| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.algorithm.clustering |
Clustering algorithms
Clustering algorithms are supposed to implement the
Algorithm-Interface. |
| de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
Correlation 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.evaluation.paircounting |
Evaluation of clustering results via pair counting.
|
| de.lmu.ifi.dbs.elki.result |
Result types, representation and handling
|
| 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.visualizers.optics |
Visualizers that do work on OPTICS plots
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.vis1d |
Visualizers based on 1D projections.
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d |
Visualizers based on 2D projections.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractProjectedClustering<R extends Clustering<Model>,V extends NumberVector<V,?>>
|
class |
AbstractProjectedDBSCAN<R extends Clustering<Model>,V extends NumberVector<V,?>>
Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
|
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 |
|---|---|
private Clustering<Model> |
SLINK.extractClusters_erich(DBIDs ids,
DataStore<DBID> pi,
DataStore<D> lambda,
int minclusters)
Extract all clusters from the pi-lambda-representation.
|
private Clustering<OPTICSModel> |
OPTICSXi.extractClusters(ClusterOrderResult<N> clusterOrderResult,
Relation<?> relation,
double ixi,
int minpts)
Extract clusters from a cluster order result.
|
private Clustering<DendrogramModel<D>> |
SLINK.extractClusters(DBIDs ids,
DataStore<DBID> pi,
DataStore<D> lambda,
int minclusters)
Extract all clusters from the pi-lambda-representation.
|
Clustering<OPTICSModel> |
OPTICSXi.run(Database database,
Relation<?> relation) |
Clustering<Model> |
SNNClustering.run(Database database,
Relation<O> relation)
Perform SNN clustering
|
Clustering<Model> |
DBSCAN.run(Database database,
Relation<O> relation)
Performs the DBSCAN algorithm on the given database.
|
Clustering<MeanModel<V>> |
KMeans.run(Database database,
Relation<V> relation)
Run k-means
|
Clustering<Model> |
AbstractProjectedDBSCAN.run(Database database,
Relation<V> relation) |
Clustering<EMModel<V>> |
EM.run(Database database,
Relation<V> relation)
Performs the EM clustering algorithm on the given database.
|
| Modifier and Type | Field and Description |
|---|---|
protected Class<? extends ClusteringAlgorithm<Clustering<Model>>> |
COPAC.Parameterizer.algC |
private Class<? extends ClusteringAlgorithm<Clustering<Model>>> |
COPAC.partitionAlgorithm
Get the algorithm to run on each partition.
|
| 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> |
CASH.run(Database database,
Relation<ParameterizationFunction> relation)
Run CASH on the relation.
|
Clustering<Model> |
ORCLUS.run(Database database,
Relation<V> relation)
Performs the ORCLUS algorithm on the given database.
|
Clustering<Model> |
COPAC.run(Relation<V> relation)
Performs the COPAC algorithm on the given database.
|
Clustering<CorrelationModel<V>> |
ERiC.run(Relation<V> relation)
Performs the ERiC algorithm on the given database.
|
private Clustering<Model> |
COPAC.runPartitionAlgorithm(Relation<V> relation,
Map<Integer,DBIDs> partitionMap,
DistanceQuery<V,D> query)
Runs the partition algorithm and creates the result.
|
| Modifier and Type | Method and Description |
|---|---|
ClusteringAlgorithm<Clustering<Model>> |
COPAC.getPartitionAlgorithm(DistanceQuery<V,D> query)
Returns the partition algorithm.
|
| Modifier and Type | Method and Description |
|---|---|
private SortedMap<Integer,List<Cluster<CorrelationModel<V>>>> |
ERiC.extractCorrelationClusters(Clustering<Model> copacResult,
Relation<V> database,
int dimensionality)
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.
|
| Constructor and Description |
|---|
COPAC(FilteredLocalPCABasedDistanceFunction<V,?,D> partitionDistanceFunction,
Class<? extends ClusteringAlgorithm<Clustering<Model>>> partitionAlgorithm,
Collection<Pair<OptionID,Object>> partitionAlgorithmParameters)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
private Clustering<SubspaceModel<V>> |
SUBCLU.result
Holds the result;
|
| Modifier and Type | Method and Description |
|---|---|
private Clustering<SubspaceModel<V>> |
DiSH.computeClusters(Relation<V> database,
ClusterOrderResult<PreferenceVectorBasedCorrelationDistance> clusterOrder,
DiSHDistanceFunction.Instance<V> distFunc)
Computes the hierarchical clusters according to the cluster order.
|
Clustering<SubspaceModel<V>> |
SUBCLU.getResult()
Returns the result of the algorithm.
|
Clustering<SubspaceModel<V>> |
DiSH.run(Database database,
Relation<V> relation)
Performs the DiSH algorithm on the given database.
|
Clustering<Model> |
PROCLUS.run(Database database,
Relation<V> relation)
Performs the PROCLUS algorithm on the given database.
|
Clustering<SubspaceModel<V>> |
SUBCLU.run(Relation<V> relation)
Performs the SUBCLU algorithm on the given database.
