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Packages that use AbstractParameterizable | |
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de.lmu.ifi.dbs.elki | ELKI framework "Environment for Developing KDD-Applications Supported by Index-Structures"
KDDTask is the main class of the ELKI-Framework
for command-line interaction. |
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.outlier | Outlier detection algorithms |
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 | Base classes for stand alone applications. |
de.lmu.ifi.dbs.elki.application.cache | Utility applications for the persistence layer such as distance cache builders. |
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.connection | Database connections are classes implementing data sources. |
de.lmu.ifi.dbs.elki.distance | Distances and (in subpackages) distance functions and similarity functions . |
de.lmu.ifi.dbs.elki.distance.distancefunction | Distance functions for use within ELKI. |
de.lmu.ifi.dbs.elki.distance.distancefunction.adapter | Distance functions deriving distances from e.g. similarity measures |
de.lmu.ifi.dbs.elki.distance.distancefunction.correlation | Distance functions using correlations. |
de.lmu.ifi.dbs.elki.distance.distancefunction.external | Distance functions using external data sources. |
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace | Distance functions based on subspaces. |
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries | Distance functions designed for time series. |
de.lmu.ifi.dbs.elki.distance.similarityfunction | Similarity functions. |
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel | Kernel functions. |
de.lmu.ifi.dbs.elki.evaluation | Functionality for the evaluation of algorithms. |
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.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 | MkAppTree |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop | MkCoPTree |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax | MkMaxTree |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab | MkTabTree |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree | MTree |
de.lmu.ifi.dbs.elki.index.tree.spatial | Tree-based index structures for spatial indexing. |
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants | R*-Tree and variants. |
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu | DeLiCluTree |
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn | RdKNNTree |
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar | RStarTree |
de.lmu.ifi.dbs.elki.math.linearalgebra.pca | Principal Component Analysis (PCA) and Eigenvector processing. |
de.lmu.ifi.dbs.elki.normalization | Data normalization (and reconstitution) of data sets. |
de.lmu.ifi.dbs.elki.parser | Parsers for different file formats and data types. |
de.lmu.ifi.dbs.elki.parser.meta | MetaParsers for different file formats and data types. |
de.lmu.ifi.dbs.elki.preprocessing | Preprocessors used for data preparation in a first step of various algorithms or distance and similarity measures. |
de.lmu.ifi.dbs.elki.result | Result types, representation and handling |
de.lmu.ifi.dbs.elki.visualization | Visualization package of ELKI. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki | |
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class |
KDDTask<O extends DatabaseObject>
Provides a KDDTask that can be used to perform any algorithm implementing Algorithm using any DatabaseConnection implementing
DatabaseConnection . |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm | |
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class |
AbstractAlgorithm<O extends DatabaseObject,R extends Result>
AbstractAlgorithm sets the values for flags verbose and time. |
class |
APRIORI
Provides the APRIORI algorithm for Mining Association Rules. |
class |
DependencyDerivator<V extends RealVector<V,?>,D extends Distance<D>>
Dependency derivator computes quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA. |
class |
DistanceBasedAlgorithm<O extends DatabaseObject,D extends Distance<D>,R extends Result>
Provides an abstract algorithm already setting the distance function. |
class |
DummyAlgorithm<V extends NumberVector<V,?>>
Dummy Algorithm, which just iterates over all points once, doing a 10NN query each. |
class |
KNNDistanceOrder<O extends DatabaseObject,D extends Distance<D>>
Provides an order of the kNN-distances for all objects within the database. |
class |
KNNJoin<V extends NumberVector<V,?>,D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry>
Joins in a given spatial database to each object its k-nearest neighbors. |
class |
MaterializeDistances<V extends RealVector<V,?>,D extends NumberDistance<D,N>,N extends Number>
Algorithm to materialize all the distances in a data set. |
class |
NullAlgorithm<V extends NumberVector<V,?>>
Null Algorithm, which does nothing. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering | |
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class |
ByLabelClustering<O extends DatabaseObject>
