| Package | Description |
|---|---|
| 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.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.initialization |
Initialization strategies for k-means.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality |
Quality measures for k-Means results.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.subspace |
Axis-parallel subspace clustering algorithms
The clustering algorithms in this package are instances of both, projected
clustering algorithms or subspace clustering algorithms according to the
classical but somewhat obsolete classification schema of clustering
algorithms for axis-parallel subspaces.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.clustering |
Clustering based 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.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.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.style |
Style management for ELKI visualizations
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.optics |
Visualizers that do work on OPTICS plots
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster |
Visualizers for clustering results based on 2D projections
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj |
Visualizers that do not use a particular projection
|
| Modifier and Type | Method and Description |
|---|---|
protected Cluster<BiclusterModel> |
AbstractBiclustering.defineBicluster(java.util.BitSet rows,
java.util.BitSet cols)
Defines a Bicluster as given by the included rows and columns.
|
protected Cluster<BiclusterModel> |
AbstractBiclustering.defineBicluster(long[] rows,
long[] cols)
Defines a Bicluster as given by the included rows and columns.
|
| Modifier and Type | Method and Description |
|---|---|
private java.util.List<java.util.List<Cluster<CorrelationModel>>> |
ERiC.extractCorrelationClusters(Clustering<Model> dbscanResult,
Relation<V> relation,
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 |
|---|---|
private boolean |
ERiC.isParent(ERiCNeighborPredicate.Instance npred,
Cluster<CorrelationModel> parent,
It<Cluster<CorrelationModel>> iter)
Returns true, if the specified parent cluster is a parent of one child of
the children clusters.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
ERiC.buildHierarchy(Clustering<CorrelationModel> clustering,
java.util.List<java.util.List<Cluster<CorrelationModel>>> clusterMap,
ERiCNeighborPredicate.Instance npred) |
private boolean |
ERiC.isParent(ERiCNeighborPredicate.Instance npred,
Cluster<CorrelationModel> parent,
It<Cluster<CorrelationModel>> iter)
Returns true, if the specified parent cluster is a parent of one child of
the children clusters.
|
| Modifier and Type | Field and Description |
|---|---|
protected java.util.Collection<Cluster<DendrogramModel>> |
SimplifiedHierarchyExtraction.TempCluster.children
(Finished) child clusters
|
| Modifier and Type | Method and Description |
|---|---|
protected Cluster<DendrogramModel> |
SimplifiedHierarchyExtraction.Instance.makeCluster(DBIDRef lead,
double depth,
DBIDs members)
Make the cluster for the given object
|
protected Cluster<DendrogramModel> |
AbstractCutDendrogram.Instance.makeCluster(DBIDRef lead,
double depth,
DBIDs members)
Make the cluster for the given object
|
protected Cluster<DendrogramModel> |
SimplifiedHierarchyExtraction.Instance.toCluster(SimplifiedHierarchyExtraction.TempCluster temp,
Clustering<DendrogramModel> clustering,
DBIDRef lead)
Make the cluster for the given object
|
| Modifier and Type | Method and Description |
|---|---|
void |
SimplifiedHierarchyExtraction.TempCluster.addChild(Cluster<DendrogramModel> clu)
Add a child cluster.
|
private void |
HDBSCANHierarchyExtraction.Instance.collectChildren(HDBSCANHierarchyExtraction.TempCluster temp,
Clustering<DendrogramModel> clustering,
HDBSCANHierarchyExtraction.TempCluster cur,
Cluster<DendrogramModel> clus,
boolean flatten)
Recursive flattening of clusters.
|
private void |
HDBSCANHierarchyExtraction.Instance.finalizeCluster(HDBSCANHierarchyExtraction.TempCluster temp,
Clustering<DendrogramModel> clustering,
Cluster<DendrogramModel> parent,
boolean flatten)
Make the cluster for the given object
|
| Modifier and Type | Method and Description |
|---|---|
protected java.util.List<Cluster<M>> |
XMeans.splitCluster(Cluster<M> parentCluster,
Database database,
Relation<V> relation)
Conditionally splits the clusters based on the information criterion.
