitVector> relation,
int needed,
DBIDs ids,
ArrayModifiableDBIDs survivors,
int length)
Returns the frequent BitSets out of the given BitSets with respect to the
given database.
|
FrequentItemsetsResult |
FPGrowth.run(Database db,
Relation<BitVector> relation)
Run the FP-Growth algorithm
|
FrequentItemsetsResult |
Eclat.run(Database db,
Relation<BitVector> relation)
Run the Eclat algorithm
|
FrequentItemsetsResult |
APRIORI.run(Relation<BitVector> relation)
Performs the APRIORI algorithm on the given database.
|
| Modifier and Type | Method and Description |
|---|---|
private double |
GaussianUniformMixture.loglikelihoodNormal(DBIDs objids,
Relation<V> database)
Computes the loglikelihood of all normal objects.
|
OutlierResult |
DWOF.run(Database database,
Relation<O> relation)
Performs the Generalized DWOF_SCORE algorithm on the given database by
calling all the other methods in the proper order.
|
OutlierResult |
OPTICSOF.run(Database database,
Relation<O> relation)
Perform OPTICS-based outlier detection.
|
OutlierResult |
SimpleCOP.run(Database database,
Relation<V> data) |
OutlierResult |
COP.run(Relation<V> relation)
Process a single relation.
|
OutlierResult |
GaussianUniformMixture.run(Relation<V> relation)
Run the algorithm
|
OutlierResult |
GaussianModel.run(Relation<V> relation)
Run the algorithm
|
| Modifier and Type | Method and Description |
|---|---|
protected double |
ABOD.computeABOF(Relation<V> relation,
KernelMatrix kernelMatrix,
DBIDRef pA,
MeanVariance s)
Compute the exact ABOF value.
|
OutlierResult |
FastABOD.run(Database db,
Relation<V> relation)
Run Fast-ABOD on the data set.
|
OutlierResult |
LBABOD.run(Database db,
Relation<V> relation)
Run LB-ABOD on the data set.
|
OutlierResult |
ABOD.run(Database db,
Relation<V> relation)
Run ABOD on the data set.
|
| Modifier and Type | Method and Description |
|---|---|
OutlierResult |
KMeansOutlierDetection.run(Database database,
Relation<O> relation)
Run the outlier detection algorithm.
|
OutlierResult |
EMOutlier.run(Database database,
Relation<V> relation)
Runs the algorithm in the timed evaluation part.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<O> |
HilOut.HilbertFeatures.relation
Relation indexed
|
| Modifier and Type | Method and Description |
|---|---|
protected DoubleDBIDList |
ReferenceBasedOutlierDetection.computeDistanceVector(NumberVector refPoint,
Relation<? extends NumberVector> database,
PrimitiveDistanceQuery<? super NumberVector> distFunc)
Computes for each object the distance to one reference point.
|
protected DoubleDataStore |
DBOutlierScore.computeOutlierScores(Database database,
Relation<O> relation,
double d) |
protected DoubleDataStore |
DBOutlierDetection.computeOutlierScores(Database database,
Relation<O> relation,
double neighborhoodSize) |
protected abstract DoubleDataStore |
AbstractDBOutlier.computeOutlierScores(Database database,
Relation<O> relation,
double d)
computes an outlier score for each object of the database.
|
OutlierResult |
ReferenceBasedOutlierDetection.run(Database database,
Relation<? extends NumberVector> relation)
Run the algorithm on the given relation.
|
OutlierResult |
ODIN.run(Database database,
Relation<O> relation)
Run the ODIN algorithm
|
OutlierResult |
KNNOutlier.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
AbstractDBOutlier.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
HilOut.run(Database database,
Relation<O> relation) |
OutlierResult |
KNNWeightOutlier.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
| Constructor and Description |
|---|
HilbertFeatures(Relation<O> relation,
double[] min,
double diameter)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
OutlierResult |
ParallelKNNWeightOutlier.run(Database database,
Relation<O> relation)
Run the parallel kNN weight outlier detector.
|
OutlierResult |
ParallelKNNOutlier.run(Database database,
Relation<O> relation) |
| Modifier and Type | Field and Description |
|---|---|
private Relation<? extends NumberVector> |
ALOCI.ALOCIQuadTree.relation
Relation indexed.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
INFLO.computeINFLO(Relation<O> relation,
ModifiableDBIDs pruned,
WritableDataStore<ModifiableDBIDs> knns,
WritableDataStore<ModifiableDBIDs> rnns,
WritableDoubleDataStore density,
WritableDoubleDataStore inflos,
DoubleMinMax inflominmax)
Compute the final INFLO scores.
|
protected void |
INFLO.computeNeighborhoods(Relation<O> relation,
KNNQuery<O> knnQuery,
ModifiableDBIDs pruned,
WritableDataStore<ModifiableDBIDs> knns,
WritableDataStore<ModifiableDBIDs> rnns,
WritableDoubleDataStore density)
Compute neighborhoods
|
protected void |
LoOP.computePDists(Relation<O> relation,
KNNQuery<O> knn,
WritableDoubleDataStore pdists)
Compute the probabilistic distances used by LoOP.
|
protected double |
LoOP.computePLOFs(Relation<O> relation,
KNNQuery<O> knn,
WritableDoubleDataStore pdists,
WritableDoubleDataStore plofs)
Compute the LOF values, using the pdist distances.
|
private int |
KDEOS.dimensionality(Relation<O> rel)
Ugly hack to allow using this implementation without having a well-defined
dimensionality.
|
protected void |
KDEOS.estimateDensities(Relation<O> rel,
KNNQuery<O> knnq,
DBIDs ids,
WritableDataStore<double[]> densities)
Perform the kernel density estimation step.
|
private Pair<Pair<KNNQuery<O>,KNNQuery<O>>,Pair<RKNNQuery<O>,RKNNQuery<O>>> |
OnlineLOF.getKNNAndRkNNQueries(Database database,
Relation<O> relation,
StepProgress stepprog)
Get the kNN and rkNN queries for the algorithm.
|
private Pair<KNNQuery<O>,KNNQuery<O>> |
FlexibleLOF.getKNNQueries(Database database,
Relation<O> relation,
StepProgress stepprog)
Get the kNN queries for the algorithm.
|
protected Pair<KNNQuery<O>,KNNQuery<O>> |
LoOP.getKNNQueries(Database database,
Relation<O> relation,
StepProgress stepprog)
Get the kNN queries for the algorithm.
|
OutlierResult |
FlexibleLOF.run(Database database,
Relation<O> relation)
Performs the Generalized LOF algorithm on the given database by calling
FlexibleLOF.doRunInTime(de.lmu.ifi.dbs.elki.database.ids.DBIDs, de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery<O>, de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery<O>, de.lmu.ifi.dbs.elki.logging.progress.StepProgress). |
OutlierResult |
OnlineLOF.run(Database database,
Relation<O> relation)
Performs the Generalized LOF_SCORE algorithm on the given database by
calling
#doRunInTime(Database) and adds a OnlineLOF.LOFKNNListener to
the preprocessors. |
OutlierResult |
LOCI.run(Database database,
Relation<O> relation)
Run the algorithm
|
OutlierResult |
LDF.run(Database database,
Relation<O> relation)
Run the naive kernel density LOF algorithm.
|
OutlierResult |
KDEOS.run(Database database,
Relation<O> rel)
Run the KDEOS outlier detection algorithm.
|
OutlierResult |
SimplifiedLOF.run(Database database,
Relation<O> relation)
Run the Simple LOF algorithm.
|
OutlierResult |
LoOP.run(Database database,
Relation<O> relation)
Performs the LoOP algorithm on the given database.
|
OutlierResult |
INFLO.run(Database database,
Relation<O> relation)
Run the algorithm
|
OutlierResult |
COF.run(Database database,
Relation<O> relation)
Runs the COF algorithm on the given database.
|
OutlierResult |
ALOCI.run(Database database,
Relation<O> relation) |
OutlierResult |
LOF.run(Database database,
Relation<O> relation)
Runs the LOF algorithm on the given database.
|
OutlierResult |
LDOF.run(Database database,
Relation<O> relation)
Run the algorithm
|
OutlierResult |
SimpleKernelDensityLOF.run(Database database,
Relation<O> relation)
Run the naive kernel density LOF algorithm.
