Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm |
Algorithms suitable as a task for the
KDDTask main routine. |
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.algorithm.outlier |
Outlier detection algorithms
|
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.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.cache |
Utility applications for the persistence layer such as distance cache builders.
|
de.lmu.ifi.dbs.elki.application.jsmap |
JavaScript based map client - server architecture.
|
de.lmu.ifi.dbs.elki.application.visualization |
Visualization applications in ELKI.
|
de.lmu.ifi.dbs.elki.database |
ELKI database layer - loading, storing, indexing and accessing data
|
de.lmu.ifi.dbs.elki.database.relation |
Relations, materialized and virtual (views).
|
de.lmu.ifi.dbs.elki.evaluation.histogram |
Functionality for the evaluation of algorithms using histograms.
|
de.lmu.ifi.dbs.elki.result |
Result types, representation and handling
|
de.lmu.ifi.dbs.elki.result.optics |
Result classes for OPTICS.
|
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.visualization |
Visualization package of ELKI.
|
de.lmu.ifi.dbs.elki.visualization.gui |
Package to provide a visualization GUI.
|
de.lmu.ifi.dbs.elki.workflow |
Work flow packages, e.g. following the usual KDD model, closely related to CRISP-DM
|
Modifier and Type | Method and Description |
---|---|
Result |
NullAlgorithm.run(Database database) |
R |
AbstractAlgorithm.run(Database database) |
Result |
Algorithm.run(Database database)
Runs the algorithm.
|
AprioriResult |
APRIORI.run(Database database,
Relation<BitVector> relation)
Performs the APRIORI algorithm on the given database.
|
Result |
DummyAlgorithm.run(Database database,
Relation<O> relation)
Run the algorithm.
|
KNNDistanceOrderResult<D> |
KNNDistanceOrder.run(Database database,
Relation<O> relation)
Provides an order of the kNN-distances for all objects within the specified
database.
|
CollectionResult<CTriple<DBID,DBID,Double>> |
MaterializeDistances.run(Database database,
Relation<O> relation)
Iterates over all points in the database.
|
DataStore<KNNList<D>> |
KNNJoin.run(Database database,
Relation<V> relation)
Joins in the given spatial database to each object its k-nearest neighbors.
|
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.
|
Modifier and Type | Method and Description |
---|---|
protected void |
DBSCAN.expandCluster(Database database,
RangeQuery<O,D> rangeQuery,
DBID startObjectID,
FiniteProgress objprog,
IndefiniteProgress clusprog)
DBSCAN-function expandCluster.
|
protected void |
OPTICS.expandClusterOrder(ClusterOrderResult<D> clusterOrder,
Database database,
RangeQuery<O,D> rangeQuery,
DBID objectID,
D epsilon,
FiniteProgress progress)
OPTICS-function expandClusterOrder.
|
protected void |
OPTICS.expandClusterOrderDouble(ClusterOrderResult<DoubleDistance> clusterOrder,
Database database,
RangeQuery<O,DoubleDistance> rangeQuery,
DBID objectID,
DoubleDistance epsilon,
FiniteProgress progress)
OPTICS-function expandClusterOrder.
|
protected DistanceQuery<V,DoubleDistance> |
AbstractProjectedClustering.getDistanceQuery(Database database)
Returns the distance function.
|
C |
ClusteringAlgorithm.run(Database database) |
ClusterOrderResult<D> |
OPTICSTypeAlgorithm.run(Database database) |
Clustering<OPTICSModel> |
OPTICSXi.run(Database database,
Relation<?> relation) |
ClusterOrderResult<D> |
DeLiClu.run(Database database,
Relation<NV> relation) |
ClusterOrderResult<D> |
OPTICS.run(Database database,
Relation<O> relation)
Run OPTICS on the database.
|
Result |
SLINK.run(Database database,
Relation<O> relation)
Performs the SLINK algorithm on the given database.
|
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 | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
Clustering<Model> |
ByLabelClustering.run(Database database) |
Clustering<Model> |
ByLabelHierarchicalClustering.run(Database database) |
Modifier and Type | Method and Description |
---|---|
Database |
SOD.SODProxyScoreResult.getDatabase() |
Modifier and Type | Method and Description |
---|---|
protected DataStore<Double> |
DBOutlierDetection.computeOutlierScores(Database database,
DistanceQuery<O,D> distFunc,
D neighborhoodSize) |
protected abstract DataStore<Double> |
AbstractDBOutlier.computeOutlierScores(Database database,
DistanceQuery<O,D> distFunc,
D d)
computes an outlier score for each object of the database.
|
protected DataStore<Double> |
DBOutlierScore.computeOutlierScores(Database database,
DistanceQuery<O,D> distFunc,
D d) |
protected Pair<KNNQuery<O,D>,KNNQuery<O,D>> |
LoOP.getKNNQueries(Database database,
Relation<O> relation,
StepProgress stepprog)
Get the kNN queries for the algorithm.