Pseudo clustering using labels. |
class |
ByLabelHierarchicalClustering<O extends DatabaseObject>
Pseudo clustering using labels. |
class |
DBSCAN<O extends DatabaseObject,D extends Distance<D>>
DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database. |
class |
DeLiClu<O extends NumberVector<O,?>,D extends Distance<D>>
DeLiClu provides the DeLiClu algorithm, a hierarchical algorithm to find density-connected sets in a database. |
class |
EM<V extends RealVector<V,?>>
Provides the EM algorithm (clustering by expectation maximization). |
class |
KMeans<D extends Distance<D>,V extends RealVector<V,?>>
Provides the k-means algorithm. |
class |
OPTICS<O extends DatabaseObject,D extends Distance<D>>
OPTICS provides the OPTICS algorithm. |
class |
ProjectedDBSCAN<V extends RealVector<V,?>>
Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor. |
class |
SLINK<O extends DatabaseObject,D extends Distance<D>>
Efficient implementation of the Single-Link Algorithm SLINK of R. |
class |
SNNClustering<O extends DatabaseObject,D extends Distance<D>>
Shared nearest neighbor clustering. |
class |
TrivialAllInOne<O extends DatabaseObject>
Trivial pseudo-clustering that just considers all points to be one big cluster. |
class |
TrivialAllNoise<O extends DatabaseObject>
Trivial pseudo-clustering that just considers all points to be noise. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation | |
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class |
CASH
Provides the CASH algorithm, an subspace clustering algorithm based on the hough transform. |
class |
COPAC<V extends RealVector<V,?>>
Provides the COPAC algorithm, an algorithm to partition a database according to the correlation dimension of its objects and to then perform an arbitrary clustering algorithm over the partitions. |
class |
ERiC<V extends RealVector<V,?>>
Performs correlation clustering on the data partitioned according to local correlation dimensionality and builds a hierarchy of correlation clusters that allows multiple inheritance from the clustering result. |
class |
FourC<O extends RealVector<O,?>>
4C identifies local subgroups of data objects sharing a uniform correlation. |
class |
ORCLUS<V extends RealVector<V,?>>
ORCLUS provides the ORCLUS algorithm, an algorithm to find clusters in high dimensional spaces. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace | |
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class |
CLIQUE<V extends RealVector<V,?>>
Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality. |
class |
DiSH<V extends RealVector<V,?>>
Algorithm for detecting subspace hierarchies. |
class |
PreDeCon<V extends RealVector<V,?>>
PreDeCon computes clusters of subspace preference weighted connected points. |
class |
PROCLUS<V extends RealVector<V,?>>
Provides the PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces. |
class |
ProjectedClustering<V extends RealVector<V,?>>
Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS . |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.outlier |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.outlier | |
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class |
ABOD<V extends RealVector<V,?>>
Angle-Based Outlier Detection Outlier detection using variance analysis on angles, especially for high dimensional data sets. |
class |
LOCI<O extends DatabaseObject,D extends NumberDistance<D,?>>
Fast Outlier Detection Using the "Local Correlation Integral". |
class |
LOF<O extends DatabaseObject,D extends NumberDistance<D,?>>
Algorithm to compute density-based local outlier factors in a database based on a specified parameter LOF.K_ID (-lof.k ). |
class |
SOD<V extends RealVector<V,Double>,D extends Distance<D>>
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Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.statistics |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.statistics | |
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class |
DistanceStatisticsWithClasses<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
Algorithm to gather statistics over the distance distribution in the data set. |
class |
EvaluateRankingQuality<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
Evaluate a distance function with respect to kNN queries. |
class |
RankingQualityHistogram<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
Evaluate a distance function with respect to kNN queries. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.application |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.application | |
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class |
AbstractApplication
AbstractApplication sets the values for flags verbose and help. |
class |
GeneratorXMLSpec
Generate a data set based on a specified model (using an XML specification) |
class |
StandAloneApplication
StandAloneApplication sets additionally to the flags set by AbstractApplication the output parameter out. |
class |
StandAloneInputApplication