|
| Modifier and Type | Method and Description |
|---|---|
protected double[][] |
XMeans.splitCentroid(Cluster<? extends MeanModel> parentCluster,
Relation<V> relation)
Split an existing centroid into two initial centers.
|
protected java.util.List<Cluster<M>> |
XMeans.splitCluster(Cluster<M> parentCluster,
Database database,
Relation<V> relation)
Conditionally splits the clusters based on the information criterion.
|
| Modifier and Type | Method and Description |
|---|---|
void |
PredefinedInitialMeans.setInitialClusters(java.util.List<? extends Cluster<? extends MeanModel>> initialMeans)
Set the initial means.
|
| Modifier and Type | Method and Description |
|---|---|
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 |
|---|---|
protected 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.
|
protected 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.
|
protected Cluster<SubspaceModel> |
FastDOC.runDOC(Database database,
Relation<V> relation,
ArrayModifiableDBIDs S,
int d,
int n,
int m,
int r,
int minClusterSize)
Performs a single run of FastDOC, finding a single cluster.
|
| Modifier and Type | Method and Description |
|---|---|
private java.util.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 java.util.List<Cluster<SubspaceModel>> |
DiSH.sortClusters(Relation<V> relation,
it.unimi.dsi.fastutil.objects.Object2ObjectMap<long[],java.util.List<ArrayModifiableDBIDs>> clustersMap)
Returns a sorted list of the clusters w.r.t. the subspace dimensionality in
descending order.
|
| Modifier and Type | Method and Description |
|---|---|
private boolean |
DiSH.isParent(Relation<V> relation,
Cluster<SubspaceModel> parent,
It<Cluster<SubspaceModel>> iter,
int db_dim)
Returns true, if the specified parent cluster is a parent of one child of
the children clusters.
|
| Modifier and Type | Method and Description |
|---|---|
private Subspace |
SUBCLU.bestSubspace(java.util.List<Subspace> subspaces,
Subspace candidate,
java.util.TreeMap<Subspace,java.util.List<Cluster<Model>>> clusterMap)
Determines the
d-dimensional subspace of the (d+1)
-dimensional candidate with minimal number of objects in the cluster. |
private void |
DiSH.buildHierarchy(Relation<V> database,
Clustering<SubspaceModel> clustering,
java.util.List<Cluster<SubspaceModel>> clusters,
int dimensionality)
Builds the cluster hierarchy.
|
private boolean |
DiSH.isParent(Relation<V> relation,
Cluster<SubspaceModel> parent,
It<Cluster<SubspaceModel>> iter,
int db_dim)
Returns true, if the specified parent cluster is a parent of one child of
the children clusters.
|
| Modifier and Type | Method and Description |
|---|---|
private double |
CBLOF.computeLargeClusterCBLOF(O obj,
NumberVectorDistanceFunction<? super O> distanceQuery,
NumberVector clusterMean,
Cluster<MeanModel> cluster) |
private double |
CBLOF.computeSmallClusterCBLOF(O obj,
NumberVectorDistanceFunction<? super O> distance,
java.util.List<NumberVector> largeClusterMeans,
Cluster<MeanModel> cluster) |
| Modifier and Type | Method and Description |
|---|---|
private void |
CBLOF.computeCBLOFs(Relation<O> relation,
NumberVectorDistanceFunction<? super O> distance,
WritableDoubleDataStore cblofs,
DoubleMinMax cblofMinMax,
java.util.List<? extends Cluster<MeanModel>> largeClusters,
java.util.List<? extends Cluster<MeanModel>> smallClusters)
Compute the CBLOF scores for all the data.
