| Package | Description |
|---|---|
| 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.em |
Expectation-Maximization clustering algorithm.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan |
Generalized DBSCAN.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction |
Extraction of partitional clusterings from hierarchical results.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.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.
|
| 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.data.type |
Data type information, also used for type restrictions.
|
| de.lmu.ifi.dbs.elki.datasource.parser |
Parsers for different file formats and data types.
|
| de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster |
Similarity measures for comparing clusters.
|
| de.lmu.ifi.dbs.elki.evaluation.clustering |
Evaluation of clustering results.
|
| de.lmu.ifi.dbs.elki.evaluation.clustering.internal |
Internal evaluation measures for clusterings.
|
| de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments |
Pair-segment analysis of multiple clusterings.
|
| de.lmu.ifi.dbs.elki.evaluation.outlier |
Evaluate an outlier score using a misclassification based cost model.
|
| 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.result.textwriter.naming |
Naming schemes for clusters (for output when an algorithm doesn't generate cluster names).
|
| de.lmu.ifi.dbs.elki.visualization |
Visualization package of ELKI.
|
| de.lmu.ifi.dbs.elki.visualization.opticsplot |
Code for drawing OPTICS plots
|
| de.lmu.ifi.dbs.elki.visualization.style |
Style management for ELKI visualizations.
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.actions |
Action-only "visualizers" that only produce menu entries.
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.optics |
Visualizers that do work on OPTICS plots
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj |
Visualizers that do not use a particular projection.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractProjectedClustering<R extends Clustering<?>,V extends NumberVector>
|
interface |
ClusteringAlgorithm<C extends Clustering<? extends Model>>
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm-Interface. |
| Modifier and Type | Method and Description |
|---|---|
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.
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<MedoidModel> |
AffinityPropagationClusteringAlgorithm.run(Database db,
Relation<O> relation)
Perform affinity propagation clustering.
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<BiclusterWithInversionsModel> |
ChengAndChurch.biclustering() |
protected abstract Clustering<M> |
AbstractBiclustering.biclustering()
Run the actual biclustering algorithm.
|
Clustering<M> |
AbstractBiclustering.run(Relation<V> relation)
Prepares the algorithm for running on a specific database.
|
| Modifier and Type | Method and Description |
|---|---|
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.
|
Clustering<Model> |
LMCLUS.run(Database database,
Relation<NumberVector> relation)
The main LMCLUS (Linear manifold clustering algorithm) is processed in this
method.
|
Clustering<Model> |
ORCLUS.run(Database database,
Relation<V> relation)
Performs the ORCLUS algorithm on the given database.
|
Clustering<CorrelationModel<V>> |
ERiC.run(Database database,
Relation<V> relation)
Performs the ERiC algorithm on the given database.
|
Clustering<DimensionModel> |
COPAC.run(Database database,
Relation<V> relation)
Run the COPAC algorithm.
|
Clustering<Model> |
CASH.run(Database database,
Relation<V> vrel)
Run CASH on the relation.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
ERiC.buildHierarchy(Clustering<CorrelationModel<V>> clustering,
List<List<Cluster<CorrelationModel<V>>>> clusterMap,
ERiCNeighborPredicate.Instance npred) |
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.
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<M> |
EM.run(Database database,
Relation<V> relation)
Performs the EM clustering algorithm on the given database.
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<Model> |
GeneralizedDBSCAN.Instance.run()
Run the actual GDBSCAN algorithm.
|
Clustering<Model> |
GeneralizedDBSCAN.run(Database database) |
Clustering<Model> |
LSDBC.run(Database database,
Relation<O> relation)
Run the LSDBC algorithm
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<DendrogramModel> |
ExtractFlatClusteringFromHierarchy.extractClusters(DBIDs ids,
DBIDDataStore pi,
DoubleDataStore lambda)
Extract all clusters from the pi-lambda-representation.
|
Clustering<DendrogramModel> |
SimplifiedHierarchyExtraction.extractClusters(DBIDs ids,
DBIDDataStore pi,
DoubleDataStore lambda,
DoubleDataStore coredist)
Extract all clusters from the pi-lambda-representation.
|
Clustering<DendrogramModel> |
HDBSCANHierarchyExtraction.extractClusters(DBIDs ids,
DBIDDataStore pi,
DoubleDataStore lambda,
DoubleDataStore coredist)
Extract all clusters from the pi-lambda-representation.