|
| Constructor and Description |
|---|
ALOCIQuadTree(double[] min,
double[] max,
double[] shift,
int nmin,
Relation<? extends NumberVector> relation)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
OutlierResult |
ParallelLOF.run(Database database,
Relation<O> relation) |
OutlierResult |
ParallelSimplifiedLOF.run(Database database,
Relation<O> relation) |
| Modifier and Type | Method and Description |
|---|---|
private ArrayList<ArrayDBIDs> |
HiCS.buildOneDimIndexes(Relation<? extends NumberVector> relation)
Calculates "index structures" for every attribute, i.e. sorts a
ModifiableArray of every DBID in the database for every dimension and
stores them in a list
|
private void |
HiCS.calculateContrast(Relation<? extends NumberVector> relation,
HiCS.HiCSSubspace subspace,
ArrayList<ArrayDBIDs> subspaceIndex,
Random random)
Calculates the actual contrast of a given subspace.
|
private Set<HiCS.HiCSSubspace> |
HiCS.calculateSubspaces(Relation<? extends NumberVector> relation,
ArrayList<ArrayDBIDs> subspaceIndex,
Random random)
Identifies high contrast subspaces in a given full-dimensional database.
|
OutlierResult |
ExternalDoubleOutlierScore.run(Database database,
Relation<?> relation)
Run the algorithm.
|
OutlierResult |
FeatureBagging.run(Database database,
Relation<NumberVector> relation)
Run the algorithm on a data set.
|
OutlierResult |
HiCS.run(Relation<V> relation)
Perform HiCS on a given database.
|
| Modifier and Type | Method and Description |
|---|---|
OutlierResult |
CTLuMoranScatterplotOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuMoranScatterplotOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
TrimmedMeanApproach.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Run the algorithm.
|
OutlierResult |
TrimmedMeanApproach.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Run the algorithm.
|
OutlierResult |
CTLuMedianAlgorithm.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuMedianAlgorithm.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuZTestOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuZTestOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuScatterplotOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuScatterplotOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
SOF.run(Database database,
Relation<N> spatial,
Relation<O> relation)
The main run method
|
OutlierResult |
SOF.run(Database database,
Relation<N> spatial,
Relation<O> relation)
The main run method
|
OutlierResult |
CTLuMedianMultipleAttributes.run(Database database,
Relation<N> spatial,
Relation<O> attributes)
Run the algorithm
|
OutlierResult |
CTLuMedianMultipleAttributes.run(Database database,
Relation<N> spatial,
Relation<O> attributes)
Run the algorithm
|
OutlierResult |
SLOM.run(Database database,
Relation<N> spatial,
Relation<O> relation) |
OutlierResult |
SLOM.run(Database database,
Relation<N> spatial,
Relation<O> relation) |
OutlierResult |
CTLuMeanMultipleAttributes.run(Database database,
Relation<N> spatial,
Relation<O> attributes)
Run the algorithm
|
OutlierResult |
CTLuMeanMultipleAttributes.run(Database database,
Relation<N> spatial,
Relation<O> attributes)
Run the algorithm
|
OutlierResult |
CTLuGLSBackwardSearchAlgorithm.run(Database database,
Relation<V> relationx,
Relation<? extends NumberVector> relationy)
Run the algorithm
|
OutlierResult |
CTLuGLSBackwardSearchAlgorithm.run(Database database,
Relation<V> relationx,
Relation<? extends NumberVector> relationy)
Run the algorithm
|
OutlierResult |
CTLuRandomWalkEC.run(Relation<N> spatial,
Relation<? extends NumberVector> relation)
Run the algorithm.
|
OutlierResult |
CTLuRandomWalkEC.run(Relation<N> spatial,
Relation<? extends NumberVector> relation)
Run the algorithm.
|
private Pair<DBIDVar,Double> |
CTLuGLSBackwardSearchAlgorithm.singleIteration(Relation<V> relationx,
Relation<? extends NumberVector> relationy)
Run a single iteration of the GLS-SOD modeling step
|
private Pair<DBIDVar,Double> |
CTLuGLSBackwardSearchAlgorithm.singleIteration(Relation<V> relationx,
Relation<? extends NumberVector> relationy)
Run a single iteration of the GLS-SOD modeling step
|
| Modifier and Type | Method and Description |
|---|---|
private DataStore<DBIDs> |
ExtendedNeighborhood.Factory.extendNeighborhood(Database database,
Relation<? extends O> relation)
Method to load the external neighbors.
|
NeighborSetPredicate |
ExternalNeighborhood.Factory.instantiate(Database database,
Relation<?> relation) |
NeighborSetPredicate |
NeighborSetPredicate.Factory.instantiate(Database database,
Relation<? extends O> relation)
Instantiation method.
|
NeighborSetPredicate |
ExtendedNeighborhood.Factory.instantiate(Database database,
Relation<? extends O> relation) |
NeighborSetPredicate |
PrecomputedKNearestNeighborNeighborhood.Factory.instantiate(Database database,
Relation<? extends O> relation) |
private DataStore<DBIDs> |
ExternalNeighborhood.Factory.loadNeighbors(Database database,
Relation<?> relation)
Method to load the external neighbors.
|
| Modifier and Type | Method and Description |
|---|---|
LinearWeightedExtendedNeighborhood |
LinearWeightedExtendedNeighborhood.Factory.instantiate(Database database,
Relation<? extends O> relation) |
UnweightedNeighborhoodAdapter |
UnweightedNeighborhoodAdapter.Factory.instantiate(Database database,
Relation<? extends O> relation) |
WeightedNeighborSetPredicate |
WeightedNeighborSetPredicate.Factory.instantiate(Database database,
Relation<? extends O> relation)
Instantiation method.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<V> |
OUTRES.KernelDensityEstimator.relation
Relation to retrieve data from
|
| Modifier and Type | Method and Description |
|---|---|
protected ArrayList<ArrayList<DBIDs>> |
AbstractAggarwalYuOutlier.buildRanges(Relation<V> relation)
Grid discretization of the data:
Each attribute of data is divided into phi equi-depth ranges. |
private static double[] |
SOD.computePerDimensionVariances(Relation<? extends NumberVector> relation,
Vector center,
DBIDs neighborhood)
Compute the per-dimension variances for the given neighborhood and center.
|
private DBIDs |
SOD.getNearestNeighbors(Relation<V> relation,
SimilarityQuery<V> simQ,
DBIDRef queryObject)
Get the k nearest neighbors in terms of the shared nearest neighbor
distance.
|
OutlierResult |
AggarwalYuEvolutionary.run(Database database,
Relation<V> relation)
Performs the evolutionary algorithm on the given database.
|
OutlierResult |
SOD.run(Relation<V> relation)
Performs the SOD algorithm on the given database.
|
OutlierResult |
AggarwalYuNaive.run(Relation<V> relation)
Run the algorithm on the given relation.
|
OutlierResult |
OUTRES.run(Relation<V> relation)
Main loop for OUTRES
|
| Constructor and Description |
|---|
EvolutionarySearch(Relation<V> relation,
ArrayList<ArrayList<DBIDs>> ranges,
int m,
Random random)
Constructor.
|
KernelDensityEstimator(Relation<V> relation)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
OutlierResult |
LibSVMOneClassOutlierDetection.run(Relation<V> relation)
Run one-class SVM.
|
| Modifier and Type | Method and Description |
|---|---|
OutlierResult |
ByLabelOutlier.run(Relation<?> relation)
Run the algorithm
|
OutlierResult |
TrivialNoOutlier.run(Relation<?> relation)
Run the actual algorithm.
|
OutlierResult |
TrivialAllOutlier.run(Relation<?> relation)
Run the actual algorithm.
|
OutlierResult |
TrivialAverageCoordinateOutlier.run(Relation<? extends NumberVector> relation)
Run the actual algorithm.
|
OutlierResult |
TrivialGeneratedOutlier.run(Relation<Model> models,
Relation<NumberVector> vecs,
Relation<?> labels)
Run the algorithm
|
OutlierResult |
TrivialGeneratedOutlier.run(Relation<Model> models,
Relation<NumberVector> vecs,
Relation<?> labels)
Run the algorithm
|
OutlierResult |
TrivialGeneratedOutlier.run(Relation<Model> models,
Relation<NumberVector> vecs,
Relation<?> labels)
Run the algorithm
|
| Modifier and Type | Method and Description |
|---|---|
private void |
EvaluateRetrievalPerformance.computeDistances(ModifiableDoubleDBIDList nlist,
DBIDIter query,
DistanceQuery<O> distQuery,
Relation<O> relation)
Compute the distances to the neighbor objects.
|
protected double |
HopkinsStatisticClusteringTendency.computeNNForRealData(KNNQuery<NumberVector> knnQuery,
Relation<NumberVector> relation,
int dim)
Search nearest neighbors for real data members.
|
void |
EvaluateRetrievalPerformance.KNNEvaluator.evaluateKNN(double[] knnperf,
ModifiableDoubleDBIDList nlist,
Relation<?> lrelation,
gnu.trove.map.hash.TObjectIntHashMap<Object> counters,
Object label)
Evaluate by simulating kNN classification for k=1...maxk
|
private DoubleMinMax |
DistanceStatisticsWithClasses.exactMinMax(Relation<O> relation,
DistanceQuery<O> distFunc)
Compute the exact maximum and minimum.