|
OutlierResult |
INFLO.run(Database database) |
OutlierResult |
LOCI.run(Database database)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
OutlierAlgorithm.run(Database database) |
OutlierResult |
KNNOutlier.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
LoOP.run(Database database,
Relation<O> relation)
Performs the LoOP algorithm on the given database.
|
OutlierResult |
AbstractDBOutlier.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
OPTICSOF.run(Database database,
Relation<O> relation)
Perform OPTICS-based outlier detection.
|
OutlierResult |
LDOF.run(Database database,
Relation<O> relation) |
OutlierResult |
KNNWeightOutlier.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
ABOD.run(Database database,
Relation<V> relation)
Run ABOD on the data set
|
OutlierResult |
EMOutlier.run(Database database,
Relation<V> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
AggarwalYuEvolutionary.run(Database database,
Relation<V> relation)
Performs the evolutionary algorithm on the given database.
|
Modifier and Type | Method and Description |
---|---|
OutlierResult |
RescaleMetaOutlierAlgorithm.run(Database database) |
OutlierResult |
ExternalDoubleOutlierScore.run(Database database,
Relation<?> relation)
Run the algorithm.
|
Modifier and Type | Method and Description |
---|---|
OutlierResult |
TrimmedMeanApproach.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector<?,?>> relation)
Run the algorithm
|
OutlierResult |
CTLuZTestOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector<?,?>> relation)
Main method
|
OutlierResult |
SLOM.run(Database database,
Relation<N> spatial,
Relation<O> relation) |
OutlierResult |
SOF.run(Database database,
Relation<N> spatial,
Relation<O> relation)
The main run method
|
Modifier and Type | Method and Description |
---|---|
OutlierResult |
ByLabelOutlier.run(Database database) |
Modifier and Type | Method and Description |
---|---|
HistogramResult<DoubleVector> |
DistanceStatisticsWithClasses.run(Database database)
Iterates over all points in the database.
|
HistogramResult<DoubleVector> |
EvaluateRankingQuality.run(Database database)
Run the algorithm.
|
HistogramResult<DoubleVector> |
RankingQualityHistogram.run(Database database,
Relation<O> relation) |
Modifier and Type | Field and Description |
---|---|
private Database |
CacheFloatDistanceInOnDiskMatrix.database
Holds the database connection to have the algorithm run with.
|
private Database |
CacheFloatDistanceInOnDiskMatrix.Parameterizer.database
Holds the database connection to have the algorithm run with.
|
private Database |
CacheDoubleDistanceInOnDiskMatrix.database
Holds the database connection to have the algorithm run with.
|
private Database |
CacheDoubleDistanceInOnDiskMatrix.Parameterizer.database
Holds the database connection to have the algorithm run with.
|
Constructor and Description |
---|
CacheDoubleDistanceInOnDiskMatrix(boolean verbose,
Database database,
DistanceFunction<O,D> distance,
File out)
Constructor.
|
CacheFloatDistanceInOnDiskMatrix(boolean verbose,
Database database,
DistanceFunction<O,D> distance,
File out)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
private Database |
JSONWebServer.db
The database we use for obtaining object bundles
|
Modifier and Type | Field and Description |
---|---|
private Database |
KNNExplorer.database
Holds the database connection to have the algorithm run with.
|
protected Database |
KNNExplorer.Parameterizer.database |
private Database |
KNNExplorer.ExplorerWindow.db |
Modifier and Type | Method and Description |
---|---|
void |
KNNExplorer.ExplorerWindow.run(Database db,
DistanceQuery<O,D> distanceQuery)
Process the given Database and distance function.
|
Constructor and Description |
---|
KNNExplorer(boolean verbose,
Database database,
DistanceFunction<O,D> distanceFunction)
Constructor.
|
Modifier and Type | Interface and Description |
---|---|
interface |
UpdatableDatabase
Database API with updates.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDatabase
Abstract base class for database API implementations.
|
class |
HashmapDatabase
Provides a mapping for associations based on a Hashtable and functions to get
the next usable ID for insertion, making IDs reusable after deletion of the
entry.
|
class |
ProxyDatabase
A proxy database to use e.g. for projections and partitions.
|
class |
StaticArrayDatabase
This database class uses array-based storage and thus does not allow for
dynamic insert, delete and update operations.
|
Modifier and Type | Method and Description |
---|---|
static <O,D extends Distance<D>> |
QueryUtil.getDistanceQuery(Database database,
DistanceFunction<? super O,D> distanceFunction,
Object... hints)
Get a distance query for a given distance function, automatically choosing
a relation.
|
static <O,D extends Distance<D>> |
QueryUtil.getKNNQuery(Database database,
DistanceFunction<? super O,D> distanceFunction,
Object... hints)
Get a KNN query object for the given distance function.
|
static <O,D extends Distance<D>> |
QueryUtil.getRangeQuery(Database database,
DistanceFunction<? super O,D> distanceFunction,
Object... hints)
Get a range query object for the given distance function.