|
Clustering<DendrogramModel> |
SimplifiedHierarchyExtraction.run(Database database) |
Clustering<DendrogramModel> |
HDBSCANHierarchyExtraction.run(Database database) |
Clustering<DendrogramModel> |
ExtractFlatClusteringFromHierarchy.run(Database database) |
| Modifier and Type | Method and Description |
|---|---|
private void |
HDBSCANHierarchyExtraction.TempCluster.collectChildren(Clustering<DendrogramModel> clustering,
HDBSCANHierarchyExtraction.TempCluster cur,
Cluster<DendrogramModel> clus,
boolean flatten,
boolean hierarchical)
Recursive flattening of clusters.
|
private void |
HDBSCANHierarchyExtraction.TempCluster.finalizeCluster(Clustering<DendrogramModel> clustering,
Cluster<DendrogramModel> parent,
boolean flatten,
boolean hierarchical)
Make the cluster for the given object
|
private Cluster<DendrogramModel> |
SimplifiedHierarchyExtraction.TempCluster.toCluster(Clustering<DendrogramModel> clustering,
DBIDRef lead)
Make the cluster for the given object
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<M> |
XMeans.run(Database database,
Relation<V> relation)
Run the algorithm on a database and relation.
|
Clustering<KMeansModel> |
SingleAssignmentKMeans.run(Database database,
Relation<V> relation) |
Clustering<MedoidModel> |
KMedoidsPAM.run(Database database,
Relation<V> relation)
Run k-medoids
|
Clustering<MedoidModel> |
KMedoidsEM.run(Database database,
Relation<V> relation)
Run k-medoids
|
Clustering<MeanModel> |
KMediansLloyd.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansMacQueen.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansLloyd.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansHybridLloydMacQueen.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansHamerly.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansElkan.run(Database database,
Relation<V> relation) |
Clustering<M> |
KMeansBisecting.run(Database database,
Relation<V> relation) |
Clustering<KMeansModel> |
KMeansBatchedLloyd.run(Database database,
Relation<V> relation) |
Clustering<M> |
KMeans.run(Database database,
Relation<V> rel)
Run the clustering algorithm.
|
Clustering<MedoidModel> |
CLARA.run(Database database,
Relation<V> relation) |
Clustering<M> |
BestOfMultipleKMeans.run(Database database,
Relation<V> relation) |
| Modifier and Type | Method and Description |
|---|---|
Clustering<KMeansModel> |
ParallelLloydKMeans.run(Database database,
Relation<V> relation) |
| 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.
|
static int |
AbstractKMeansQualityMeasure.numPoints(Clustering<? extends MeanModel> clustering)
Compute the number of points in a given set of clusters (which may be
less than the complete data set for X-means!)
|
<V extends NumberVector> |
WithinClusterVarianceQualityMeasure.quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation) |
<V extends NumberVector> |
WithinClusterMeanDistanceQualityMeasure.quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation) |
<V extends NumberVector> |
BayesianInformationCriterionZhao.quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation) |
<V extends NumberVector> |
BayesianInformationCriterion.quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation) |
<V extends NumberVector> |
AkaikeInformationCriterion.quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation) |
<V extends O> |
KMeansQualityMeasure.quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation)
Calculates and returns the quality measure.
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<? extends Model> |
ExternalClustering.run(Database database)
Run the algorithm.
|
| 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) |
| Modifier and Type | Field and Description |
|---|---|
private Clustering<SubspaceModel> |
SUBCLU.result
Holds the result;
|
| Modifier and Type | Method and Description |
|---|---|
private Clustering<SubspaceModel> |
DiSH.computeClusters(Relation<V> database,
DiSH.DiSHClusterOrder clusterOrder)
Computes the hierarchical clusters according to the cluster order.
|
Clustering<SubspaceModel> |
SUBCLU.getResult()
Returns the result of the algorithm.
|
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> |
DiSH.run(Database db,
Relation<V> relation)
Performs the DiSH algorithm on the given database.
|
Clustering<SubspaceModel> |
DOC.run(Database database,
Relation<V> relation)
Performs the DOC or FastDOC (as configured) algorithm on the given
Database.
|
Clustering<SubspaceModel> |
SUBCLU.run(Relation<V> relation)
Performs the SUBCLU algorithm on the given database.
|
Clustering<SubspaceModel> |
CLIQUE.run(Relation<V> relation)
Performs the CLIQUE algorithm on the given database.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
DiSH.buildHierarchy(Relation<V> database,
Clustering<SubspaceModel> clustering,
List<Cluster<SubspaceModel>> clusters,
int dimensionality)
Builds the cluster hierarchy.