|
private void |
CBLOF.computeCBLOFs(Relation<O> relation,
NumberVectorDistanceFunction<? super O> distance,
WritableDoubleDataStore cblofs,
DoubleMinMax cblofMinMax,
java.util.List<? extends Cluster<MeanModel>> largeClusters,
java.util.List<? extends Cluster<MeanModel>> smallClusters)
Compute the CBLOF scores for all the data.
|
private int |
CBLOF.getClusterBoundary(Relation<O> relation,
java.util.List<? extends Cluster<MeanModel>> clusters)
Compute the boundary index separating the large cluster from the small
cluster.
|
| Modifier and Type | Field and Description |
|---|---|
static java.util.Comparator<Cluster<?>> |
Cluster.BY_NAME_SORTER
A partial comparator for Clusters, based on their name.
|
private ModifiableHierarchy<Cluster<M>> |
Clustering.hierarchy
Cluster hierarchy.
|
private java.util.List<Cluster<M>> |
Clustering.toplevelclusters
Keep a list of top level clusters.
|
| Modifier and Type | Method and Description |
|---|---|
java.util.List<Cluster<M>> |
Clustering.getAllClusters()
Collect all clusters (recursively) into a List.
|
Hierarchy<Cluster<M>> |
Clustering.getClusterHierarchy()
Get the cluster hierarchy.
|
java.util.List<Cluster<M>> |
Clustering.getToplevelClusters()
Return top level clusters
|
It<Cluster<M>> |
Clustering.iterToplevelClusters()
Iterate over the top level clusters.
|
| Modifier and Type | Method and Description |
|---|---|
void |
Clustering.addChildCluster(Cluster<M> parent,
Cluster<M> child)
Add a cluster to the clustering.
|
void |
Clustering.addChildCluster(Cluster<M> parent,
Cluster<M> child)
Add a cluster to the clustering.
|
void |
Clustering.addToplevelCluster(Cluster<M> clus)
Add a cluster to the clustering.
|
| Constructor and Description |
|---|
Clustering(java.lang.String name,
java.lang.String shortname,
java.util.List<Cluster<M>> toplevelclusters)
Constructor with a list of top level clusters
|
| Modifier and Type | Method and Description |
|---|---|
<T extends Cluster<?>> |
ClusterIntersectionSimilarityFunction.instantiate(Relation<T> relation) |
<T extends Cluster<?>> |
ClusterJaccardSimilarityFunction.instantiate(Relation<T> relation) |
| Modifier and Type | Method and Description |
|---|---|
SimpleTypeInformation<? super Cluster<?>> |
ClusterIntersectionSimilarityFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super Cluster<?>> |
ClusterJaccardSimilarityFunction.getInputTypeRestriction() |
| Modifier and Type | Method and Description |
|---|---|
double |
ClusterIntersectionSimilarityFunction.distance(Cluster<?> o1,
Cluster<?> o2) |
double |
ClusterIntersectionSimilarityFunction.distance(Cluster<?> o1,
Cluster<?> o2) |
double |
ClusterJaccardSimilarityFunction.distance(Cluster<?> o1,
Cluster<?> o2) |
double |
ClusterJaccardSimilarityFunction.distance(Cluster<?> o1,
Cluster<?> o2) |
double |
ClusterIntersectionSimilarityFunction.similarity(Cluster<?> o1,
Cluster<?> o2) |
double |
ClusterIntersectionSimilarityFunction.similarity(Cluster<?> o1,
Cluster<?> o2) |
double |
ClusterJaccardSimilarityFunction.similarity(Cluster<?> o1,
Cluster<?> o2) |
double |
ClusterJaccardSimilarityFunction.similarity(Cluster<?> o1,
Cluster<?> o2) |
| Modifier and Type | Method and Description |
|---|---|
static double |
EvaluateClustering.evaluateRanking(ScoreEvaluation eval,
Cluster<?> clus,
DoubleDBIDList ranking)
Evaluate given a cluster (of positive elements) and a scoring list.
