Uses of Classde.lmu.ifi.dbs.elki.utilities.documentation.Title

• Packages that use Title
Package Description
de.lmu.ifi.dbs.elki.algorithm
Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.classification
Classification algorithms.
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.affinitypropagation
Affinity Propagation (AP) clustering.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation
Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.em
Expectation-Maximization clustering algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan
Generalized DBSCAN Generalized DBSCAN is an abstraction of the original DBSCAN idea, that allows the use of arbitrary "neighborhood" and "core point" predicates.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical
Hierarchical agglomerative clustering (HAC).
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
K-means clustering and variations
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.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.clustering.uncertain
Clustering algorithms for uncertain data.
de.lmu.ifi.dbs.elki.algorithm.itemsetmining
Algorithms for frequent itemset mining such as APRIORI.
de.lmu.ifi.dbs.elki.algorithm.outlier
Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased
Angle-based outlier detection algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering
Clustering based outlier detection.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance
Distance-based outlier detection algorithms, such as DBOutlier and kNN.
de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic
Outlier detection algorithms based on intrinsic dimensionality.
de.lmu.ifi.dbs.elki.algorithm.outlier.lof
LOF family of 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.subspace
Subspace outlier detection methods Methods that detect outliers in subspaces (projections) of the data set.
de.lmu.ifi.dbs.elki.algorithm.projection
de.lmu.ifi.dbs.elki.algorithm.statistics
Statistical analysis algorithms.
de.lmu.ifi.dbs.elki.algorithm.timeseries
Algorithms for change point detection in time series.
de.lmu.ifi.dbs.elki.datasource
Data normalization (and reconstitution) of data sets
de.lmu.ifi.dbs.elki.datasource.filter.transform
Data space transformations
de.lmu.ifi.dbs.elki.datasource.parser
Parsers for different file formats and data types The general use-case for any parser is to create objects out of an InputStream (e.g. by reading a data file).
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries
Distance functions designed for time series Note that some regular distance functions (e.g., Euclidean) are also used on time series.
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.metrical.mtreevariants.mtree
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.linearalgebra
The linear algebra package provides classes and computational methods for operations on matrices and vectors.
de.lmu.ifi.dbs.elki.math.linearalgebra.pca
Principal Component Analysis (PCA) and Eigenvector processing
de.lmu.ifi.dbs.elki.math.linearalgebra.pca.filter
Filter eigenvectors based on their eigenvalues.
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier
Visualizers for outlier scores based on 2D projections
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm

Classes in de.lmu.ifi.dbs.elki.algorithm with annotations of type Title
Modifier and Type Class and Description
class  DependencyDerivator<V extends NumberVector>
Dependency derivator computes quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA.
class  DummyAlgorithm<O extends NumberVector>
Dummy algorithm, which just iterates over all points once, doing a 10NN query each.
class  KNNDistancesSampler<O>
Provides an order of the kNN-distances for all objects within the database.
class  KNNJoin<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry>
Joins in a given spatial database to each object its k-nearest neighbors.
class  NullAlgorithm
Null Algorithm, which does nothing.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.classification

Classes in de.lmu.ifi.dbs.elki.algorithm.classification with annotations of type Title
Modifier and Type Class and Description
class  KNNClassifier<O>
KNNClassifier classifies instances based on the class distribution among the k nearest neighbors in a database.
class  PriorProbabilityClassifier
Classifier to classify instances based on the prior probability of classes in the database, without using the actual data values.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.clustering

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering with annotations of type Title
Modifier and Type Class and Description
class  DBSCAN<O>
Density-Based Clustering of Applications with Noise (DBSCAN), an algorithm to find density-connected sets in a database.
class  GriDBSCAN<V extends NumberVector>
Using Grid for Accelerating Density-Based Clustering.
class  SNNClustering<O>
Shared nearest neighbor clustering.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation with annotations of type Title
Modifier and Type Class and Description
class  AffinityPropagationClusteringAlgorithm<O>
Cluster analysis by affinity propagation.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with annotations of type Title
Modifier and Type Class and Description
class  CASH<V extends NumberVector>
The CASH algorithm is a subspace clustering algorithm based on the Hough transform.
class  COPAC<V extends NumberVector>
COPAC is 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 NumberVector>
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<V extends NumberVector>
4C identifies local subgroups of data objects sharing a uniform correlation.
class  HiCO<V extends NumberVector>
Implementation of the HiCO algorithm, an algorithm for detecting hierarchies of correlation clusters.
class  ORCLUS<V extends NumberVector>
ORCLUS: Arbitrarily ORiented projected CLUSter generation.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.clustering.em

