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.hierarchical |
Hierarchical agglomerative clustering (HAC).
|
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.uncertain |
Clustering algorithms for uncertain data.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.meta |
Meta outlier detection algorithms: external scores, score rescaling.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood |
Spatial outlier neighborhood classes
|
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.jsmap |
JavaScript based map client - server architecture.
|
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.datastore |
General data store layer API (along the lines of
Map<DBID, T> - use everywhere!) |
de.lmu.ifi.dbs.elki.database.datastore.memory |
Memory data store implementation for ELKI.
|
de.lmu.ifi.dbs.elki.database.relation |
Relations, materialized and virtual (views).
|
de.lmu.ifi.dbs.elki.evaluation |
Functionality for the evaluation of algorithms.
|
de.lmu.ifi.dbs.elki.evaluation.classification |
Evaluation of classification algorithms.
|
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.histogram |
Functionality for the evaluation of algorithms using histograms.
|
de.lmu.ifi.dbs.elki.evaluation.index |
Simple index evaluation methods
|
de.lmu.ifi.dbs.elki.evaluation.outlier |
Evaluate an outlier score using a misclassification based cost model.
|
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.preprocessed |
Index structure based on preprocessors
|
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 |
Tree-based index structures
|
de.lmu.ifi.dbs.elki.index.tree.metrical |
Tree-based index structures for metrical vector spaces.
|
de.lmu.ifi.dbs.elki.index.tree.metrical.covertree |
Cover-tree variations.
|
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants |
M-Tree and variants.
|
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 |
Tree-based index structures for spatial indexing.
|
de.lmu.ifi.dbs.elki.index.tree.spatial.kd |
K-d-tree and variants.
|
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants |
R*-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.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.geometry |
Algorithms from computational geometry.
|
de.lmu.ifi.dbs.elki.result |
Result types, representation and handling
|
de.lmu.ifi.dbs.elki.result.outlier |
Outlier result classes
|
de.lmu.ifi.dbs.elki.result.textwriter |
Text serialization (CSV, Gnuplot, Console, ...)
|
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.visualization.gui.detail |
Classes for managing a detail view.
|
de.lmu.ifi.dbs.elki.visualization.gui.overview |
Classes for managing the overview plot.
|
de.lmu.ifi.dbs.elki.visualization.opticsplot |
Code for drawing OPTICS plots
|
de.lmu.ifi.dbs.elki.visualization.visualizers |
Visualizers for various results
|
de.lmu.ifi.dbs.elki.visualization.visualizers.histogram |
Visualizers based on 1D projected histograms.
|
de.lmu.ifi.dbs.elki.visualization.visualizers.optics |
Visualizers that do work on OPTICS plots
|
de.lmu.ifi.dbs.elki.visualization.visualizers.pairsegments |
Visualizers for inspecting cluster differences using pair counting segments.
|
de.lmu.ifi.dbs.elki.visualization.visualizers.thumbs |
Thumbnail "Visualizers" (that take care of refreshing thumbnails)
|
de.lmu.ifi.dbs.elki.workflow |
Work flow packages, e.g. following the usual KDD model, closely related to CRISP-DM
|
tutorial.clustering |
Classes from the tutorial on implementing a custom k-means variation.
|
tutorial.outlier |
Modifier and Type | Class and Description |
---|---|
class |
AbstractAlgorithm<R extends Result>
This class serves also as a model of implementing an algorithm within this
framework.
|
class |
AbstractDistanceBasedAlgorithm<O,R extends Result>
Abstract base class for distance-based algorithms.
|
class |
AbstractNumberVectorDistanceBasedAlgorithm<O,R extends Result>
Abstract base class for distance-based algorithms that need to work with
synthetic numerical vectors such as mean vectors.
|
class |
AbstractPrimitiveDistanceBasedAlgorithm<O,R extends Result>
Abstract base class for distance-based algorithms that need to work with
synthetic objects such as mean vectors.
|
Modifier and Type | Class and Description |
---|---|
static class |
KNNDistancesSampler.KNNDistanceOrderResult
Curve result for a list containing the knn distances.
|
Modifier and Type | Method and Description |
---|---|
Result |
NullAlgorithm.run(Database database) |
Result |
Algorithm.run(Database database)
Runs the algorithm.
|
Result |
DummyAlgorithm.run(Database database,
Relation<O> relation)
Run the algorithm.
|
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
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractClassifier<O,R extends Result>
Abstract base class for algorithms.
|
Modifier and Type | Method and Description |
---|---|
Result |
KNNClassifier.run(Database database)
Deprecated.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractHDBSCAN<O,R extends Result>
Abstract base class for HDBSCAN variations.