|
| Modifier and Type | Method and Description |
|---|---|
protected double |
EvaluateCIndex.processCluster(Cluster<?> cluster,
java.util.List<? extends Cluster<?>> clusters,
int i,
DistanceQuery<O> dq,
DoubleHeap maxDists,
DoubleHeap minDists,
int w) |
protected void |
EvaluateCIndex.processSingleton(Cluster<?> cluster,
Relation<? extends O> rel,
DistanceQuery<O> dq,
DoubleHeap maxDists,
DoubleHeap minDists,
int w) |
| Modifier and Type | Method and Description |
|---|---|
static int |
EvaluateSimplifiedSilhouette.centroids(Relation<? extends NumberVector> rel,
java.util.List<? extends Cluster<?>> clusters,
NumberVector[] centroids,
NoiseHandling noiseOption)
Compute centroids.
|
protected double[] |
EvaluateConcordantPairs.computeWithinDistances(Relation<? extends NumberVector> rel,
java.util.List<? extends Cluster<?>> clusters,
int withinPairs) |
static int |
EvaluateVarianceRatioCriteria.globalCentroid(Centroid overallCentroid,
Relation<? extends NumberVector> rel,
java.util.List<? extends Cluster<?>> clusters,
NumberVector[] centroids,
NoiseHandling noiseOption)
Update the global centroid.
|
protected double |
EvaluateCIndex.processCluster(Cluster<?> cluster,
java.util.List<? extends Cluster<?>> clusters,
int i,
DistanceQuery<O> dq,
DoubleHeap maxDists,
DoubleHeap minDists,
int w) |
double[] |
EvaluateDaviesBouldin.withinGroupDistances(Relation<? extends NumberVector> rel,
java.util.List<? extends Cluster<?>> clusters,
NumberVector[] centroids) |
| Modifier and Type | Field and Description |
|---|---|
private java.util.List<java.util.List<? extends Cluster<?>>> |
Segments.clusters
Clusters
|
| Modifier and Type | Method and Description |
|---|---|
private void |
Segments.recursivelyFill(java.util.List<java.util.List<? extends Cluster<?>>> cs) |
private void |
Segments.recursivelyFill(java.util.List<java.util.List<? extends Cluster<?>>> cs,
int depth,
SetDBIDs first,
SetDBIDs second,
int[] path,
boolean objectsegment) |
| Modifier and Type | Method and Description |
|---|---|
private DoubleObjPair<Polygon> |
KMLOutputHandler.buildHullsRecursively(Cluster<Model> clu,
Hierarchy<Cluster<Model>> hier,
java.util.Map<java.lang.Object,DoubleObjPair<Polygon>> hulls,
Relation<? extends NumberVector> coords)
Recursively step through the clusters to build the hulls.
|
private java.lang.StringBuilder |
KMLOutputHandler.makeDescription(Cluster<?> c)
Make an HTML description.
|
| Modifier and Type | Method and Description |
|---|---|
private DoubleObjPair<Polygon> |
KMLOutputHandler.buildHullsRecursively(Cluster<Model> clu,
Hierarchy<Cluster<Model>> hier,
java.util.Map<java.lang.Object,DoubleObjPair<Polygon>> hulls,
Relation<? extends NumberVector> coords)
Recursively step through the clusters to build the hulls.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
TextWriter.writeClusterResult(Database db,
StreamFactory streamOpener,
Clustering<Model> clustering,
Cluster<Model> clus,
java.util.List<Relation<?>> ra,
NamingScheme naming) |
| Modifier and Type | Field and Description |
|---|---|
private java.util.Map<Cluster<?>,java.lang.String> |
SimpleEnumeratingScheme.names
Assigned cluster names.
|
| Modifier and Type | Method and Description |
|---|---|
java.lang.String |
NamingScheme.getNameFor(Cluster<?> cluster)
Retrieve a name for the given cluster.