|
private void |
EvaluateRetrievalPerformance.findMatches(ModifiableDBIDs posn,
Relation<?> lrelation,
Object label)
Find all matching objects.
|
protected void |
HopkinsStatisticClusteringTendency.initializeDataExtends(Relation<NumberVector> relation,
int dim,
double[] min,
double[] extend)
Initialize the uniform sampling area.
|
Result |
HopkinsStatisticClusteringTendency.run(Database database,
Relation<NumberVector> relation)
Runs the algorithm in the timed evaluation part.
|
Result |
EstimateIntrinsicDimensionality.run(Database database,
Relation<O> relation) |
HistogramResult<DoubleVector> |
RankingQualityHistogram.run(Database database,
Relation<O> relation)
Process a database
|
CollectionResult<DoubleVector> |
AveragePrecisionAtK.run(Database database,
Relation<O> relation,
Relation<?> lrelation)
Run the algorithm
|
CollectionResult<DoubleVector> |
AveragePrecisionAtK.run(Database database,
Relation<O> relation,
Relation<?> lrelation)
Run the algorithm
|
EvaluateRetrievalPerformance.RetrievalPerformanceResult |
EvaluateRetrievalPerformance.run(Database database,
Relation<O> relation,
Relation<?> lrelation)
Run the algorithm
|
EvaluateRetrievalPerformance.RetrievalPerformanceResult |
EvaluateRetrievalPerformance.run(Database database,
Relation<O> relation,
Relation<?> lrelation)
Run the algorithm
|
private ScalesResult |
AddSingleScale.run(Relation<? extends NumberVector> rel)
Add scales to a single vector relation.
|
private DoubleMinMax |
DistanceStatisticsWithClasses.sampleMinMax(Relation<O> relation,
DistanceQuery<O> distFunc)
Estimate minimum and maximum via sampling.
|
| Modifier and Type | Method and Description |
|---|---|
static Relation<NumberVector> |
GreedyEnsembleExperiment.applyPrescaling(ScalingFunction scaling,
Relation<NumberVector> relation,
DBIDs skip)
Prescale each vector (except when in
skip) with the given scaling
function. |
| Modifier and Type | Method and Description |
|---|---|
static Relation<NumberVector> |
GreedyEnsembleExperiment.applyPrescaling(ScalingFunction scaling,
Relation<NumberVector> relation,
DBIDs skip)
Prescale each vector (except when in
skip) with the given scaling
function. |
| Modifier and Type | Field and Description |
|---|---|
private Relation<? extends NumberVector> |
VectorUtil.SortDBIDsBySingleDimension.data
The relation to sort.
|
| Modifier and Type | Method and Description |
|---|---|
static Vector |
VectorUtil.computeMedoid(Relation<? extends NumberVector> relation,
DBIDs sample)
Compute medoid for a given subset.
|
| Constructor and Description |
|---|
SortDBIDsBySingleDimension(Relation<? extends NumberVector> data)
Constructor.
|
SortDBIDsBySingleDimension(Relation<? extends NumberVector> data,
int dim)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
static NumberVector |
ModelUtil.getPrototype(Model model,
Relation<? extends NumberVector> relation)
Get the representative vector for a cluster model.
|
static <V extends NumberVector> |
ModelUtil.getPrototype(Model model,
Relation<? extends V> relation,
NumberVector.Factory<V> factory)
Get (and convert!)
|
static NumberVector |
ModelUtil.getPrototypeOrCentroid(Model model,
Relation<? extends NumberVector> relation,
DBIDs ids)
Get the representative vector for a cluster model, or compute the centroid.
|
static <V extends NumberVector> |
ModelUtil.getPrototypeOrCentroid(Model model,
Relation<? extends V> relation,
DBIDs ids,
NumberVector.Factory<V> factory)
Get the representative vector for a cluster model, or compute the centroid.
|
| Constructor and Description |
|---|
CorrelationAnalysisSolution(LinearEquationSystem solution,
Relation<V> db,
Matrix strongEigenvectors,
Matrix weakEigenvectors,
Matrix similarityMatrix,
Vector centroid)
Provides a new CorrelationAnalysisSolution holding the specified matrix.
|
CorrelationAnalysisSolution(LinearEquationSystem solution,
Relation<V> db,
Matrix strongEigenvectors,
Matrix weakEigenvectors,
Matrix similarityMatrix,
Vector centroid,
NumberFormat nf)
Provides a new CorrelationAnalysisSolution holding the specified matrix and
number format.
|
| Modifier and Type | Field and Description |
|---|---|
protected List<Relation<?>> |
AbstractDatabase.relations
The relations we manage.
|
| Modifier and Type | Method and Description |
|---|---|
private Relation<?> |
HashmapDatabase.addNewRelation(SimpleTypeInformation<?> meta)
Add a new representation for the given meta.
|
protected Relation<?>[] |
HashmapDatabase.alignColumns(ObjectBundle pack)
Find a mapping from package columns to database columns, eventually adding
new database columns when needed.
|
<O> Relation<O> |
Database.getRelation(TypeInformation restriction,
Object... hints)
Get an object representation.
|
<O> Relation<O> |
AbstractDatabase.getRelation(TypeInformation restriction,
Object... hints) |
| Modifier and Type | Method and Description |
|---|---|
Collection<Relation<?>> |
Database.getRelations()
Get all relations of a database.
|
Collection<Relation<?>> |
AbstractDatabase.getRelations() |
| Modifier and Type | Method and Description |
|---|---|
void |
ProxyDatabase.addRelation(Relation<?> relation)
Add a new representation.
|
<O> DistanceQuery<O> |
Database.getDistanceQuery(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
Object... hints)
Get the distance query for a particular distance function.
|
<O> DistanceQuery<O> |
AbstractDatabase.getDistanceQuery(Relation<O> objQuery,
DistanceFunction<? super O> distanceFunction,
Object... hints) |
static <O> KNNQuery<O> |
QueryUtil.getKNNQuery(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
Object... hints)
Get a KNN query object for the given distance function.
|
static <O> RangeQuery<O> |
QueryUtil.getRangeQuery(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
Object... hints)
Get a range query object for the given distance function for radius-based
neighbor search.
|
static <O> RKNNQuery<O> |
QueryUtil.getRKNNQuery(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
Object... hints)
Get a rKNN query object for the given distance function.
|
<O> SimilarityQuery<O> |
Database.getSimilarityQuery(Relation<O> relation,
SimilarityFunction<? super O> similarityFunction,
Object... hints)
Get the similarity query for a particular similarity function.
|
<O> SimilarityQuery<O> |
AbstractDatabase.getSimilarityQuery(Relation<O> objQuery,
SimilarityFunction<? super O> similarityFunction,
Object... hints) |
| Constructor and Description |
|---|
ProxyDatabase(DBIDs ids,
Relation<?>... relations)
Constructor.
|
| Constructor and Description |
|---|
ProxyDatabase(DBIDs ids,
Iterable<Relation<?>> relations)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends O> |
AbstractDistanceQuery.relation
The data to use for this query
|
| Modifier and Type | Method and Description |
|---|---|
Relation<? extends O> |
DistanceQuery.getRelation()
Access the underlying data query.
|
Relation<? extends O> |
AbstractDistanceQuery.getRelation() |
| Constructor and Description |
|---|
AbstractDatabaseDistanceQuery(Relation<? extends O> relation)
Constructor.
|
AbstractDistanceQuery(Relation<? extends O> relation)
Constructor.
|
DBIDDistanceQuery(Relation<DBID> relation,
DBIDDistanceFunction distanceFunction)
Constructor.
|
DBIDRangeDistanceQuery(Relation<DBID> relation,
DBIDRangeDistanceFunction distanceFunction)
Constructor.
|
PrimitiveDistanceQuery(Relation<? extends O> relation,
PrimitiveDistanceFunction<? super O> distanceFunction)
Constructor.
|
PrimitiveDistanceSimilarityQuery(Relation<? extends O> relation,
PrimitiveDistanceFunction<? super O> distanceFunction,
PrimitiveSimilarityFunction<? super O> similarityFunction)
Constructor.
|
SpatialPrimitiveDistanceQuery(Relation<? extends V> relation,
SpatialPrimitiveDistanceFunction<? super V> distanceFunction) |
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends O> |
PreprocessorKNNQuery.relation
The data to use for this query
|
protected Relation<? extends O> |
AbstractDistanceKNNQuery.relation
The data to use for this query
|
| Modifier and Type | Method and Description |
|---|---|
private KNNHeap |
LinearScanPrimitiveDistanceKNNQuery.linearScan(Relation<? extends O> relation,
DBIDIter iter,
O obj,
KNNHeap heap)
Main loop of the linear scan.