|
Modifier and Type | Class and Description |
---|---|
class |
PointerDensityHierarchyRepresentationResult
Extended pointer representation useful for HDBSCAN.
|
class |
PointerHierarchyRepresentationResult
The pointer representation of a hierarchical clustering.
|
Modifier and Type | Class and Description |
---|---|
class |
ClusterOrder
Class to store the result of an ordering clustering algorithm such as OPTICS.
|
class |
CorrelationClusterOrder
Cluster order entry for correlation-based OPTICS variants.
|
static class |
OPTICSXi.SteepAreaResult
Result containing the chi-steep areas.
|
Modifier and Type | Class and Description |
---|---|
static class |
DiSH.DiSHClusterOrder
DiSH cluster order.
|
Modifier and Type | Class and Description |
---|---|
static class |
RepresentativeUncertainClustering.RepresentativenessEvaluation
Representativeness evaluation result.
|
Modifier and Type | Method and Description |
---|---|
protected C |
CenterOfMassMetaClustering.runClusteringAlgorithm(ResultHierarchy hierarchy,
Result parent,
DBIDs ids,
DataStore<DoubleVector> store,
int dim,
String title)
Run a clustering algorithm on a single instance.
|
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 | Method and Description |
---|---|
private OutlierResult |
RescaleMetaOutlierAlgorithm.getOutlierResult(ResultHierarchy hier,
Result result)
Find an OutlierResult to work with.
|
Modifier and Type | Interface and Description |
---|---|
interface |
NeighborSetPredicate
Predicate to obtain the neighbors of a reference object as set.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractPrecomputedNeighborhood
Abstract base class for precomputed neighborhoods.
|
class |
ExtendedNeighborhood
Neighborhood obtained by computing the k-fold closure of an existing
neighborhood.
|
class |
ExternalNeighborhood
A precomputed neighborhood, loaded from an external file.
|
class |
PrecomputedKNearestNeighborNeighborhood
Neighborhoods based on k nearest neighbors.
|
Modifier and Type | Class and Description |
---|---|
static class |
EvaluateRetrievalPerformance.RetrievalPerformanceResult
Result object for MAP scores.
|
Modifier and Type | Method and Description |
---|---|
Result |
AddSingleScale.run(Database database) |
Result |
HopkinsStatisticClusteringTendency.run(Database database,
Relation<NumberVector> relation)
Runs the algorithm in the timed evaluation part.
|
Result |
EstimateIntrinsicDimensionality.run(Database database,
Relation<O> relation) |
Modifier and Type | Field and Description |
---|---|
private Result |
JSONWebServer.baseResult
Starting point.
|
Modifier and Type | Method and Description |
---|---|
void |
JSONResultHandler.processNewResult(ResultHierarchy hier,
Result newResult) |
Constructor and Description |
---|
JSONWebServer(int port,
ResultHierarchy hier,
Result baseResult)
Constructor.
|
Modifier and Type | Class and Description |
---|---|
class |
Clustering<M extends Model>
Result class for clusterings.
|
Modifier and Type | Class and Description |
---|---|
class |
CorrelationAnalysisSolution<V extends NumberVector>
A solution of correlation analysis is a matrix of equations describing the
dependencies.
|
Modifier and Type | Interface and Description |
---|---|
interface |
Database
Database specifies the requirements for any database implementation.
|
interface |
UpdatableDatabase
Database API with updates.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDatabase
Abstract base class for database API implementations.
|
class |
HashmapDatabase
Database storing data using hashtable storage, and thus allowing additional
and removal of objects.
|
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 |
---|---|
void |
DatabaseEventManager.fireResultAdded(Result r,
Result parent)
Informs all registered
ResultListener that a new result was
added. |
void |
DatabaseEventManager.fireResultRemoved(Result r,
Result parent)
Informs all registered
ResultListener that a new result has
been removed. |
Modifier and Type | Interface and Description |
---|---|
interface |
DataStore<T>
Generic storage interface for objects indexed by
DBID . |
interface |
DBIDDataStore
DBID-valued data store (avoids boxing/unboxing).
|
interface |
DoubleDataStore
Double-valued data store (avoids boxing/unboxing).
|
interface |
IntegerDataStore
Integer-valued data store (avoids boxing/unboxing).
|
interface |
WritableDataStore<T>
Writable data store.
|
interface |
WritableDBIDDataStore
Data store specialized for doubles.
|
interface |
WritableDoubleDataStore
Data store specialized for doubles.