StandAloneInputApplication extends StandAloneApplication and sets additionally the parameter in. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.application.cache |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.application.cache | |
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class |
CacheDoubleDistanceInOnDiskMatrix<O extends DatabaseObject,N extends NumberDistance<N,D>,D extends Number>
Wrapper to convert a traditional text-serialized result into a on-disk matrix for random access. |
class |
CacheFloatDistanceInOnDiskMatrix<O extends DatabaseObject,N extends NumberDistance<N,D>,D extends Number>
Wrapper to convert a traditional text-serialized result into a on-disk matrix for random access. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.application.visualization |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.application.visualization | |
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class |
KNNExplorer<O extends NumberVector<O,?>,N extends NumberDistance<N,D>,D extends Number>
User application to explore the k Nearest Neighbors for a given data set and distance function. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.database |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.database | |
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class |
AbstractDatabase<O extends DatabaseObject>
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 |
IndexDatabase<O extends DatabaseObject>
IndexDatabase is a database implementation which is supported by an index structure. |
class |
MetricalIndexDatabase<O extends DatabaseObject,D extends Distance<D>,N extends MetricalNode<N,E>,E extends MTreeEntry<D>>
MetricalIndexDatabase is a database implementation which is supported by a metrical index structure. |
class |
SequentialDatabase<O extends DatabaseObject>
SequentialDatabase is a simple implementation of a Database. |
class |
SpatialIndexDatabase<O extends NumberVector<O,?>,N extends SpatialNode<N,E>,E extends SpatialEntry>
SpatialIndexDatabase is a database implementation which is supported by a spatial index structure. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.database.connection |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.database.connection | |
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class |
AbstractDatabaseConnection<O extends DatabaseObject>
Abstract super class for all database connections. |
class |
FileBasedDatabaseConnection<O extends DatabaseObject>
Provides a file based database connection based on the parser to be set. |
class |
InputStreamDatabaseConnection<O extends DatabaseObject>
Provides a database connection expecting input from an input stream such as stdin. |
class |
MultipleFileBasedDatabaseConnection<O extends DatabaseObject>
Provides a database connection based on multiple files and parsers to be set. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance | |
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class |
AbstractMeasurementFunction<O extends DatabaseObject,D extends Distance<D>>
Abstract implementation of interface MeasurementFunction that provides some methods
valid for any extending class. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.distancefunction |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.distancefunction | |
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class |
AbstractDistanceFunction<O extends DatabaseObject,D extends Distance<D>>
AbstractDistanceFunction provides some methods valid for any extending class. |
class |
AbstractDoubleDistanceFunction<O extends DatabaseObject>
Provides an abstract superclass for DistanceFunctions that are based on DoubleDistance. |
class |
AbstractFloatDistanceFunction<O extends DatabaseObject>
Provides a DistanceFunction that is based on FloatDistance. |
class |
AbstractLocallyWeightedDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>>
Abstract super class for locally weighted distance functions using a preprocessor to compute the local weight matrix. |
class |
AbstractPreprocessorBasedDistanceFunction<O extends DatabaseObject,P extends Preprocessor<O>,D extends Distance<D>>
Abstract super class for distance functions needing a preprocessor. |
class |
ArcCosineDistanceFunction<V extends FeatureVector<V,?>>
Cosine distance function for feature vectors. |
class |
CosineDistanceFunction<V extends FeatureVector<V,?>>
Cosine distance function for feature vectors. |
class |
EuclideanDistanceFunction<V extends NumberVector<V,?>>
Provides the Euclidean distance for NumberVectors. |
class |
KernelBasedLocallyWeightedDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>>
Provides a kernel based locally weighted distance function. |
class |
LocallyWeightedDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>>
Provides a locally weighted distance function. |
class |
LPNormDistanceFunction<V extends FeatureVector<V,N>,N extends Number>
Provides a LP-Norm for FeatureVectors. |
class |
ManhattanDistanceFunction<V extends NumberVector<V,?>>
Manhattan distance function to compute the Manhattan distance for a pair of NumberVectors. |
class |