|
private KNNHeap |
LinearScanEuclideanDistanceKNNQuery.linearScan(Relation<? extends O> relation,
DBIDIter iter,
O obj,
KNNHeap heap)
Main loop of the linear scan.
|
| Constructor and Description |
|---|
PreprocessorKNNQuery(Relation<O> relation,
AbstractMaterializeKNNPreprocessor<O> preprocessor)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends O> |
AbstractDistanceRangeQuery.relation
The data to use for this query
|
| Modifier and Type | Method and Description |
|---|---|
private void |
LinearScanPrimitiveDistanceRangeQuery.linearScan(Relation<? extends O> relation,
DBIDIter iter,
O obj,
double range,
ModifiableDoubleDBIDList result)
Main loop for linear scan,
|
private void |
LinearScanEuclideanDistanceRangeQuery.linearScan(Relation<? extends O> relation,
DBIDIter iter,
O obj,
double range,
ModifiableDoubleDBIDList result)
Main loop for linear scan,
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends O> |
PreprocessorRKNNQuery.relation
The data to use for this query
|
protected Relation<? extends O> |
AbstractRKNNQuery.relation
The data to use for this query
|
| Constructor and Description |
|---|
PreprocessorRKNNQuery(Relation<O> database,
MaterializeKNNAndRKNNPreprocessor.Factory<O> preprocessor)
Constructor.
|
PreprocessorRKNNQuery(Relation<O> relation,
MaterializeKNNAndRKNNPreprocessor<O> preprocessor)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends O> |
AbstractSimilarityQuery.relation
The data to use for this query
|
| Modifier and Type | Method and Description |
|---|---|
Relation<? extends O> |
AbstractSimilarityQuery.getRelation() |
Relation<? extends O> |
SimilarityQuery.getRelation()
Access the underlying data query.
|
| Constructor and Description |
|---|
AbstractDBIDSimilarityQuery(Relation<? extends O> relation)
Constructor.
|
AbstractSimilarityQuery(Relation<? extends O> relation)
Constructor.
|
PrimitiveSimilarityQuery(Relation<? extends O> relation,
PrimitiveSimilarityFunction<? super O> similarityFunction)
Constructor.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
DoubleRelation
Interface for double-valued relations.
|
interface |
ModifiableRelation<O>
Relations that allow modification.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractRelation<O>
Abstract base class for relations.
|
class |
ConvertToStringView
Representation adapter that uses toString() to produce a string
representation.
|
class |
DBIDView
Pseudo-representation that is the object ID itself.
|
class |
MaterializedDoubleRelation
Represents a single representation.
|
class |
MaterializedRelation<O>
Represents a single representation.
|
class |
ProjectedView<IN,OUT>
Projected relation view (non-materialized)
|
class |
ProxyView<O>
A virtual partitioning of the database.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<? extends O> |
RelationUtil.RelationObjectIterator.database
The database we use.
|
(package private) Relation<? extends O> |
RelationUtil.CollectionFromRelation.db
The database we query.
|
(package private) Relation<?> |
ConvertToStringView.existing
The database we use
|
private Relation<O> |
ProxyView.inner
The wrapped representation where we get the IDs from.
|
private Relation<IN> |
ProjectedView.inner
The wrapped representation where we get the IDs from.
|
| Modifier and Type | Method and Description |
|---|---|
static <V extends NumberVector,T extends NumberVector> |
RelationUtil.relationUglyVectorCast(Relation<T> database)
An ugly vector type cast unavoidable in some situations due to Generics.
|
| Modifier and Type | Method and Description |
|---|---|
static <V extends FeatureVector<?>> |
RelationUtil.assumeVectorField(Relation<V> relation)
Get the vector field type information from a relation.
|
static double[][] |
RelationUtil.computeMinMax(Relation<? extends NumberVector> relation)
Determines the minimum and maximum values in each dimension of all objects
stored in the given database.
|
static int |
RelationUtil.dimensionality(Relation<? extends SpatialComparable> relation)
Get the dimensionality of a database relation.
|
static <V extends SpatialComparable> |
RelationUtil.getColumnLabel(Relation<? extends V> rel,
int col)
Get the column name or produce a generic label "Column XY".
|
static <V extends NumberVector> |
RelationUtil.getNumberVectorFactory(Relation<V> relation)
Get the number vector factory of a database relation.
|
static double[][] |
RelationUtil.relationAsMatrix(Relation<? extends NumberVector> relation,
ArrayDBIDs ids)
Copy a relation into a double matrix.
|
static <V extends NumberVector,T extends NumberVector> |
RelationUtil.relationUglyVectorCast(Relation<T> database)
An ugly vector type cast unavoidable in some situations due to Generics.
|
static double[] |
RelationUtil.variances(Relation<? extends NumberVector> database,
NumberVector centroid,
DBIDs ids)
Determines the variances in each dimension of the specified objects stored
in the given database.
|
| Constructor and Description |
|---|
CollectionFromRelation(Relation<? extends O> db)
Constructor.
|
ConvertToStringView(Relation<?> existing)
Constructor.
|
ProjectedView(Relation<IN> inner,
Projection<IN,OUT> projection)
Constructor.
|
ProxyView(DBIDs idview,
Relation<O> inner)
Constructor.
|
RelationObjectIterator(DBIDIter iter,
Relation<? extends O> database)
Full Constructor.
|
RelationObjectIterator(Relation<? extends O> database)
Simplified constructor.
|
| Modifier and Type | Method and Description |
|---|---|
<O extends DBID> |
AbstractDBIDRangeDistanceFunction.instantiate(Relation<O> database) |
<T extends NumberVector> |
AbstractSpatialNorm.instantiate(Relation<T> relation) |
<T extends NumberVector> |
AbstractSpatialDistanceFunction.instantiate(Relation<T> relation) |
<T extends DBID> |
RandomStableDistanceFunction.instantiate(Relation<T> relation) |
<T extends O> |
SharedNearestNeighborJaccardDistanceFunction.instantiate(Relation<T> database) |
<T extends O> |
AbstractPrimitiveDistanceFunction.instantiate(Relation<T> relation)
Instantiate with a database to get the actual distance query.
|
<T extends O> |
DistanceFunction.instantiate(Relation<T> relation)
Instantiate with a database to get the actual distance query.
|
<T extends V> |
SpatialPrimitiveDistanceFunction.instantiate(Relation<T> relation) |
| Constructor and Description |
|---|
Instance(Relation<O> database,
DistanceFunction<? super O> parent)
Constructor.
|
Instance(Relation<O> database,
I index,
F parent)
Constructor.
|
Instance(Relation<T> database,
SharedNearestNeighborIndex<T> preprocessor,
SharedNearestNeighborJaccardDistanceFunction<T> parent)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
<T extends O> |
ArccosSimilarityAdapter.instantiate(Relation<T> database) |
<T extends O> |
LnSimilarityAdapter.instantiate(Relation<T> database) |
<T extends O> |
LinearAdapterLinear.instantiate(Relation<T> database) |
abstract <T extends O> |
AbstractSimilarityAdapter.instantiate(Relation<T> database) |
| Constructor and Description |
|---|
Instance(Relation<O> database,
DistanceFunction<? super O> parent,
SimilarityQuery<? super O> similarityQuery)
Constructor.
|
Instance(Relation<O> database,
DistanceFunction<? super O> parent,
SimilarityQuery<? super O> similarityQuery)
Constructor.
|
Instance(Relation<O> database,
DistanceFunction<? super O> parent,
SimilarityQuery<O> similarityQuery)
Constructor.
|
Instance(Relation<O> database,
DistanceFunction<? super O> parent,
SimilarityQuery<O> similarityQuery)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
<O extends DBID> |
FileBasedFloatDistanceFunction.instantiate(Relation<O> database) |
<O extends DBID> |
FileBasedDoubleDistanceFunction.instantiate(Relation<O> database) |
| Modifier and Type | Method and Description |
|---|---|
<T extends NumberVector> |
HellingerDistanceFunction.instantiate(Relation<T> database) |
| Modifier and Type | Method and Description |
|---|---|
<T extends O> |
JaccardSimilarityDistanceFunction.instantiate(Relation<T> relation) |
| Modifier and Type | Method and Description |
|---|---|
<T extends NumberVector> |
SubspaceLPNormDistanceFunction.instantiate(Relation<T> database) |
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends DBID> |
AbstractDBIDSimilarityFunction.database
The database we work on
|
| Modifier and Type | Method and Description |
|---|---|
<T extends O> |
FractionalSharedNearestNeighborSimilarityFunction.instantiate(Relation<T> database) |
<T extends O> |
AbstractPrimitiveSimilarityFunction.instantiate(Relation<T> relation) |
<T extends O> |
IndexBasedSimilarityFunction.instantiate(Relation<T> database)
Preprocess the database to get the actual distance function.