|
interface |
WritableIntegerDataStore
Data store specialized for doubles.
|
Modifier and Type | Class and Description |
---|---|
class |
ArrayDBIDStore
A class to answer representation queries using the stored Array.
|
class |
ArrayDoubleStore
A class to answer representation queries using the stored Array.
|
class |
ArrayIntegerStore
A class to answer representation queries using the stored Array.
|
protected class |
ArrayRecordStore.StorageAccessor<T>
Access a single record in the given data.
|
class |
ArrayStore<T>
A class to answer representation queries using the stored Array.
|
class |
MapIntegerDBIDDBIDStore
Writable data store for double values.
|
class |
MapIntegerDBIDDoubleStore
Writable data store for double values.
|
class |
MapIntegerDBIDIntegerStore
Writable data store for double values.
|
protected class |
MapIntegerDBIDRecordStore.StorageAccessor<T>
Access a single record in the given data.
|
class |
MapIntegerDBIDStore<T>
A class to answer representation queries using a map.
|
protected class |
MapRecordStore.StorageAccessor<T>
Access a single record in the given data.
|
class |
MapStore<T>
A class to answer representation queries using a map.
|
Modifier and Type | Interface and Description |
---|---|
interface |
DoubleRelation
Interface for double-valued relations.
|
interface |
ModifiableRelation<O>
Relations that allow modification.
|
interface |
Relation<O>
An object representation from a database.
|
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 | Method and Description |
---|---|
protected void |
AutomaticEvaluation.autoEvaluateClusterings(ResultHierarchy hier,
Result newResult) |
protected void |
AutomaticEvaluation.autoEvaluateOutliers(ResultHierarchy hier,
Result newResult) |
void |
NoAutomaticEvaluation.processNewResult(ResultHierarchy hier,
Result newResult) |
void |
AutomaticEvaluation.processNewResult(ResultHierarchy hier,
Result newResult) |
Modifier and Type | Class and Description |
---|---|
class |
ConfusionMatrixEvaluationResult
Provides the prediction performance measures for a classifier based on the
confusion matrix.
|
Modifier and Type | Class and Description |
---|---|
static class |
EvaluateClustering.ScoreResult
Result object for outlier score judgements.
|
Modifier and Type | Method and Description |
---|---|
void |
EvaluateClustering.processNewResult(ResultHierarchy hier,
Result newResult) |
void |
LogClusterSizes.processNewResult(ResultHierarchy hier,
Result result) |
Modifier and Type | Method and Description |
---|---|
void |
EvaluateSimplifiedSilhouette.processNewResult(ResultHierarchy hier,
Result result) |
void |
EvaluateVarianceRatioCriteria.processNewResult(ResultHierarchy hier,
Result result) |
void |
EvaluateConcordantPairs.processNewResult(ResultHierarchy hier,
Result result) |
void |
EvaluateDaviesBouldin.processNewResult(ResultHierarchy hier,
Result result) |
void |
EvaluateSquaredErrors.processNewResult(ResultHierarchy hier,
Result result) |
void |
EvaluatePBMIndex.processNewResult(ResultHierarchy hier,
Result result) |
void |
EvaluateCIndex.processNewResult(ResultHierarchy hier,
Result result) |
void |
EvaluateSilhouette.processNewResult(ResultHierarchy hier,
Result result) |
Modifier and Type | Class and Description |
---|---|
class |
Segments
Creates segments of two or more clusterings.
|
Modifier and Type | Method and Description |
---|---|
void |
ClusterPairSegmentAnalysis.processNewResult(ResultHierarchy hier,
Result result)
Perform clusterings evaluation
|
Modifier and Type | Method and Description |
---|---|
void |
ComputeOutlierHistogram.processNewResult(ResultHierarchy hier,
Result newResult) |
Modifier and Type | Class and Description |
---|---|
class |
IndexStatistics.IndexMetaResult
Result class.
|
Modifier and Type | Method and Description |
---|---|
void |
IndexPurity.processNewResult(ResultHierarchy hier,
Result newResult) |
void |
IndexStatistics.processNewResult(ResultHierarchy hier,
Result newResult) |
Modifier and Type | Class and Description |
---|---|
class |
JudgeOutlierScores.ScoreResult
Result object for outlier score judgements.
|
static class |
OutlierPrecisionAtKCurve.PrecisionAtKCurve
Precision at K curve.
|
static class |
OutlierPrecisionRecallCurve.PRCurve
P/R Curve
|
static class |
OutlierROCCurve.ROCResult
Result object for ROC curves.
|
static class |
OutlierSmROCCurve.SmROCResult
Result object for Smooth ROC curves.