RepresentationSelectingDistanceFunction<O extends DatabaseObject,M extends MultiRepresentedObject<O>,D extends Distance<D>>
Distance function for multirepresented objects that selects one representation and computes the distances only within the selected representation. |
class |
WeightedDistanceFunction<V extends NumberVector<V,?>>
Provides the Weighted distance for feature vectors. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.distancefunction.adapter |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.distancefunction.adapter | |
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class |
SimilarityAdapterAbstract<V extends FeatureVector<V,?>>
Adapter from a normalized similarity function to a distance function. |
class |
SimilarityAdapterArccos<V extends FeatureVector<V,?>>
Adapter from a normalized similarity function to a distance function using arccos(sim) . |
class |
SimilarityAdapterLinear<V extends FeatureVector<V,?>>
Adapter from a normalized similarity function to a distance function using 1 - sim . |
class |
SimilarityAdapterLn<V extends FeatureVector<V,?>>
Adapter from a normalized similarity function to a distance function using -log(sim) . |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation | |
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class |
AbstractCorrelationDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>,D extends CorrelationDistance<D>>
Abstract super class for correlation based distance functions. |
class |
AbstractPreferenceVectorBasedCorrelationDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>>
Abstract super class for all preference vector based correlation distance functions. |
class |
ERiCDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>>
Provides a distance function for building the hierarchiy in the ERiC algorithm. |
class |
PCABasedCorrelationDistanceFunction<V extends RealVector<V,?>,P extends HiCOPreprocessor<V>,D extends CorrelationDistance<D>>
Provides the correlation distance for real valued vectors. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.distancefunction.external |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.distancefunction.external | |
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class |
DiskCacheBasedDoubleDistanceFunction<V extends DatabaseObject>
Provides a DistanceFunction that is based on double distances given by a distance matrix of an external file. |
class |
DiskCacheBasedFloatDistanceFunction<V extends DatabaseObject>
Provides a DistanceFunction that is based on float distances given by a distance matrix of an external file. |
class |
FileBasedDoubleDistanceFunction<V extends DatabaseObject>
Provides a DistanceFunction that is based on double distances given by a distance matrix of an external file. |
class |
FileBasedFloatDistanceFunction<V extends DatabaseObject>
Provides a DistanceFunction that is based on float distances given by a distance matrix of an external file. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace | |
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class |
AbstractDimensionsSelectingDoubleDistanceFunction<V extends NumberVector<V,?>>
Provides a distance function that computes the distance (which is a double distance) between feature vectors only in specified dimensions. |
class |
DimensionSelectingDistanceFunction<N extends Number,V extends FeatureVector<V,N>>
Provides a distance function that computes the distance between feature vectors as the absolute difference of their values in a specified dimension. |
class |
DimensionsSelectingEuclideanDistanceFunction<V extends NumberVector<V,?>>
Provides a distance function that computes the Euclidean distance between feature vectors only in specified dimensions. |
class |
DiSHDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>>
Distance function used in the DiSH algorithm. |
class |
HiSCDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>>
Distance function used in the HiSC algorithm. |
class |
SubspaceDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>>
Provides a distance function to determine a kind of correlation distance between two points, which is a pair consisting of the distance between the two subspaces spanned by the strong eigenvectors of the two points and the affine distance between the two subspaces. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries | |
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class |
AbstractEditDistanceFunction<V extends NumberVector<V,?>>
Provides the Edit Distance for NumberVectors. |
class |
DTWDistanceFunction<V extends NumberVector<V,?>>
Provides the Dynamic Time Warping distance for NumberVectors. |
class |
EDRDistanceFunction<V extends NumberVector<V,?>>
Provides the Edit Distance on Real Sequence distance for NumberVectors. |
class |
ERPDistanceFunction<V extends NumberVector<V,?>>
Provides the Edit Distance With Real Penalty distance for NumberVectors. |
class |
LCSSDistanceFunction<V extends NumberVector<V,?>>
Provides the Longest Common Subsequence distance for NumberVectors. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.similarityfunction |