|
abstract <T extends O> |
AbstractIndexBasedSimilarityFunction.instantiate(Relation<T> database) |
<T extends O> |
SharedNearestNeighborSimilarityFunction.instantiate(Relation<T> database) |
<T extends O> |
SimilarityFunction.instantiate(Relation<T> relation)
Instantiate with a representation to get the actual similarity query.
|
| Constructor and Description |
|---|
AbstractDBIDSimilarityFunction(Relation<? extends DBID> database)
Constructor.
|
Instance(Relation<O> database,
I index)
Constructor.
|
Instance(Relation<O> database,
SharedNearestNeighborIndex<O> preprocessor,
SharedNearestNeighborSimilarityFunction<? super O> similarityFunction)
Constructor.
|
Instance(Relation<T> database,
SharedNearestNeighborIndex<T> preprocessor,
FractionalSharedNearestNeighborSimilarityFunction<? super T> similarityFunction)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
<T extends Cluster<?>> |
ClusterIntersectionSimilarityFunction.instantiate(Relation<T> relation) |
<T extends Cluster<?>> |
ClusterJaccardSimilarityFunction.instantiate(Relation<T> relation) |
<T extends Clustering<?>> |
ClusteringBCubedF1SimilarityFunction.instantiate(Relation<T> relation) |
<T extends Clustering<?>> |
ClusteringDistanceSimilarityFunction.instantiate(Relation<T> relation) |
<T extends Clustering<?>> |
ClusteringRandIndexSimilarityFunction.instantiate(Relation<T> relation) |
<T extends Clustering<?>> |
ClusteringFowlkesMallowsSimilarityFunction.instantiate(Relation<T> relation) |
<T extends Clustering<?>> |
ClusteringAdjustedRandIndexSimilarityFunction.instantiate(Relation<T> relation) |
| Modifier and Type | Method and Description |
|---|---|
<T extends NumberVector> |
PolynomialKernelFunction.instantiate(Relation<T> database) |
| Constructor and Description |
|---|
KernelMatrix(PrimitiveSimilarityFunction<? super O> kernelFunction,
Relation<? extends O> relation,
DBIDs ids)
Provides a new kernel matrix.
|
KernelMatrix(SimilarityQuery<? super O> kernelFunction,
Relation<? extends O> relation,
DBIDs ids)
Provides a new kernel matrix.
|
| Modifier and Type | Method and Description |
|---|---|
static int |
EvaluateSimplifiedSilhouette.centroids(Relation<? extends NumberVector> rel,
List<? extends Cluster<?>> clusters,
NumberVector[] centroids,
NoiseHandling noiseOption)
Compute centroids.
|
protected double[] |
EvaluateConcordantPairs.computeWithinDistances(Relation<? extends NumberVector> rel,
List<? extends Cluster<?>> clusters,
int withinPairs) |
double |
EvaluateSimplifiedSilhouette.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateVarianceRatioCriteria.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateConcordantPairs.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateDaviesBouldin.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 |
EvaluatePBMIndex.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateCIndex.evaluateClustering(Database db,
Relation<? extends O> rel,
DistanceQuery<O> dq,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateSilhouette.evaluateClustering(Database db,
Relation<O> rel,
DistanceQuery<O> dq,
Clustering<?> c)
Evaluate a single clustering.
|
static int |
EvaluateVarianceRatioCriteria.globalCentroid(Centroid overallCentroid,
Relation<? extends NumberVector> rel,
List<? extends Cluster<?>> clusters,
NumberVector[] centroids,
NoiseHandling noiseOption)
Update the global centroid.
|
double[] |
EvaluateDaviesBouldin.withinGroupDistances(Relation<? extends NumberVector> rel,
List<? extends Cluster<?>> clusters,
NumberVector[] centroids) |
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<?> |
ComputeSimilarityMatrixImage.SimilarityMatrix.relation
The database
|
| Modifier and Type | Method and Description |
|---|---|
Relation<?> |
ComputeSimilarityMatrixImage.SimilarityMatrix.getRelation()
Get the relation
|
| Modifier and Type | Method and Description |
|---|---|
private ComputeSimilarityMatrixImage.SimilarityMatrix |
ComputeSimilarityMatrixImage.computeSimilarityMatrixImage(Relation<O> relation,
DBIDIter iter)
Compute the actual similarity image.
|
| Constructor and Description |
|---|
SimilarityMatrix(RenderedImage img,
Relation<?> relation,
ArrayDBIDs ids)
Constructor
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<O> |
AbstractIndex.relation
The representation we are bound to.
|
| Modifier and Type | Method and Description |
|---|---|
I |
IndexFactory.instantiate(Relation<V> relation)
Sets the database in the distance function of this index (if existing).
|
| Constructor and Description |
|---|
AbstractIndex(Relation<O> relation)
Constructor.
|
AbstractRefiningIndex(Relation<O> relation)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
Relation<? extends O> |
PrecomputedDistanceMatrix.PrecomputedDistanceQuery.getRelation() |
| Modifier and Type | Method and Description |
|---|---|
PrecomputedDistanceMatrix<O> |
PrecomputedDistanceMatrix.Factory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
PrecomputedDistanceMatrix(Relation<O> relation,
DistanceFunction<? super O> distanceFunction)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
InMemoryIDistanceIndex<V> |
InMemoryIDistanceIndex.Factory.instantiate(Relation<V> relation) |
| Constructor and Description |
|---|
InMemoryIDistanceIndex(Relation<O> relation,
DistanceQuery<O> distance,
KMedoidsInitialization<O> initialization,
int numref)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
InMemoryInvertedIndex<V> |
InMemoryInvertedIndex.Factory.instantiate(Relation<V> relation) |
| Constructor and Description |
|---|
InMemoryInvertedIndex(Relation<V> relation)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
InMemoryLSHIndex.Instance |
InMemoryLSHIndex.instantiate(Relation<V> relation) |
| Constructor and Description |
|---|
Instance(Relation<V> relation,
ArrayList<? extends LocalitySensitiveHashFunction<? super V>> hashfunctions,
int numberOfBuckets)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
ArrayList<? extends LocalitySensitiveHashFunction<? super NumberVector>> |
CosineHashFunctionFamily.generateHashFunctions(Relation<? extends NumberVector> relation,
int l) |
ArrayList<? extends LocalitySensitiveHashFunction<? super NumberVector>> |
AbstractProjectedHashFunctionFamily.generateHashFunctions(Relation<? extends NumberVector> relation,
int l) |
ArrayList<? extends LocalitySensitiveHashFunction<? super V>> |
LocalitySensitiveHashFunctionFamily.generateHashFunctions(Relation<? extends V> relation,
int l)
Generate hash functions for the given relation.
|
| Modifier and Type | Method and Description |
|---|---|
I |
LocalProjectionIndex.Factory.instantiate(Relation<V> relation)
Instantiate the index for a given database.
|
| Constructor and Description |
|---|
AbstractPreprocessorIndex(Relation<O> relation)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<V> |
RandomProjectedNeighborssAndDensities.points
entire point set
|
| Modifier and Type | Method and Description |
|---|---|
void |
RandomProjectedNeighborssAndDensities.computeSetsBounds(Relation<V> points,
int minSplitSize,
DBIDs ptList)
Create random projections, project points and put points into sets of size
about minSplitSize/2
|
| Modifier and Type | Method and Description |
|---|---|
private MetricalIndexTree<O,N,E> |
MetricalIndexApproximationMaterializeKNNPreprocessor.getMetricalIndex(Relation<O> relation)
Do some (limited) type checking, then cast the database into a spatial
database.
|
SpatialApproximationMaterializeKNNPreprocessor<NumberVector,N,E> |
SpatialApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<NumberVector> relation) |
MetricalIndexApproximationMaterializeKNNPreprocessor<O,N,E> |
MetricalIndexApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
MaterializeKNNPreprocessor<O> |
MaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
abstract AbstractMaterializeKNNPreprocessor<O> |
AbstractMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
RandomSampleKNNPreprocessor<O> |
RandomSampleKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
MaterializeKNNAndRKNNPreprocessor<O> |
MaterializeKNNAndRKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
CachedDoubleDistanceKNNPreprocessor<O> |
CachedDoubleDistanceKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
PartitionApproximationMaterializeKNNPreprocessor<O> |
PartitionApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
KNNJoinMaterializeKNNPreprocessor<O> |
KNNJoinMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation) |
SpacefillingKNNPreprocessor<V> |
SpacefillingKNNPreprocessor.Factory.instantiate(Relation<V> relation) |
SpacefillingMaterializeKNNPreprocessor<V> |
SpacefillingMaterializeKNNPreprocessor.Factory.instantiate(Relation<V> relation) |
NaiveProjectedKNNPreprocessor<V> |
NaiveProjectedKNNPreprocessor.Factory.instantiate(Relation<V> relation) |
| Constructor and Description |
|---|
AbstractMaterializeKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k)
Constructor.