|
Modifier and Type | Method and Description |
---|---|
void |
OutlierROCCurve.processNewResult(ResultHierarchy hier,
Result result) |
void |
OutlierRankingEvaluation.processNewResult(ResultHierarchy hier,
Result result) |
void |
OutlierThresholdClustering.processNewResult(ResultHierarchy hier,
Result newResult) |
void |
JudgeOutlierScores.processNewResult(ResultHierarchy hier,
Result result) |
void |
OutlierSmROCCurve.processNewResult(ResultHierarchy hier,
Result result) |
void |
OutlierPrecisionRecallCurve.processNewResult(ResultHierarchy hier,
Result result) |
void |
OutlierPrecisionAtKCurve.processNewResult(ResultHierarchy hier,
Result result) |
Modifier and Type | Class and Description |
---|---|
static class |
ComputeSimilarityMatrixImage.SimilarityMatrix
Similarity matrix image.
|
Modifier and Type | Method and Description |
---|---|
void |
ComputeSimilarityMatrixImage.processNewResult(ResultHierarchy hier,
Result result) |
Modifier and Type | Interface and Description |
---|---|
interface |
DistanceIndex<O>
Index with support for distance queries (e.g. precomputed distance matrixes,
caches)
|
interface |
DynamicIndex
Index that supports dynamic insertions and removals.
|
interface |
Index
Interface defining the minimum requirements for all index classes.
|
interface |
KNNIndex<O>
Index with support for kNN queries.
|
interface |
RangeIndex<O>
Index with support for kNN queries.
|
interface |
RKNNIndex<O>
Index with support for kNN queries.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractIndex<O>
Abstract base class for indexes with some implementation defaults.
|
class |
AbstractRefiningIndex<O>
Abstract base class for Filter-refinement indexes.
|
Modifier and Type | Class and Description |
---|---|
class |
PrecomputedDistanceMatrix<O>
Distance matrix, for precomputing similarity for a small data set.
|
Modifier and Type | Class and Description |
---|---|
class |
InMemoryIDistanceIndex<O>
In-memory iDistance index, a metric indexing method using a reference point
embedding.
|
Modifier and Type | Class and Description |
---|---|
class |
InMemoryInvertedIndex<V extends NumberVector>
Simple index using inverted lists.
|
Modifier and Type | Class and Description |
---|---|
class |
InMemoryLSHIndex.Instance
Instance of a LSH index for a single relation.
|
Modifier and Type | Interface and Description |
---|---|
interface |
LocalProjectionIndex<V extends NumberVector,P extends ProjectionResult>
Abstract index interface for local projections
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractPreprocessorIndex<O,R>
Abstract base class for simple preprocessor based indexes, requiring a simple
object storage for preprocessing results.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractMaterializeKNNPreprocessor<O>
Abstract base class for KNN Preprocessors.
|
class |
CachedDoubleDistanceKNNPreprocessor<O>
Preprocessor that loads an existing cached kNN result.
|
class |
KNNJoinMaterializeKNNPreprocessor<V extends NumberVector>
Class to materialize the kNN using a spatial join on an R-tree.
|
class |
MaterializeKNNAndRKNNPreprocessor<O>
A preprocessor for annotation of the k nearest neighbors and the reverse k
nearest neighbors (and their distances) to each database object.
|
class |
MaterializeKNNPreprocessor<O>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
class |
MetricalIndexApproximationMaterializeKNNPreprocessor<O extends NumberVector,N extends Node<E>,E extends MTreeEntry>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
class |
NaiveProjectedKNNPreprocessor<O extends NumberVector>
Compute the approximate k nearest neighbors using 1 dimensional projections.
|
class |
PartitionApproximationMaterializeKNNPreprocessor<O>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
class |
RandomSampleKNNPreprocessor<O>
Class that computed the kNN only on a random sample.
|
class |
SpacefillingKNNPreprocessor<O extends NumberVector>
Compute the nearest neighbors approximatively using space filling curves.
|
class |
SpacefillingMaterializeKNNPreprocessor<O extends NumberVector>
Compute the nearest neighbors approximatively using space filling curves.
|
class |
SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
Modifier and Type | Interface and Description |
---|---|
interface |
FilteredLocalPCAIndex<NV extends NumberVector>
Interface for an index providing local PCA results.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractFilteredPCAIndex<NV extends NumberVector>
Abstract base class for a local PCA based index.
|
class |
KNNQueryFilteredPCAIndex<NV extends NumberVector>
Provides the local neighborhood to be considered in the PCA as the k nearest
neighbors of an object.