|
Clustering<SubspaceModel<V>> |
CLIQUE.run(Relation<V> relation)
Performs the CLIQUE algorithm on the given database.
|
| Modifier and Type | Method and Description |
|---|---|
Clustering<Model> |
ByLabelClustering.run(Database database) |
Clustering<Model> |
ByLabelHierarchicalClustering.run(Database database) |
Clustering<Model> |
TrivialAllNoise.run(Relation<?> relation) |
Clustering<Model> |
ByLabelClustering.run(Relation<?> relation)
Run the actual clustering algorithm.
|
Clustering<Model> |
ByLabelHierarchicalClustering.run(Relation<?> relation)
Run the actual clustering algorithm.
|
Clustering<Model> |
TrivialAllInOne.run(Relation<?> relation) |
| Modifier and Type | Method and Description |
|---|---|
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> |
PairCountingFMeasure.compareClusterings(R result1,
S result2)
Compare two clustering results.
|
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> |
PairCountingFMeasure.compareClusterings(R result1,
S result2)
Compare two clustering results.
|
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> |
PairCountingFMeasure.compareClusterings(R result1,
S result2,
boolean noiseSpecial,
boolean hierarchicalSpecial)
Compare two clustering results.
|
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> |
PairCountingFMeasure.compareClusterings(R result1,
S result2,
boolean noiseSpecial,
boolean hierarchicalSpecial)
Compare two clustering results.
|
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> |
PairCountingFMeasure.compareClusterings(R result1,
S result2,
double beta)
Compare two clustering results.
|
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> |
PairCountingFMeasure.compareClusterings(R result1,
S result2,
double beta)
Compare two clustering results.
|
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> |
PairCountingFMeasure.compareClusterings(R result1,
S result2,
double beta,
boolean noiseSpecial,
boolean hierarchicalSpecial)
Compare two clustering results.
|
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> |
PairCountingFMeasure.compareClusterings(R result1,
S result2,
double beta,
boolean noiseSpecial,
boolean hierarchicalSpecial)
Compare two clustering results.
|
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> |
PairCountingFMeasure.countPairs(R result1,
S result2)
Compare two sets of generated pairs.
|
static <R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model> |
PairCountingFMeasure.countPairs(R result1,
S result2)
Compare two sets of generated pairs.
|
static <R extends Clustering<M>,M extends Model> |
PairCountingFMeasure.getPairGenerator(R clusters,
boolean noiseSpecial,
boolean hierarchicalSpecial)
Get a pair generator for the given Clustering
|
| 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 | 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.
|
Clustering<Model> |
VisualizerContext.getOrCreateDefaultClustering()
Convenience method to get the clustering to use, and fall back to a default
"clustering".
|
| Modifier and Type | Method and Description |
|---|---|
static <D extends Distance<D>> |
OPTICSCut.makeOPTICSCut(ClusterOrderResult<D> co,
OPTICSDistanceAdapter<D> adapter,
double epsilon)
Compute an OPTICS cut clustering
|
| Constructor and Description |
|---|
OPTICSColorFromClustering(ColorLibrary colors,
Clustering<?> refc)
Constructor.
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Clustering<OPTICSModel> |
OPTICSClusterVisualization.clus
Our clustering
|
| Modifier and Type | Method and Description |
|---|---|
protected static Clustering<OPTICSModel> |
OPTICSClusterVisualization.findOPTICSClustering(Result result)
Find the first OPTICS clustering child of a result.
|
| Modifier and Type | Field and Description |
|---|---|
private Clustering<Model> |
P1DHistogramVisualizer.clustering
The clustering we visualize
|
| Modifier and Type | Field and Description |
|---|---|
(package private) Clustering<MeanModel<NV>> |
ClusterMeanVisualization.clustering
Clustering to visualize.
|
private Clustering<Model> |
ClusteringVisualization.clustering
The result we visualize
|
(package private) Clustering<EMModel<NV>> |
EMClusterVisualization.clustering
The result we work on
|
(package private) Clustering<Model> |
ClusterConvexHullVisualization.clustering
The result we work on
|
| Modifier and Type | Method and Description |
|---|---|
private static <NV extends NumberVector<NV,?>> |
ClusterMeanVisualization.Factory.findMeanModel(Clustering<?> c)
Test if the given clustering has a mean model.
|
private static <NV extends NumberVector<NV,?>> |
EMClusterVisualization.Factory.findMeanModel(Clustering<?> c)
Test if the given clustering has a mean model.
|
| Modifier and Type | Method and Description |
|---|---|
private static <NV extends NumberVector<NV,?>> |
ClusterMeanVisualization.Factory.findMeanModel(Clustering<?> c)
Test if the given clustering has a mean model.
|
private static <NV extends NumberVector<NV,?>> |
EMClusterVisualization.Factory.findMeanModel(Clustering<?> c)
Test if the given clustering has a mean model.
|
private void |
BubbleVisualization.setupCSS(SVGPlot svgp,
Clustering<? extends Model> clustering)
Registers the Bubble-CSS-Class at a SVGPlot.
|
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