|
CachedDoubleDistanceKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k,
File file)
Constructor.
|
KNNJoinMaterializeKNNPreprocessor(Relation<V> relation,
DistanceFunction<? super V> distanceFunction,
int k)
Constructor.
|
MaterializeKNNAndRKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k)
Constructor.
|
MaterializeKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k)
Constructor with preprocessing step.
|
MetricalIndexApproximationMaterializeKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k)
Constructor
|
NaiveProjectedKNNPreprocessor(Relation<O> relation,
double window,
int projections,
RandomProjectionFamily proj,
Random random)
Constructor.
|
PartitionApproximationMaterializeKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k,
int partitions,
RandomFactory rnd)
Constructor
|
RandomSampleKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k,
double share,
RandomFactory rnd)
Constructor.
|
SpacefillingKNNPreprocessor(Relation<O> relation,
List<SpatialSorter> curvegen,
double window,
int variants,
int odim,
RandomProjectionFamily proj,
Random random)
Constructor.
|
SpacefillingMaterializeKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k,
List<SpatialSorter> curvegen,
double window,
int variants,
Random random)
Constructor.
|
SpatialApproximationMaterializeKNNPreprocessor(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
int k)
Constructor
|
| Modifier and Type | Method and Description |
|---|---|
abstract I |
AbstractFilteredPCAIndex.Factory.instantiate(Relation<NV> relation) |
I |
FilteredLocalPCAIndex.Factory.instantiate(Relation<NV> relation)
Instantiate the index for a given database.
|
KNNQueryFilteredPCAIndex<V> |
KNNQueryFilteredPCAIndex.Factory.instantiate(Relation<V> relation) |
| Constructor and Description |
|---|
AbstractFilteredPCAIndex(Relation<NV> relation,
PCAFilteredRunner pca)
Constructor.
|
KNNQueryFilteredPCAIndex(Relation<NV> relation,
PCAFilteredRunner pca,
KNNQuery<NV> knnQuery,
int k)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
private long[] |
HiSCPreferenceVectorIndex.determinePreferenceVector(Relation<V> relation,
DBIDRef id,
DBIDs neighborIDs,
StringBuilder msg)
Determines the preference vector according to the specified neighbor ids.
|
private long[] |
DiSHPreferenceVectorIndex.determinePreferenceVector(Relation<V> relation,
ModifiableDBIDs[] neighborIDs,
StringBuilder msg)
Determines the preference vector according to the specified neighbor ids.
|
private long[] |
DiSHPreferenceVectorIndex.determinePreferenceVectorByApriori(Relation<V> relation,
ModifiableDBIDs[] neighborIDs,
StringBuilder msg)
Determines the preference vector with the apriori strategy.
|
private RangeQuery<V>[] |
DiSHPreferenceVectorIndex.initRangeQueries(Relation<V> relation,
int dimensionality)
Initializes the dimension selecting distancefunctions to determine the
preference vectors.
|
HiSCPreferenceVectorIndex<V> |
HiSCPreferenceVectorIndex.Factory.instantiate(Relation<V> relation) |
abstract I |
AbstractPreferenceVectorIndex.Factory.instantiate(Relation<V> relation) |
DiSHPreferenceVectorIndex<V> |
DiSHPreferenceVectorIndex.Factory.instantiate(Relation<V> relation) |
I |
PreferenceVectorIndex.Factory.instantiate(Relation<V> relation)
Instantiate the index for a given database.
|
| Constructor and Description |
|---|
AbstractPreferenceVectorIndex(Relation<NV> relation)
Constructor.
|
DiSHPreferenceVectorIndex(Relation<V> relation,
double[] epsilon,
int minpts,
DiSHPreferenceVectorIndex.Strategy strategy)
Constructor.
|
HiSCPreferenceVectorIndex(Relation<V> relation,
double alpha,
int k)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
I |
SharedNearestNeighborIndex.Factory.instantiate(Relation<O> database)
Instantiate the index for a given database.
|
SharedNearestNeighborPreprocessor<O> |
SharedNearestNeighborPreprocessor.Factory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
SharedNearestNeighborPreprocessor(Relation<O> relation,
int numberOfNeighbors,
DistanceFunction<O> distanceFunction)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<O> |
ProjectedIndex.relation
The relation we predend to index.
|
(package private) Relation<I> |
ProjectedIndex.view
The view that we really index.
|
| Modifier and Type | Method and Description |
|---|---|
ProjectedIndex<O,O> |
LatLngAsECEFIndex.Factory.instantiate(Relation<O> relation) |
ProjectedIndex<O,I> |
ProjectedIndex.Factory.instantiate(Relation<O> relation) |
ProjectedIndex<O,O> |
LngLatAsECEFIndex.Factory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
LatLngAsECEFIndex(Relation<O> relation,
Projection<O,O> proj,
Relation<O> view,
Index inner,
boolean norefine)
Constructor.
|
LatLngAsECEFIndex(Relation<O> relation,
Projection<O,O> proj,
Relation<O> view,
Index inner,
boolean norefine)
Constructor.
|
LngLatAsECEFIndex(Relation<O> relation,
Projection<O,O> proj,
Relation<O> view,
Index inner,
boolean norefine)
Constructor.
|
LngLatAsECEFIndex(Relation<O> relation,
Projection<O,O> proj,
Relation<O> view,
Index inner,
boolean norefine)
Constructor.
|
ProjectedIndex(Relation<O> relation,
Projection<O,I> proj,
Relation<I> view,
Index inner,
boolean norefine,
double kmulti)
Constructor.
|
ProjectedIndex(Relation<O> relation,
Projection<O,I> proj,
Relation<I> view,
Index inner,
boolean norefine,
double kmulti)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
CoverTree<O> |
CoverTree.Factory.instantiate(Relation<O> relation) |
SimplifiedCoverTree<O> |
SimplifiedCoverTree.Factory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
AbstractCoverTree(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
double expansion,
int truncate)
Constructor.
|
CoverTree(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
double expansion,
int truncate)
Constructor.
|
SimplifiedCoverTree(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
double expansion,
int truncate)
Constructor.
|
| Constructor and Description |
|---|
AbstractMkTree(Relation<O> relation,
PageFile<N> pagefile,
S settings)
Constructor.
|
AbstractMkTreeUnified(Relation<O> relation,
PageFile<N> pagefile,
S settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
MkAppTreeIndex.relation
The relation indexed
|
| Modifier and Type | Method and Description |
|---|---|
MkAppTreeIndex<O> |
MkAppTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
MkAppTree(Relation<O> relation,
PageFile<MkAppTreeNode<O>> pageFile,
MkAppTreeSettings<O> settings)
Constructor.
|
MkAppTreeIndex(Relation<O> relation,
PageFile<MkAppTreeNode<O>> pageFile,
MkAppTreeSettings<O> settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
MkCoPTreeIndex.relation
Relation indexed
|
| Modifier and Type | Method and Description |
|---|---|
MkCoPTreeIndex<O> |
MkCopTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
MkCoPTree(Relation<O> relation,
PageFile<MkCoPTreeNode<O>> pagefile,
MkTreeSettings<O,MkCoPTreeNode<O>,MkCoPEntry> settings)
Constructor.
|
MkCoPTreeIndex(Relation<O> relation,
PageFile<MkCoPTreeNode<O>> pageFile,
MkTreeSettings<O,MkCoPTreeNode<O>,MkCoPEntry> settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
MkMaxTreeIndex.relation
Relation indexed.
|
| Modifier and Type | Method and Description |
|---|---|
MkMaxTreeIndex<O> |
MkMaxTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
MkMaxTree(Relation<O> relation,
PageFile<MkMaxTreeNode<O>> pagefile,
MkTreeSettings<O,MkMaxTreeNode<O>,MkMaxEntry> settings)
Constructor.
|
MkMaxTreeIndex(Relation<O> relation,
PageFile<MkMaxTreeNode<O>> pagefile,
MkTreeSettings<O,MkMaxTreeNode<O>,MkMaxEntry> settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
MkTabTreeIndex.relation
The relation indexed.