|
Modifier and Type | Interface and Description |
---|---|
interface |
PreferenceVectorIndex<NV extends NumberVector>
Interface for an index providing preference vectors.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractPreferenceVectorIndex<NV extends NumberVector>
Abstract base class for preference vector based algorithms.
|
class |
DiSHPreferenceVectorIndex<V extends NumberVector>
Preprocessor for DiSH preference vector assignment to objects of a certain
database.
|
class |
HiSCPreferenceVectorIndex<V extends NumberVector>
Preprocessor for HiSC preference vector assignment to objects of a certain
database.
|
Modifier and Type | Interface and Description |
---|---|
interface |
SharedNearestNeighborIndex<O>
Interface for an index providing nearest neighbor sets.
|
Modifier and Type | Class and Description |
---|---|
class |
SharedNearestNeighborPreprocessor<O>
A preprocessor for annotation of the ids of nearest neighbors to each
database object.
|
Modifier and Type | Class and Description |
---|---|
class |
LatLngAsECEFIndex<O extends NumberVector>
Index a 2d data set (consisting of Lat/Lng pairs) by using a projection to 3D
coordinates (WGS-86 to ECEF).
|
class |
LngLatAsECEFIndex<O extends NumberVector>
Index a 2d data set (consisting of Lng/Lat pairs) by using a projection to 3D
coordinates (WGS-86 to ECEF).
|
class |
ProjectedIndex<O,I>
Class to index data in an arbitrary projection only.
|
Modifier and Type | Class and Description |
---|---|
class |
IndexTree<N extends Node<E>,E extends Entry>
Abstract super class for all tree based index classes.
|
Modifier and Type | Class and Description |
---|---|
class |
MetricalIndexTree<O,N extends Node<E>,E extends Entry>
Abstract super class for all metrical index classes.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractCoverTree<O>
Abstract base class for cover tree variants.
|
class |
CoverTree<O>
Cover tree data structure (in-memory).
|
class |
SimplifiedCoverTree<O>
Simplified cover tree data structure (in-memory).
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractMTree<O,N extends AbstractMTreeNode<O,N,E>,E extends MTreeEntry,S extends MTreeSettings<O,N,E>>
Abstract super class for all M-Tree variants.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractMkTree<O,N extends AbstractMTreeNode<O,N,E>,E extends MTreeEntry,S extends MTreeSettings<O,N,E>>
Abstract class for all M-Tree variants supporting processing of reverse
k-nearest neighbor queries by using the k-nn distances of the entries, where
k is less than or equal to the given parameter.
|
class |
AbstractMkTreeUnified<O,N extends AbstractMTreeNode<O,N,E>,E extends MTreeEntry,S extends MkTreeSettings<O,N,E>>
Abstract class for all M-Tree variants supporting processing of reverse
k-nearest neighbor queries by using the k-nn distances of the entries, where
k is less than or equal to the given parameter.
|
Modifier and Type | Class and Description |
---|---|
class |
MkAppTree<O>
MkAppTree is a metrical index structure based on the concepts of the M-Tree
supporting efficient processing of reverse k nearest neighbor queries for
parameter k < kmax.
|
class |
MkAppTreeIndex<O>
MkAppTree used as database index.
|
Modifier and Type | Class and Description |
---|---|
class |
MkCoPTree<O>
MkCopTree is a metrical index structure based on the concepts of the M-Tree
supporting efficient processing of reverse k nearest neighbor queries for
parameter k < kmax.
|
class |
MkCoPTreeIndex<O>
MkCoPTree used as database index.
|
Modifier and Type | Class and Description |
---|---|
class |
MkMaxTree<O>
MkMaxTree is a metrical index structure based on the concepts of the M-Tree
supporting efficient processing of reverse k nearest neighbor queries for
parameter k <= k_max.
|
class |
MkMaxTreeIndex<O>
MkMax tree
|
Modifier and Type | Class and Description |
---|---|
class |
MkTabTree<O>
MkTabTree is a metrical index structure based on the concepts of the M-Tree
supporting efficient processing of reverse k nearest neighbor queries for
parameter k < kmax.
|
class |
MkTabTreeIndex<O>
MkTabTree used as database index.
|
Modifier and Type | Class and Description |
---|---|
class |
MTree<O>
MTree is a metrical index structure based on the concepts of the M-Tree.
|
class |
MTreeIndex<O>
Class for using an m-tree as database index.
|
Modifier and Type | Class and Description |
---|---|
class |
SpatialIndexTree<N extends SpatialNode<N,E>,E extends SpatialEntry>
Abstract super class for all spatial index tree classes.