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Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel | |
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class |
AbstractDoubleKernelFunction<O extends DatabaseObject>
Provides an abstract superclass for KernelFunctions that are based on DoubleDistance. |
class |
AbstractKernelFunction<O extends DatabaseObject,D extends Distance<D>>
AbstractKernelFunction provides some methods valid for any extending class. |
class |
ArbitraryKernelFunctionWrapper<O extends RealVector<O,?>>
Provides a wrapper for arbitrary kernel functions whose kernel matrix has been precomputed. |
class |
FooKernelFunction<O extends FeatureVector<?,?>>
Provides an experimental KernelDistanceFunction for RealVectors. |
class |
KernelMatrix<O extends RealVector<O,?>>
Provides a class for storing the kernel matrix and several extraction methods for convenience. |
class |
LinearKernelFunction<O extends FeatureVector<O,?>>
Provides a linear Kernel function that computes a similarity between the two feature vectors V1 and V2 defined by V1^T*V2. |
class |
PolynomialKernelFunction<O extends FeatureVector<O,?>>
Provides a polynomial Kernel function that computes a similarity between the two feature vectors V1 and V2 defined by (V1^T*V2)^degree. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.evaluation |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.evaluation | |
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class |
ComputeROCCurve<O extends DatabaseObject>
Compute a ROC curve to evaluate a ranking algorithm and compute the corresponding ROCAUC value. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree | |
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class |
TreeIndex<O extends DatabaseObject,N extends Node<N,E>,E extends Entry>
Abstract super class for all tree based index classes. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical | |
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MetricalIndex<O extends DatabaseObject,D extends Distance<D>,N extends MetricalNode<N,E>,E extends MetricalEntry>
Abstract super class for all metrical index classes. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants | |
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class |
AbstractMTree<O extends DatabaseObject,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>>
Abstract super class for all M-Tree variants. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees | |
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class |
AbstractMkTree<O extends DatabaseObject,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>>
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 specified parameter AbstractMkTree.K_MAX_PARAM . |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp | |
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class |
MkAppTree<O extends DatabaseObject,D extends NumberDistance<D,N>,N extends Number>
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. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop | |
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class |
MkCoPTree<O extends DatabaseObject,D extends NumberDistance<D,N>,N extends Number>
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. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax | |
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class |
MkMaxTree<O extends DatabaseObject,D extends Distance<D>>
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. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab | |
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class |
MkTabTree<O extends DatabaseObject,D extends Distance<D>>
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. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree | |
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class |
MTree<O extends DatabaseObject,D extends Distance<D>>
MTree is a metrical index structure based on the concepts of the M-Tree. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial | |
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SpatialIndex<O extends NumberVector<O,?>,N extends SpatialNode<N,E>,E extends SpatialEntry>
Abstract super class for all spatial index classes. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants | |
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AbstractRStarTree<O extends NumberVector<O,?>,N extends AbstractRStarTreeNode<N,E>,E extends SpatialEntry>
Abstract superclass for index structures based on a R*-Tree. |
class |
NonFlatRStarTree<O extends NumberVector<O,?>,N extends AbstractRStarTreeNode<N,E>,E extends SpatialEntry>
Abstract superclass for all non-flat R*-Tree variants. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu | |
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class |
DeLiCluTree<O extends NumberVector<O,?>>
DeLiCluTree is a spatial index structure based on an R-TRee. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn | |
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RdKNNTree<O extends NumberVector<O,?>,D extends NumberDistance<D,N>,N extends Number>
RDkNNTree is a spatial index structure based on the concepts of the R*-Tree supporting efficient processing of reverse k nearest neighbor queries. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar | |
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RStarTree<O extends NumberVector<O,?>>
RStarTree is a spatial index structure based on the concepts of the R*-Tree. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.math.linearalgebra.pca |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.math.linearalgebra.pca | |
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CompositeEigenPairFilter
The CompositeEigenPairFilter can be used to build a chain of
eigenpair filters. |
class |
CovarianceMatrixBuilder<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
Abstract class with the task of computing a Covariance matrix to be used in PCA. |
class |
FirstNEigenPairFilter
The FirstNEigenPairFilter marks the n highest eigenpairs as strong eigenpairs, where n is a user specified number. |
class |
KernelCovarianceMatrixBuilder<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
Kernel Covariance Matrix Builder. |
class |
LimitEigenPairFilter
The LimitEigenPairFilter marks all eigenpairs having an (absolute) eigenvalue below the specified threshold (relative or absolute) as weak eigenpairs, the others are marked as strong eigenpairs. |
class |
NormalizingEigenPairFilter
The NormalizingEigenPairFilter normalizes all eigenvectors s.t. |
class |
PCAFilteredRunner<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
PCA runner that will do dimensionality reduction. |
class |
PCARunner<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
Class to run PCA on given data. |
class |
PercentageEigenPairFilter
The PercentageEigenPairFilter sorts the eigenpairs in descending order of their eigenvalues and marks the first eigenpairs, whose sum of eigenvalues is higher than the given percentage of the sum of all eigenvalues as strong eigenpairs. |
class |
ProgressiveEigenPairFilter
The ProgressiveEigenPairFilter sorts the eigenpairs in descending order of their eigenvalues and marks the first eigenpairs, whose sum of eigenvalues is higher than the given percentage of the sum of all eigenvalues as strong eigenpairs. |
class |
RelativeEigenPairFilter
The RelativeEigenPairFilter sorts the eigenpairs in descending order of their eigenvalues and marks the first eigenpairs who are a certain factor above the average of the remaining eigenvalues. |
class |
SignificantEigenPairFilter
The SignificantEigenPairFilter sorts the eigenpairs in descending order of their eigenvalues and chooses the contrast of an Eigenvalue to the remaining Eigenvalues is maximal. |
class |
StandardCovarianceMatrixBuilder<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
Class for building a "traditional" covariance matrix. |
class |
WeakEigenPairFilter
The WeakEigenPairFilter sorts the eigenpairs in descending order of their eigenvalues and returns the first eigenpairs who are above the average mark as "strong", the others as "weak". |
class |
WeightedCovarianceMatrixBuilder<V extends RealVector<V,?>,D extends NumberDistance<D,?>>
CovarianceMatrixBuilder with weights. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.normalization |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.normalization | |
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class |
AbstractNormalization<O extends DatabaseObject>
Abstract super class for all normalizations. |
class |
AttributeWiseMinMaxNormalization<V extends RealVector<V,?>>
Class to perform and undo a normalization on real vectors with respect to given minimum and maximum in each dimension. |
class |
AttributeWiseVarianceNormalization<V extends RealVector<V,?>>
Class to perform and undo a normalization on real vectors with respect to given mean and standard deviation in each dimension. |
class |
DummyNormalization<O extends DatabaseObject>
Dummy normalization that does nothing. |
class |
MultiRepresentedObjectNormalization<O extends DatabaseObject>
Class to perform and undo a normalization on multi-represented objects with respect to given normalizations for each representation. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.parser |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.parser | |
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class |
AbstractParser<O extends DatabaseObject>
Abstract superclass for all parsers providing the option handler for handling options. |
class |
BitVectorLabelParser
Provides a parser for parsing one BitVector per line, bits separated by whitespace. |
class |
DoubleVectorLabelParser
Provides a parser for parsing one point per line, attributes separated by whitespace. |
class |
DoubleVectorLabelTransposingParser
Parser reads points transposed. |
class |
FloatVectorLabelParser
Provides a parser for parsing one point per line, attributes separated by whitespace. |
class |