|
java.lang.String |
SimpleEnumeratingScheme.getNameFor(Cluster<?> cluster)
Retrieve the cluster name.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) it.unimi.dsi.fastutil.objects.Object2IntOpenHashMap<Cluster<?>> |
ClusterStylingPolicy.cmap
Map from cluster objects to color offsets.
|
| Modifier and Type | Method and Description |
|---|---|
int |
ClusterStylingPolicy.getStyleForCluster(Cluster<?> c)
Get the style number for a cluster.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
OPTICSClusterVisualization.Instance.drawClusters(Clustering<OPTICSModel> clustering,
It<Cluster<OPTICSModel>> clusters,
int depth,
java.util.Map<Cluster<?>,java.lang.String> colormap)
Recursively draw clusters
|
private void |
OPTICSClusterVisualization.Instance.drawClusters(Clustering<OPTICSModel> clustering,
It<Cluster<OPTICSModel>> clusters,
int depth,
java.util.Map<Cluster<?>,java.lang.String> colormap)
Recursively draw clusters
|
| Modifier and Type | Method and Description |
|---|---|
private double |
ClusterHullVisualization.Instance.addRecursively(java.util.ArrayList<double[]> hull,
Hierarchy<Cluster<Model>> hier,
Cluster<Model> clus)
Recursively add a cluster and its children.
|
private DoubleObjPair<Polygon> |
ClusterHullVisualization.Instance.buildHullsRecursively(Cluster<Model> clu,
Hierarchy<Cluster<Model>> hier,
java.util.Map<java.lang.Object,DoubleObjPair<Polygon>> hulls)
Recursively step through the clusters to build the hulls.
|
| Modifier and Type | Method and Description |
|---|---|
private double |
ClusterHullVisualization.Instance.addRecursively(java.util.ArrayList<double[]> hull,
Hierarchy<Cluster<Model>> hier,
Cluster<Model> clus)
Recursively add a cluster and its children.
|
private DoubleObjPair<Polygon> |
ClusterHullVisualization.Instance.buildHullsRecursively(Cluster<Model> clu,
Hierarchy<Cluster<Model>> hier,
java.util.Map<java.lang.Object,DoubleObjPair<Polygon>> hulls)
Recursively step through the clusters to build the hulls.
|
| Modifier and Type | Method and Description |
|---|---|
private double |
KeyVisualization.Instance.drawHierarchy(SVGPlot svgp,
MarkerLibrary ml,
DoubleDoublePair size,
DoubleDoublePair pos,
int depth,
Cluster<Model> cluster,
it.unimi.dsi.fastutil.objects.Object2IntOpenHashMap<Cluster<Model>> cnum,
Hierarchy<Cluster<Model>> hier) |
private static <M extends Model> |
KeyVisualization.findDepth(Hierarchy<Cluster<M>> hier,
Cluster<M> cluster,
int[] size)
Recursive depth computation.
|
| Modifier and Type | Method and Description |
|---|---|
private double |
KeyVisualization.Instance.drawHierarchy(SVGPlot svgp,
MarkerLibrary ml,
DoubleDoublePair size,
DoubleDoublePair pos,
int depth,
Cluster<Model> cluster,
it.unimi.dsi.fastutil.objects.Object2IntOpenHashMap<Cluster<Model>> cnum,
Hierarchy<Cluster<Model>> hier) |
private double |
KeyVisualization.Instance.drawHierarchy(SVGPlot svgp,
MarkerLibrary ml,
DoubleDoublePair size,
DoubleDoublePair pos,
int depth,
Cluster<Model> cluster,
it.unimi.dsi.fastutil.objects.Object2IntOpenHashMap<Cluster<Model>> cnum,
Hierarchy<Cluster<Model>> hier) |
private static <M extends Model> |
KeyVisualization.findDepth(Hierarchy<Cluster<M>> hier,
Cluster<M> cluster,
int[] size)
Recursive depth computation.
|
Copyright © 2019 ELKI Development Team. License information.