|
Modifier and Type | Class and Description |
---|---|
class |
MinimalisticMemoryKDTree<O extends NumberVector>
Simple implementation of a static in-memory K-D-tree.
|
class |
SmallMemoryKDTree<O extends NumberVector>
Simple implementation of a static in-memory K-D-tree.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractRStarTree<N extends AbstractRStarTreeNode<N,E>,E extends SpatialEntry,S extends AbstractRTreeSettings>
Abstract superclass for index structures based on a R*-Tree.
|
class |
NonFlatRStarTree<N extends AbstractRStarTreeNode<N,E>,E extends SpatialEntry,S extends AbstractRTreeSettings>
Abstract superclass for all non-flat R*-Tree variants.
|
Modifier and Type | Class and Description |
---|---|
class |
DeLiCluTree
DeLiCluTree is a spatial index structure based on an R-Tree.
|
class |
DeLiCluTreeIndex<O extends NumberVector>
The common use of the DeLiClu tree: indexing number vectors.
|
Modifier and Type | Class and Description |
---|---|
class |
FlatRStarTree
FlatRTree is a spatial index structure based on a R*-Tree but with a flat
directory.
|
class |
FlatRStarTreeIndex<O extends NumberVector>
The common use of the flat rstar tree: indexing number vectors.
|
Modifier and Type | Class and Description |
---|---|
class |
RdKNNTree<O extends NumberVector>
RDkNNTree is a spatial index structure based on the concepts of the R*-Tree
supporting efficient processing of reverse k nearest neighbor queries.
|
Modifier and Type | Class and Description |
---|---|
class |
RStarTree
RStarTree is a spatial index structure based on the concepts of the R*-Tree.
|
class |
RStarTreeIndex<O extends NumberVector>
The common use of the rstar tree: indexing number vectors.
|
Modifier and Type | Class and Description |
---|---|
class |
PartialVAFile<V extends NumberVector>
PartialVAFile.
|
class |
VAFile<V extends NumberVector>
Vector-approximation file (VAFile)
Reference:
Weber, R. and Blott, S.
|
Modifier and Type | Class and Description |
---|---|
class |
XYCurve
An XYCurve is an ordered collection of 2d points, meant for chart generation.
|
class |
XYPlot
An XYCurve is an ordered collection of 2d
XYPlot.Curve s, meant for chart
generation. |
Modifier and Type | Interface and Description |
---|---|
interface |
HierarchicalResult
Result with an internal hierarchy.
|
interface |
IterableResult<O>
Interface of an "iterable" result (e.g. a list, table) that can be printed one-by-one.
|
interface |
OrderingResult
Interface for a result providing an object ordering.
|
interface |
PixmapResult
Result encapsulating a single image.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractHierarchicalResult
Abstract class for a result object with hierarchy
|
class |
BasicResult
Basic class for a result.
|
class |
CollectionResult<O>
Simple 'collection' type of result.
|
class |
EvaluationResult
Abstract evaluation result.
|
class |
FrequentItemsetsResult
Result class for Apriori Algorithm.
|
class |
HistogramResult<O>
Histogram result.
|
class |
OrderingFromDataStore<T extends Comparable<T>>
Result class providing an ordering backed by a hashmap.
|
class |
ReferencePointsResult<O>
Result used in passing the reference points to the visualizers.
|
class |
SamplingResult
Wrapper for storing the current database sample.
|
class |
ScalesResult
Class to keep shared scales across visualizers.
|
class |
SelectionResult
Selection result wrapper.
|
class |
SettingsResult
Result that keeps track of settings that were used in generating this
particular result.
|
Modifier and Type | Method and Description |
---|---|
static <C extends Result> |
ResultUtil.filterResults(ResultHierarchy hier,
Class<? super C> restrictionClass)
Return only results of the given restriction class
|
static <C extends Result> |
ResultUtil.filterResults(ResultHierarchy hier,
Result r,
Class<? super C> restrictionClass)
Return only results of the given restriction class
|
Modifier and Type | Method and Description |
---|---|
boolean |
ResultHierarchy.add(Result parent,
Result child) |
static void |
ResultUtil.addChildResult(HierarchicalResult parent,
Result child)
Add a child result.
|
void |
AbstractHierarchicalResult.addChildResult(Result child)
Add a child result.
|
static void |
ResultUtil.ensureClusteringResult(Database db,
Result result)
Ensure that the result contains at least one Clustering.