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<Model> |
ByLabelOrAllInOneClustering.run(Database database) |
Clustering<Model> |
ByLabelHierarchicalClustering.run(Database database) |
Clustering<Model> |
ByLabelClustering.run(Database database) |
Clustering<Model> |
TrivialAllNoise.run(Relation<?> relation) |
Clustering<Model> |
TrivialAllInOne.run(Relation<?> relation) |
Clustering<Model> |
ByLabelHierarchicalClustering.run(Relation<?> relation)
Run the actual clustering algorithm.
|
Clustering<Model> |
ByLabelClustering.run(Relation<?> relation)
Run the actual clustering algorithm.
|
Clustering<Model> |
ByModelClustering.run(Relation<Model> relation)
Run the actual clustering algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CenterOfMassMetaClustering<C extends Clustering<?>>
Center-of-mass meta clustering reduces uncertain objects to their center of
mass, then runs a vector-oriented clustering algorithm on this data set.
|
static class |
CenterOfMassMetaClustering.Parameterizer<C extends Clustering<?>>
Parameterization class.
|
| Modifier and Type | Method and Description |
|---|---|
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.
|
protected Clustering<?> |
RepresentativeUncertainClustering.runClusteringAlgorithm(ResultHierarchy hierarchy,
Result parent,
DBIDs ids,
DataStore<DoubleVector> store,
int dim,
String title)
Run a clustering algorithm on a single instance.
|
| Modifier and Type | Field and Description |
|---|---|
static SimpleTypeInformation<Clustering<?>> |
TypeUtil.CLUSTERING
Clustering.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Clustering<Model> |
ClusteringVectorParser.curclu
Current clustering.
|
| Modifier and Type | Method and Description |
|---|---|
<T extends Clustering<?>> |
ClusteringRandIndexSimilarityFunction.instantiate(Relation<T> relation) |
<T extends Clustering<?>> |
ClusteringFowlkesMallowsSimilarityFunction.instantiate(Relation<T> relation) |
<T extends Clustering<?>> |
ClusteringDistanceSimilarityFunction.instantiate(Relation<T> relation) |
<T extends Clustering<?>> |
ClusteringBCubedF1SimilarityFunction.instantiate(Relation<T> relation) |
<T extends Clustering<?>> |
ClusteringAdjustedRandIndexSimilarityFunction.instantiate(Relation<T> relation) |
| Modifier and Type | Method and Description |
|---|---|
SimpleTypeInformation<? super Clustering<?>> |
ClusteringRandIndexSimilarityFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super Clustering<?>> |
ClusteringFowlkesMallowsSimilarityFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super Clustering<?>> |
ClusteringBCubedF1SimilarityFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super Clustering<?>> |
ClusteringAdjustedRandIndexSimilarityFunction.getInputTypeRestriction() |
| Modifier and Type | Method and Description |
|---|---|
double |
ClusteringRandIndexSimilarityFunction.distance(Clustering<?> o1,
Clustering<?> o2) |
double |
ClusteringRandIndexSimilarityFunction.distance(Clustering<?> o1,
Clustering<?> o2) |
double |
ClusteringFowlkesMallowsSimilarityFunction.distance(Clustering<?> o1,
Clustering<?> o2) |
double |
ClusteringFowlkesMallowsSimilarityFunction.distance(Clustering<?> o1,
Clustering<?> o2) |
double |
ClusteringBCubedF1SimilarityFunction.distance(Clustering<?> o1,
Clustering<?> o2) |
double |
ClusteringBCubedF1SimilarityFunction.distance(Clustering<?> o1,
Clustering<?> o2) |
double |
ClusteringAdjustedRandIndexSimilarityFunction.distance(Clustering<?> o1,
Clustering<?> o2) |
double |
ClusteringAdjustedRandIndexSimilarityFunction.distance(Clustering<?> o1,
Clustering<?> o2) |
double |
ClusteringRandIndexSimilarityFunction.similarity(Clustering<?> o1,
Clustering<?> o2) |
double |
ClusteringRandIndexSimilarityFunction.similarity(Clustering<?> o1,
Clustering<?> o2) |
double |
ClusteringFowlkesMallowsSimilarityFunction.similarity(Clustering<?> o1,
Clustering<?> o2) |
double |
ClusteringFowlkesMallowsSimilarityFunction.similarity(Clustering<?> o1,
Clustering<?> o2) |
double |
ClusteringBCubedF1SimilarityFunction.similarity(Clustering<?> o1,
Clustering<?> o2) |
double |
ClusteringBCubedF1SimilarityFunction.similarity(Clustering<?> o1,
Clustering<?> o2) |
double |
ClusteringAdjustedRandIndexSimilarityFunction.similarity(Clustering<?> o1,
Clustering<?> o2) |
double |
ClusteringAdjustedRandIndexSimilarityFunction.similarity(Clustering<?> o1,
Clustering<?> o2) |
| Modifier and Type | Method and Description |
|---|---|
protected void |
EvaluateClustering.evaluteResult(Database db,
Clustering<?> c,
Clustering<?> refc)
Evaluate a clustering result.