|
| Modifier and Type | Method and Description |
|---|---|
MkTabTreeIndex<O> |
MkTabTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
MkTabTree(Relation<O> relation,
PageFile<MkTabTreeNode<O>> pagefile,
MkTreeSettings<O,MkTabTreeNode<O>,MkTabEntry> settings)
Constructor.
|
MkTabTreeIndex(Relation<O> relation,
PageFile<MkTabTreeNode<O>> pagefile,
MkTreeSettings<O,MkTabTreeNode<O>,MkTabEntry> settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
MTreeIndex.relation
The relation indexed.
|
| Modifier and Type | Method and Description |
|---|---|
MTreeIndex<O> |
MTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
MTreeIndex(Relation<O> relation,
PageFile<MTreeNode<O>> pagefile,
MTreeSettings<O,MTreeNode<O>,MTreeEntry> settings)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
MinimalisticMemoryKDTree<O> |
MinimalisticMemoryKDTree.Factory.instantiate(Relation<O> relation) |
SmallMemoryKDTree<O> |
SmallMemoryKDTree.Factory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
CountSortAccesses(Counter objaccess,
Relation<? extends NumberVector> data)
Constructor.
|
MinimalisticMemoryKDTree(Relation<O> relation,
int leafsize)
Constructor.
|
SmallMemoryKDTree(Relation<O> relation,
int leafsize)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
DeLiCluTreeIndex.relation
The relation we index.
|
| Modifier and Type | Method and Description |
|---|---|
DeLiCluTreeIndex<O> |
DeLiCluTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
DeLiCluTreeIndex(Relation<O> relation,
PageFile<DeLiCluNode> pagefile,
AbstractRTreeSettings settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
FlatRStarTreeIndex.relation
The relation we index
|
| Modifier and Type | Method and Description |
|---|---|
FlatRStarTreeIndex<O> |
FlatRStarTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
FlatRStarTreeIndex(Relation<O> relation,
PageFile<FlatRStarTreeNode> pagefile,
AbstractRTreeSettings settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends O> |
RStarTreeRangeQuery.relation
Relation we query.
|
protected Relation<? extends O> |
RStarTreeKNNQuery.relation
Relation we query.
|
| Constructor and Description |
|---|
EuclideanRStarTreeKNNQuery(AbstractRStarTree<?,?,?> tree,
Relation<? extends O> relation)
Constructor.
|
EuclideanRStarTreeRangeQuery(AbstractRStarTree<?,?,?> tree,
Relation<? extends O> relation)
Constructor.
|
RStarTreeKNNQuery(AbstractRStarTree<?,?,?> tree,
Relation<? extends O> relation,
SpatialPrimitiveDistanceFunction<? super O> distanceFunction)
Constructor.
|
RStarTreeRangeQuery(AbstractRStarTree<?,?,?> tree,
Relation<? extends O> relation,
SpatialPrimitiveDistanceFunction<? super O> distanceFunction)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
RdKNNTree.relation
The relation we query.
|
| Modifier and Type | Method and Description |
|---|---|
RdKNNTree<O> |
RdKNNTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
RdKNNTree(Relation<O> relation,
PageFile<RdKNNNode> pagefile,
RdkNNSettings<O> settings)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<O> |
RStarTreeIndex.relation
Relation
|
| Modifier and Type | Method and Description |
|---|---|
RStarTreeIndex<O> |
RStarTreeFactory.instantiate(Relation<O> relation) |
| Constructor and Description |
|---|
RStarTreeIndex(Relation<O> relation,
PageFile<RStarTreeNode> pagefile,
AbstractRTreeSettings settings)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
PartialVAFile<V> |
PartialVAFile.Factory.instantiate(Relation<V> relation) |
VAFile<V> |
VAFile.Factory.instantiate(Relation<V> relation) |
void |
VAFile.setPartitions(Relation<V> relation)
Initialize the data set grid by computing quantiles.
|
| Constructor and Description |
|---|
DAFile(Relation<? extends NumberVector> relation,
int dimension,
int partitions)
Constructor.
|
PartialVAFile(int pageSize,
Relation<V> relation,
int partitions)
Constructor.
|
VAFile(int pageSize,
Relation<V> relation,
int partitions)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
private ArrayList<ArrayDBIDs> |
HiCSDimensionSimilarity.buildOneDimIndexes(Relation<? extends NumberVector> relation,
DBIDs ids,
DimensionSimilarityMatrix matrix)
Calculates "index structures" for every attribute, i.e. sorts a
ModifiableArray of every DBID in the database for every dimension and
stores them in a list
|
private ArrayList<ArrayList<DBIDs>> |
MCEDimensionSimilarity.buildPartitions(Relation<? extends NumberVector> relation,
DBIDs ids,
int depth,
DimensionSimilarityMatrix matrix)
Calculates "index structures" for every attribute, i.e. sorts a
ModifiableArray of every DBID in the database for every dimension and
stores them in a list.
|
private double |
HiCSDimensionSimilarity.calculateContrast(Relation<? extends NumberVector> relation,
DBIDs subset,
ArrayDBIDs subspaceIndex1,
ArrayDBIDs subspaceIndex2,
int dim1,
int dim2,
Random random)
Calculates the actual contrast of a given subspace
|
void |
SlopeInversionDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix) |
void |
SlopeDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix) |
void |
CovarianceDimensionSimilarity.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 |
SURFINGDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix) |
void |
MCEDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix) |
void |
DimensionSimilarity.computeDimensionSimilarites(Relation<? extends V> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix)
Compute the dimension similarity matrix
|
| Modifier and Type | Method and Description |
|---|---|
<F extends NumberVector> |
CovarianceMatrix.getMeanVector(Relation<? extends F> relation)
Get the mean as vector.
|
static ProjectedCentroid |
ProjectedCentroid.make(long[] dims,
Relation<? extends NumberVector> relation)
Static Constructor from a relation.
|
static ProjectedCentroid |
ProjectedCentroid.make(long[] dims,
Relation<? extends NumberVector> relation,
DBIDs ids)
Static Constructor from a relation.
|
static Centroid |
Centroid.make(Relation<? extends NumberVector> relation)
Static constructor from an existing relation.
|
static CovarianceMatrix |
CovarianceMatrix.make(Relation<? extends NumberVector> relation)
Static Constructor from a full relation.
|
static Centroid |
Centroid.make(Relation<? extends NumberVector> relation,
DBIDs ids)
Static constructor from an existing relation.
|
static CovarianceMatrix |
CovarianceMatrix.make(Relation<? extends NumberVector> relation,
DBIDs ids)
Static Constructor from a full relation.
|
<F extends NumberVector> |
Centroid.toVector(Relation<? extends F> relation)
Get the data as vector.
|
| Modifier and Type | Method and Description |
|---|---|
Matrix |
StandardCovarianceMatrixBuilder.processDatabase(Relation<? extends NumberVector> database)
Compute Covariance Matrix for a complete database.
|
Matrix |
AbstractCovarianceMatrixBuilder.processDatabase(Relation<? extends NumberVector> database) |
Matrix |
CovarianceMatrixBuilder.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 |
StandardCovarianceMatrixBuilder.processIds(DBIDs ids,
Relation<? extends NumberVector> database)
Compute Covariance Matrix for a collection of database IDs.
|
PCAFilteredResult |
PCAFilteredAutotuningRunner.processIds(DBIDs ids,
Relation<? extends NumberVector> database) |
abstract Matrix |
AbstractCovarianceMatrixBuilder.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.
|
PCAResult |
PCARunner.processIds(DBIDs ids,
Relation<? extends NumberVector> database)
Run PCA on a collection of database IDs.
|
Matrix |
WeightedCovarianceMatrixBuilder.processIds(DBIDs ids,
Relation<? extends NumberVector> relation)
Weighted Covariance Matrix for a set of IDs.
|
PCAFilteredResult |
PCAFilteredRunner.processIds(DBIDs ids,
Relation<? extends NumberVector> database)
Run PCA on a collection of database IDs.
|
Matrix |
RANSACCovarianceMatrixBuilder.processIds(DBIDs ids,
Relation<? extends NumberVector> relation) |
PCAFilteredResult |
PCAFilteredAutotuningRunner.processQueryResult(DoubleDBIDList results,
Relation<? extends NumberVector> database) |
PCAResult |
PCARunner.processQueryResult(DoubleDBIDList results,
Relation<? extends NumberVector> database)
Run PCA on a QueryResult Collection.
|
PCAFilteredResult |
PCAFilteredRunner.processQueryResult(DoubleDBIDList results,
Relation<? extends NumberVector> database)
Run PCA on a QueryResult Collection.
|
Matrix |
AbstractCovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results,
Relation<? extends NumberVector> database) |
Matrix |
CovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results,
Relation<? extends NumberVector> database)
Compute Covariance Matrix for a QueryResult Collection.