|
static <C extends Result> |
ResultUtil.filterResults(ResultHierarchy hier,
Result r,
Class<? super C> restrictionClass)
Return only results of the given restriction class
|
static Database |
ResultUtil.findDatabase(ResultHierarchy hier,
Result baseResult)
Find the first database result in the tree.
|
static EvaluationResult |
EvaluationResult.findOrCreate(ResultHierarchy hierarchy,
Result parent,
String name,
String shortname)
Find or create an evaluation result.
|
private void |
ResultHierarchy.fireResultAdded(Result child,
Result parent)
Informs all registered
ResultListener that a new result was added. |
private void |
ResultHierarchy.fireResultChanged(Result current)
Informs all registered
ResultListener that a result has changed. |
private void |
ResultHierarchy.fireResultRemoved(Result child,
Result parent)
Informs all registered
ResultListener that a new result has been
removed. |
static List<Clustering<? extends Model>> |
ResultUtil.getClusteringResults(Result r)
Collect all clustering results from a Result
|
static List<CollectionResult<?>> |
ResultUtil.getCollectionResults(Result r)
Collect all collection results from a Result
|
static List<IterableResult<?>> |
ResultUtil.getIterableResults(Result r)
Return all Iterable results
|
static List<OrderingResult> |
ResultUtil.getOrderingResults(Result r)
Collect all ordering results from a Result
|
static List<OutlierResult> |
ResultUtil.getOutlierResults(Result r)
Collect all outlier results from a Result
|
static List<Relation<?>> |
ResultUtil.getRelations(Result r)
Collect all Annotation results from a Result
|
static List<SettingsResult> |
ResultUtil.getSettingsResults(Result r)
Collect all settings results from a Result
|
void |
LogResultStructureResultHandler.processNewResult(ResultHierarchy hier,
Result newResult) |
void |
DiscardResultHandler.processNewResult(ResultHierarchy hier,
Result newResult) |
void |
KMLOutputHandler.processNewResult(ResultHierarchy hier,
Result newResult) |
void |
ResultProcessor.processNewResult(ResultHierarchy hier,
Result newResult)
Process a result.
|
void |
ResultWriter.processNewResult(ResultHierarchy hier,
Result result) |
void |
ClusteringVectorDumper.processNewResult(ResultHierarchy hier,
Result newResult) |
private void |
LogResultStructureResultHandler.recursiveLogResult(StringBuilder buf,
Hierarchy<Result> hier,
Result result,
int depth)
Recursively walk through the result tree.
|
boolean |
ResultHierarchy.remove(Result parent,
Result child) |
static void |
ResultUtil.removeRecursive(ResultHierarchy hierarchy,
Result child)
Recursively remove a result and its children.
|
void |
ResultListener.resultAdded(Result child,
Result parent)
A new derived result was added.
|
void |
ResultHierarchy.resultChanged(Result res)
Signal that a result has changed (public API)
|
void |
ResultListener.resultChanged(Result current)
Notify that the current result has changed substantially.
|
void |
ResultListener.resultRemoved(Result child,
Result parent)
A result was removed.
|
Modifier and Type | Method and Description |
---|---|
private void |
LogResultStructureResultHandler.recursiveLogResult(StringBuilder buf,
Hierarchy<Result> hier,
Result result,
int depth)
Recursively walk through the result tree.
|
Modifier and Type | Interface and Description |
---|---|
interface |
OutlierScoreMeta
Generic meta information about the value range of an outlier score.
|
Modifier and Type | Class and Description |
---|---|
class |
BasicOutlierScoreMeta
Basic outlier score.
|
class |
InvertedOutlierScoreMeta
Class to signal a value-inverted outlier score, i.e. low values are outliers.
|
class |
OrderingFromRelation
Ordering obtained from an outlier score.
|
class |
OutlierResult
Wrap a typical Outlier result, keeping direct references to the main result
parts.
|
class |
ProbabilisticOutlierScore
Outlier score that is a probability value in the range 0.0 - 1.0
But the baseline may be different from 0.0!
|
class |
QuotientOutlierScoreMeta
Score for outlier values generated by a quotient.
|
Modifier and Type | Method and Description |
---|---|
void |
TextWriter.output(Database db,
Result r,
StreamFactory streamOpener,
Pattern filter)
Stream output.
|
private void |
TextWriter.writeOtherResult(StreamFactory streamOpener,
Result r) |
Modifier and Type | Class and Description |
---|---|
class |
VisualizerContext
Map to store context information for the visualizer.