NumberDistanceParser<D extends NumberDistance<D,N>,N extends Number>
Provides a parser for parsing one distance value per line. |
class |
ParameterizationFunctionLabelParser
Provides a parser for parsing one point per line, attributes separated by whitespace. |
class |
RealVectorLabelParser<V extends RealVector<?,?>>
Provides a parser for parsing one point per line, attributes separated by whitespace. |
class |
SparseBitVectorLabelParser
Provides a parser for parsing one sparse BitVector per line, where the indices of the one-bits are separated by whitespace. |
class |
SparseFloatVectorLabelParser
Provides a parser for parsing one point per line, attributes separated by whitespace. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.parser.meta |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.parser.meta | |
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class |
DoubleVectorProjectionParser
Parser to project the ParsingResult obtained by a suitable base parser onto a selected subset of attributes. |
class |
DoubleVectorRandomProjectionParser
Parser to project the ParsingResult obtained by a suitable base parser onto a randomly selected subset of attributes. |
class |
MetaParser<O extends DatabaseObject>
A MetaParser uses any Parser as specified by the user via parameter setting
as base parser and may perform certain transformations on the retrieved ParsingResult . |
class |
ProjectionParser<V extends RealVector<V,?>>
A ProjectionParser projects the ParsingResult of its base parser
onto a subspace specified by a BitSet. |
class |
RandomProjectionParser<V extends RealVector<V,?>>
A RandomProjectionParser selects a subset of attributes randomly for projection of a ParsingResult. |
class |
SparseFloatVectorProjectionParser
Parser to project the ParsingResult obtained by a suitable base parser onto a selected subset of attributes. |
class |
SparseFloatVectorRandomProjectionParser
Parser to project the ParsingResult obtained by a suitable base parser onto a randomly selected subset of attributes. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.preprocessing |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.preprocessing | |
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class |
DiSHPreprocessor<V extends RealVector<V,N>,N extends Number>
Preprocessor for DiSH preference vector assignment to objects of a certain database. |
class |
FourCPreprocessor<D extends Distance<D>,V extends RealVector<V,?>>
Preprocessor for 4C local dimensionality and locally weighted matrix assignment to objects of a certain database. |
class |
HiCOPreprocessor<V extends RealVector<V,?>>
Abstract superclass for preprocessors for HiCO correlation dimension assignment to objects of a certain database. |
class |
HiSCPreprocessor<V extends RealVector<V,?>>
Preprocessor for HiSC preference vector assignment to objects of a certain database. |
class |
KnnQueryBasedHiCOPreprocessor<V extends RealVector<V,?>>
Computes the HiCO correlation dimension of objects of a certain database. |
class |
MaterializeKNNPreprocessor<O extends DatabaseObject,D extends Distance<D>>
A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object. |
class |
PreDeConPreprocessor<D extends Distance<D>,V extends RealVector<V,?>>
Preprocessor for PreDeCon local dimensionality and locally weighted matrix assignment to objects of a certain database. |
class |
PreprocessorHandler<O extends DatabaseObject,P extends Preprocessor<O>>
Handler class for all objects (e.g. distance functions) using a preprocessor running on a certain database. |
class |
ProjectedDBSCANPreprocessor<D extends Distance<D>,V extends RealVector<V,?>>
Abstract superclass for preprocessor of algorithms extending the ProjectedDBSCAN algorithm. |
class |
RangeQueryBasedHiCOPreprocessor<V extends RealVector<V,?>>
Computes the HiCO correlation dimension of objects of a certain database. |
class |
SharedNearestNeighborsPreprocessor<O extends DatabaseObject,D extends Distance<D>>
A preprocessor for annotation of the ids of nearest neighbors to each database object. |
class |
SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector<O,?>,D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry>
A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object. |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.result |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.result | |
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class |
DiscardResultHandler<O extends DatabaseObject,R extends Result>
A dummy result handler that discards the actual result, for use in benchmarks. |
class |
ResultWriter<O extends DatabaseObject>
Result handler that feeds the data into a TextWriter |
Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.visualization |
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Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.visualization | |
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class |
ResultROCCurveVisualizer<O extends DatabaseObject>
Visualize a ROC curve. |
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