|
protected void |
EvaluateClustering.evaluteResult(Database db,
Clustering<?> c,
Clustering<?> refc)
Evaluate a clustering result.
|
private boolean |
EvaluateClustering.isReferenceResult(Clustering<?> t)
Test if a clustering result is a valid reference result.
|
static <C extends Model> |
LogClusterSizes.logClusterSizes(Clustering<C> c)
Log the cluster sizes of a clustering.
|
void |
ClusterContingencyTable.process(Clustering<?> result1,
Clustering<?> result2)
Process two clustering results.
|
void |
ClusterContingencyTable.process(Clustering<?> result1,
Clustering<?> result2)
Process two clustering results.
|
| Modifier and Type | Method and Description |
|---|---|
double |
EvaluateVarianceRatioCriteria.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateSquaredErrors.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateSimplifiedSilhouette.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluatePBMIndex.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateDaviesBouldin.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateConcordantPairs.evaluateClustering(Database db,
Relation<? extends NumberVector> rel,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateCIndex.evaluateClustering(Database db,
Relation<? extends O> rel,
DistanceQuery<O> dq,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateSilhouette.evaluateClustering(Database db,
Relation<O> rel,
DistanceQuery<O> dq,
Clustering<?> c)
Evaluate a single clustering.
|
| Modifier and Type | Field and Description |
|---|---|
private List<Clustering<?>> |
Segments.clusterings
Clusterings
|
| Constructor and Description |
|---|
Segments(List<Clustering<?>> clusterings)
Initialize segments.
|
| Modifier and Type | Method and Description |
|---|---|
private Clustering<Model> |
OutlierThresholdClustering.split(OutlierResult or) |
| Modifier and Type | Method and Description |
|---|---|
static List<Clustering<? extends Model>> |
ResultUtil.getClusteringResults(Result r)
Collect all clustering results from a Result
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
ClusteringVectorDumper.dumpClusteringOutput(PrintStream writer,
ResultHierarchy hierarchy,
Clustering<?> c)
Dump a single clustering result.
|
private void |
KMLOutputHandler.writeClusteringResult(XMLStreamWriter xmlw,
Clustering<Model> clustering,
Database database) |
| Modifier and Type | Method and Description |
|---|---|
private void |
TextWriter.writeClusterResult(Database db,
StreamFactory streamOpener,
Clustering<Model> clustering,
Cluster<Model> clus,
List<Relation<?>> ra,
NamingScheme naming) |
| Modifier and Type | Field and Description |
|---|---|
private Clustering<?> |
SimpleEnumeratingScheme.clustering
Clustering this scheme is applied to.
|
| Constructor and Description |
|---|
SimpleEnumeratingScheme(Clustering<?> clustering)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
private Clustering<Model> |
VisualizerContext.generateDefaultClustering()
Generate a default (fallback) clustering.
|
| Modifier and Type | Method and Description |
|---|---|
static <E extends ClusterOrder> |
OPTICSCut.makeOPTICSCut(E co,
double epsilon)
Compute an OPTICS cut clustering
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Clustering<?> |
ClusterStylingPolicy.clustering
Clustering in use.
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<?> |
ClusterStylingPolicy.getClustering()
Get the clustering used by this styling policy
|
| Constructor and Description |
|---|
ClusterStylingPolicy(Clustering<?> clustering,
StyleLibrary style)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Clustering<?> |
ClusterStyleAction.SetStyleAction.c
Clustering to use
|
| Constructor and Description |
|---|
ClusterStyleAction.SetStyleAction(Clustering<?> c,
VisualizerContext context)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Clustering<OPTICSModel> |
OPTICSClusterVisualization.Instance.clus
Our clustering
|
| Modifier and Type | Method and Description |
|---|---|
protected static Clustering<OPTICSModel> |
OPTICSClusterVisualization.findOPTICSClustering(VisualizerContext context,
Result start)
Find the first OPTICS clustering child of a result.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
OPTICSClusterVisualization.Instance.drawClusters(Clustering<OPTICSModel> clustering,
Hierarchy.Iter<Cluster<OPTICSModel>> clusters,
int depth,
Map<Cluster<?>,String> colormap)
Recursively draw clusters
|
| Modifier and Type | Method and Description |
|---|---|
protected static <M extends Model> |
KeyVisualization.findDepth(Clustering<M> c)
Compute the size of the clustering.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.