|
Modifier and Type | Field and Description |
---|---|
private Result |
VisualizerContext.baseResult
Starting point of the result tree, may be
null . |
(package private) Result |
ExportVisualizations.baseResult
Base result
|
Modifier and Type | Method and Description |
---|---|
static <O extends Result> |
VisualizationTree.filterResults(VisualizerContext context,
Object start,
Class<? super O> clazz)
Filtered iteration over the primary result tree.
|
static <A extends Result,B extends VisualizationItem> |
VisualizationTree.findNewResultVis(VisualizerContext context,
Object start,
Class<? super A> type1,
Class<? super B> type2,
VisualizationTree.Handler2<A,B> handler)
Process new result combinations of an object type1 (in first hierarchy)
having a child of type2 (in second hierarchy).
|
static <A extends Result,B extends VisualizationItem> |
VisualizationTree.findNewSiblings(VisualizerContext context,
Object start,
Class<? super A> type1,
Class<? super B> type2,
VisualizationTree.Handler2<A,B> handler)
Process new result combinations of an object type1 (in first hierarchy) and
any child of type2 (in second hierarchy)
This is a bit painful, because we have two hierarchies with different
types: results, and visualizations.
|
Modifier and Type | Method and Description |
---|---|
Result |
VisualizerContext.getBaseResult()
Starting point for visualization, may be
null . |
Modifier and Type | Method and Description |
---|---|
static String |
VisualizerParameterizer.getTitle(Database db,
Result result)
Try to automatically generate a title for this.
|
VisualizerContext |
VisualizerParameterizer.newContext(ResultHierarchy hier,
Result start)
Make a new visualization context
|
void |
ExportVisualizations.processNewResult(ResultHierarchy hier,
Result newResult) |
Constructor and Description |
---|
VisualizerContext(ResultHierarchy hier,
Result start,
Relation<?> relation,
StyleLibrary stylelib,
Collection<VisualizationProcessor> factories)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
void |
ResultVisualizer.processNewResult(ResultHierarchy hier,
Result result) |
void |
ResultWindow.resultAdded(Result child,
Result parent) |
void |
SelectionTableWindow.resultAdded(Result child,
Result parent) |
void |
ResultWindow.resultChanged(Result current) |
void |
SelectionTableWindow.resultChanged(Result current) |
void |
ResultWindow.resultRemoved(Result child,
Result parent) |
void |
SelectionTableWindow.resultRemoved(Result child,
Result parent) |
Modifier and Type | Method and Description |
---|---|
void |
DetailView.resultAdded(Result child,
Result parent) |
void |
DetailView.resultChanged(Result current) |
void |
DetailView.resultRemoved(Result child,
Result parent) |
Modifier and Type | Method and Description |
---|---|
void |
OverviewPlot.resultAdded(Result child,
Result parent) |
void |
OverviewPlot.resultChanged(Result current) |
void |
OverviewPlot.resultRemoved(Result child,
Result parent) |
Modifier and Type | Class and Description |
---|---|
class |
OPTICSPlot
Class to produce an OPTICS plot image.
|
Modifier and Type | Method and Description |
---|---|
void |
AbstractVisualization.resultAdded(Result child,
Result parent) |
void |
AbstractVisualization.resultChanged(Result current) |
void |
AbstractVisualization.resultRemoved(Result child,
Result parent) |
Modifier and Type | Method and Description |
---|---|
void |
AbstractHistogramVisualization.resultChanged(Result current) |
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 |
---|---|
void |
CircleSegmentsVisualizer.Instance.resultChanged(Result current) |
Modifier and Type | Method and Description |
---|---|
void |
ThumbnailVisualization.resultChanged(Result current) |
Modifier and Type | Field and Description |
---|---|
private Result |
AlgorithmStep.stepresult
The algorithm output
|
private Result |
EvaluationStep.stepresult
Result.
|
Modifier and Type | Method and Description |
---|---|
Result |
AlgorithmStep.getResult()
Get the result.
|
Result |
EvaluationStep.getResult()
Return the result.
|
Result |
AlgorithmStep.runAlgorithms(Database database)
Run algorithms.
|
Modifier and Type | Method and Description |
---|---|
void |
EvaluationStep.Evaluation.resultAdded(Result child,
Result parent) |
void |
EvaluationStep.Evaluation.resultChanged(Result current) |
void |
EvaluationStep.Evaluation.resultRemoved(Result child,
Result parent) |
void |
EvaluationStep.Evaluation.update(Result r)
Update on a particular result.
|
Modifier and Type | Method and Description |
---|---|
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
|
Modifier and Type | Method and Description |
---|---|
void |
SimpleScoreDumper.processNewResult(ResultHierarchy hier,
Result newResult) |
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.