|
static <O,D extends Distance<D>> |
QueryUtil.getSimilarityQuery(Database database,
SimilarityFunction<? super O,D> similarityFunction,
Object... hints)
Get a similarity query, automatically choosing a relation.
|
Constructor and Description |
---|
ProxyDatabase(DBIDs ids,
Database database)
Constructor, proxying all relations of an existing database.
|
Modifier and Type | Field and Description |
---|---|
private Database |
ProxyView.database
Our database
|
private Database |
MaterializedRelation.database
Our database
|
private Database |
DBIDView.database
The database
|
Modifier and Type | Method and Description |
---|---|
Database |
ConvertToStringView.getDatabase() |
Database |
ProxyView.getDatabase() |
Database |
MaterializedRelation.getDatabase() |
Database |
DBIDView.getDatabase() |
Database |
Relation.getDatabase()
Get the associated database.
|
Modifier and Type | Method and Description |
---|---|
static <O> ProxyView<O> |
ProxyView.wrap(Database database,
DBIDs idview,
Relation<O> inner)
Constructor-like static method.
|
Constructor and Description |
---|
DBIDView(Database database,
DBIDs ids)
Constructor.
|
MaterializedRelation(Database database,
SimpleTypeInformation<O> type,
DBIDs ids)
Constructor.
|
MaterializedRelation(Database database,
SimpleTypeInformation<O> type,
DBIDs ids,
String name)
Constructor.
|
MaterializedRelation(Database database,
SimpleTypeInformation<O> type,
DBIDs ids,
String name,
DataStore<O> content)
Constructor.
|
ProxyView(Database database,
DBIDs idview,
Relation<O> inner)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
HistogramResult<DoubleVector> |
ComputeOutlierHistogram.evaluateOutlierResult(Database database,
OutlierResult or)
Evaluate a single outlier result as histogram.
|
Modifier and Type | Method and Description |
---|---|
static Database |
ResultUtil.findDatabase(Result baseResult)
Find the first database result in the tree.
|
Modifier and Type | Method and Description |
---|---|
static <O> void |
ResultUtil.ensureClusteringResult(Database db,
Result result)
Ensure that the result contains at least one Clustering.
|
static void |
ResultUtil.ensureSelectionResult(Database db,
Result result)
Ensure that there also is a selection container object.
|
private void |
KMLOutputHandler.writeKMLData(XMLStreamWriter out,
OutlierResult outlierResult,
Database database) |
Modifier and Type | Method and Description |
---|---|
Database |
ClusterOrderResult.ReachabilityDistanceAdapter.getDatabase() |
Database |
ClusterOrderResult.PredecessorAdapter.getDatabase() |
Modifier and Type | Method and Description |
---|---|
void |
TextWriter.output(Database db,
Result r,
StreamFactory streamOpener)
Stream output.
|
private void |
TextWriter.printObject(TextWriterStream out,
Database db,
DBID objID,
List<Relation<?>> ra) |
private void |
TextWriter.writeClusterResult(Database db,
StreamFactory streamOpener,
Cluster<?> clus,
List<Relation<?>> ra,
NamingScheme naming,
List<SettingsResult> sr) |
private void |
TextWriter.writeOrderingResult(Database db,
StreamFactory streamOpener,
OrderingResult or,
List<Relation<?>> ra,
List<SettingsResult> sr) |
Modifier and Type | Method and Description |
---|---|
static SortedSet<ClassLabel> |
DatabaseUtil.getClassLabels(Database database)
Retrieves all class labels within the database.
|
static ArrayModifiableDBIDs |
DatabaseUtil.getObjectsByLabelMatch(Database database,
Pattern name_pattern)
Find object by matching their labels.
|
static Relation<String> |
DatabaseUtil.guessLabelRepresentation(Database database)
Guess a potentially label-like representation.
|
static Relation<String> |
DatabaseUtil.guessObjectLabelRepresentation(Database database)
Guess a potentially object label-like representation.
|
Modifier and Type | Method and Description |
---|---|
static String |
VisualizerParameterizer.getTitle(Database db,
Result result)
Try to automatically generate a title for this.
|
Modifier and Type | Field and Description |
---|---|
(package private) Database |
SelectionTableWindow.database
The database we use
|
Modifier and Type | Field and Description |
---|---|
private Database |
EvaluationStep.Evaluation.database
Database
|
private Database |
InputStep.database
Holds the database to have the algorithms run with.
|
protected Database |
InputStep.Parameterizer.database
Holds the database to have the algorithms run on.
|
Modifier and Type | Method and Description |
---|---|
Database |
InputStep.getDatabase()
Get the database to use.
|
Modifier and Type | Method and Description |
---|---|
HierarchicalResult |
AlgorithmStep.runAlgorithms(Database database)
Run algorithms.
|
void |
EvaluationStep.runEvaluators(HierarchicalResult r,
Database db) |
Constructor and Description |
---|
EvaluationStep.Evaluation(Database database,
List<Evaluator> evaluators)
Constructor.
|
InputStep(Database database)
Constructor.
|