|
Matrix |
AbstractCovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results,
Relation<? extends NumberVector> database,
int k) |
Matrix |
CovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results,
Relation<? extends NumberVector> database,
int k)
Compute Covariance Matrix for a QueryResult Collection.
|
Matrix |
WeightedCovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results,
Relation<? extends NumberVector> database,
int k)
Compute Covariance Matrix for a QueryResult Collection.
|
| Modifier and Type | Method and Description |
|---|---|
static LinearScale[] |
Scales.calcScales(Relation<? extends SpatialComparable> db)
Compute a linear scale for each dimension.
|
| Constructor and Description |
|---|
ZCurveTransformer(Relation<? extends NumberVector> relation,
DBIDs ids)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
static List<Relation<?>> |
ResultUtil.getRelations(Result r)
Collect all Annotation results from a Result
|
| Modifier and Type | Method and Description |
|---|---|
private DoubleObjPair<Polygon> |
KMLOutputHandler.buildHullsRecursively(Cluster<Model> clu,
Hierarchy<Cluster<Model>> hier,
Map<Object,DoubleObjPair<Polygon>> hulls,
Relation<? extends NumberVector> coords)
Recursively step through the clusters to build the hulls.
|
static SamplingResult |
ResultUtil.getSamplingResult(Relation<?> rel)
Get the sampling result attached to a relation
|
static ScalesResult |
ResultUtil.getScalesResult(Relation<? extends SpatialComparable> rel)
Get (or create) a scales result for a relation.
|
| Modifier and Type | Method and Description |
|---|---|
private StringBuilder |
KMLOutputHandler.makeDescription(Collection<Relation<?>> relations,
DBIDRef id)
Make an HTML description.
|
| Constructor and Description |
|---|
SamplingResult(Relation<?> rel)
Constructor.
|
ScalesResult(Relation<? extends SpatialComparable> relation)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
TextWriter.printObject(TextWriterStream out,
Database db,
DBIDRef objID,
List<Relation<?>> ra) |
private void |
TextWriter.writeClusterResult(Database db,
StreamFactory streamOpener,
Clustering<Model> clustering,
Cluster<Model> clus,
List<Relation<?>> ra,
NamingScheme naming) |
private void |
TextWriter.writeOrderingResult(Database db,
StreamFactory streamOpener,
OrderingResult or,
List<Relation<?>> ra) |
| Modifier and Type | Method and Description |
|---|---|
static Relation<String> |
DatabaseUtil.guessLabelRepresentation(Database database)
Guess a potentially label-like representation, preferring class labels.
|
static Relation<String> |
DatabaseUtil.guessObjectLabelRepresentation(Database database)
Guess a potentially object label-like representation.
|
| Modifier and Type | Method and Description |
|---|---|
static SortedSet<ClassLabel> |
DatabaseUtil.getClassLabels(Relation<? extends ClassLabel> database)
Retrieves all class labels within the database.
|
static <O> KNNQuery<O> |
DatabaseUtil.precomputedKNNQuery(Database database,
Relation<O> relation,
DistanceFunction<? super O> distf,
int k)
Get (or create) a precomputed kNN query for the database.
|
static <O> KNNQuery<O> |
DatabaseUtil.precomputedKNNQuery(Database database,
Relation<O> relation,
DistanceQuery<O> dq,
int k)
Get (or create) a precomputed kNN query for the database.
|
| Modifier and Type | Method and Description |
|---|---|
Collection<? extends NumberVector> |
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> |
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> |
FullDatabaseReferencePoints.getReferencePoints(Relation<? extends NumberVector> db) |
Collection<? extends NumberVector> |
AxisBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db) |
| Modifier and Type | Field and Description |
|---|---|
private Relation<?> |
VisualizerContext.relation
Relation currently visualized.
|
(package private) Relation<?> |
VisualizationTask.relation
The main representation
|
| Modifier and Type | Method and Description |
|---|---|
<R extends Relation<?>> |
VisualizationTask.getRelation() |
| Modifier and Type | Method and Description |
|---|---|
Relation<?> |
VisualizerContext.getRelation()
Current relation.
|
| Modifier and Type | Method and Description |
|---|---|
void |
VisualizerContext.setRelation(Relation<?> rel)
Set the current relation.
|
| Constructor and Description |
|---|
VisualizationTask(String name,
VisualizerContext context,
Object result,
Relation<?> relation,
VisFactory factory)
Visualization task.
|
VisualizerContext(ResultHierarchy hier,
Result start,
Relation<?> relation,
StyleLibrary stylelib,
Collection<VisualizationProcessor> factories)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Relation<V> |
ScatterPlotProjector.rel
Relation we project.
|
(package private) Relation<V> |
HistogramProjector.rel
Relation we project.
|
(package private) Relation<V> |
ParallelPlotProjector.rel
Relation we project.
|
| Modifier and Type | Method and Description |
|---|---|
Relation<V> |
ScatterPlotProjector.getRelation()
The relation we project.
|
Relation<V> |
HistogramProjector.getRelation()
Get the relation we project.
|
Relation<V> |
ParallelPlotProjector.getRelation()
The relation we project.
|
| Modifier and Type | Method and Description |
|---|---|
private int |
ParallelPlotFactory.dimensionality(Relation<?> rel) |
private int |
ScatterPlotFactory.dimensionality(Relation<?> rel) |
| Constructor and Description |
|---|
HistogramProjector(Relation<V> rel,
int maxdim)
Constructor.
|
ParallelPlotProjector(Relation<V> rel)
Constructor.
|
ScatterPlotProjector(Relation<V> rel,
int maxdim)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Relation<NV> |
ColoredHistogramVisualizer.Instance.relation
The database we visualize
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<NV> |
AbstractParallelVisualization.relation
The representation we visualize
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends NumberVector> |
AbstractScatterplotVisualization.rel
The representation we visualize
|
protected Relation<PolygonsObject> |
PolygonVisualization.Instance.rep
The representation we visualize
|
private Relation<?> |
TooltipStringVisualization.Instance.result
Number value to visualize
|
private Relation<? extends Number> |
TooltipScoreVisualization.Instance.result
Number value to visualize
|
| Modifier and Type | Method and Description |
|---|---|
static boolean |
TreeSphereVisualization.canVisualize(Relation<?> rel,
AbstractMTree<?,?,?,?> tree)
Test for a visualizable index in the context's database.
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<Vector> |
COPVectorVisualization.Instance.result
The outlier result to visualize
|
| Modifier and Type | Field and Description |
|---|---|
protected Relation<? extends UncertainObject> |
UncertainBoundingBoxVisualization.Instance.rel
The representation we visualize
|
protected Relation<? extends UncertainObject> |
UncertainSamplesVisualization.Instance.rel
The representation we visualize
|
| Modifier and Type | Method and Description |
|---|---|
protected WritableDataStore<SameSizeKMeansAlgorithm.Meta> |
SameSizeKMeansAlgorithm.initializeMeta(Relation<V> relation,
List<? extends NumberVector> means)
Initialize the metadata storage.
|
protected List<Vector> |
SameSizeKMeansAlgorithm.refineResult(Relation<V> relation,
List<Vector> means,
List<ModifiableDBIDs> clusters,
WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas,
ArrayModifiableDBIDs tids)
Perform k-means style iterations to improve the clustering result.
|
PointerHierarchyRepresentationResult |
NaiveAgglomerativeHierarchicalClustering4.run(Database db,
Relation<O> relation)
Run the algorithm
|
Result |
NaiveAgglomerativeHierarchicalClustering3.run(Database db,
Relation<O> relation)
Run the algorithm
|
Result |
NaiveAgglomerativeHierarchicalClustering1.run(Database db,
Relation<O> relation)
Run the algorithm
|
Result |
NaiveAgglomerativeHierarchicalClustering2.run(Database db,
Relation<O> relation)
Run the algorithm
|
Clustering<MeanModel> |
SameSizeKMeansAlgorithm.run(Database database,
Relation<V> relation)
Run k-means with cluster size constraints.
|
protected void |
SameSizeKMeansAlgorithm.updateDistances(Relation<V> relation,
List<Vector> means,
WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas,
NumberVectorDistanceFunction<? super V> df)
Compute the distances of each object to all means.
|
| Modifier and Type | Method and Description |
|---|---|
OutlierResult |
ODIN.run(Database database,
Relation<O> relation)
Run the ODIN algorithm
Tutorial note: the signature of this method depends on the types
that we requested in the
ODIN.getInputTypeRestriction() method. |
OutlierResult |
DistanceStddevOutlier.run(Database database,
Relation<O> relation)
Run the outlier detection algorithm
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.