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.em with annotations of type Title
Modifier and Type Class and Description
class  EM<V extends NumberVector,M extends MeanModel>
Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan with annotations of type Title
Modifier and Type Class and Description
class  LSDBC<O extends NumberVector>
Locally Scaled Density Based Clustering.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical with annotations of type Title
Modifier and Type Class and Description
class  HDBSCANLinearMemory<O>
Linear memory implementation of HDBSCAN clustering.
class  SLINK<O>
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with annotations of type Title
Modifier and Type Class and Description
class  KMeansCompare<V extends NumberVector>
Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.
class  KMeansLloyd<V extends NumberVector>
The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).
class  KMeansMacQueen<V extends NumberVector>
The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
class  KMeansMinusMinus<V extends NumberVector>
k-means--: A Unified Approach to Clustering and Outlier Detection.
class  KMeansSort<V extends NumberVector>
Sort-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means (with sorting).
class  KMedoidsPAM<V>
The original Partitioning Around Medoids (PAM) algorithm or k-medoids clustering, as proposed by Kaufman and Rousseeuw in "Clustering by means of Medoids".
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.clustering.optics

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.optics with annotations of type Title
Modifier and Type Class and Description
class  DeLiClu<V extends NumberVector>
DeliClu: Density-Based Hierarchical Clustering A hierarchical algorithm to find density-connected sets in a database, closely related to OPTICS but exploiting the structure of a R-tree for acceleration.
class  OPTICSHeap<O>
The OPTICS algorithm for density-based hierarchical clustering.
class  OPTICSList<O>
The OPTICS algorithm for density-based hierarchical clustering.
class  OPTICSXi
Extract clusters from OPTICS Plots using the original Xi extraction.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with annotations of type Title
Modifier and Type Class and Description
class  CLIQUE
Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.
class  DiSH<V extends NumberVector>
Algorithm for detecting subspace hierarchies.
class  DOC<V extends NumberVector>
DOC is a sampling based subspace clustering algorithm.
class  FastDOC<V extends NumberVector>
The heuristic variant of the DOC algorithm, FastDOC Reference: C.
class  HiSC<V extends NumberVector>
Implementation of the HiSC algorithm, an algorithm for detecting hierarchies of subspace clusters.
class  P3C<V extends NumberVector>
P3C: A Robust Projected Clustering Algorithm.
class  PreDeCon<V extends NumberVector>
PreDeCon computes clusters of subspace preference weighted connected points.
class  PROCLUS<V extends NumberVector>
The PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces.
class  SUBCLU<V extends NumberVector>
Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial with annotations of type Title
Modifier and Type Class and Description
class  ByLabelClustering
Pseudo clustering using labels.
class  ByLabelHierarchicalClustering
Pseudo clustering using labels.
class  ByModelClustering
Pseudo clustering using annotated models.
class  TrivialAllInOne
Trivial pseudo-clustering that just considers all points to be one big cluster.
class  TrivialAllNoise
Trivial pseudo-clustering that just considers all points to be noise.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain with annotations of type Title
Modifier and Type Class and Description
class  FDBSCAN
FDBSCAN is an adaption of DBSCAN for fuzzy (uncertain) objects.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.itemsetmining

Classes in de.lmu.ifi.dbs.elki.algorithm.itemsetmining with annotations of type Title
Modifier and Type Class and Description
class  APRIORI
The APRIORI algorithm for Mining Association Rules.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.outlier

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier with annotations of type Title
Modifier and Type Class and Description
class  COP<V extends NumberVector>
Correlation outlier probability: Outlier Detection in Arbitrarily Oriented Subspaces Reference: Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
Outlier Detection in Arbitrarily Oriented Subspaces
Proc.
class  DWOF<O>
Algorithm to compute dynamic-window outlier factors in a database based on a specified parameter k, which specifies the number of the neighbors to be considered during the calculation of the DWOF score.
class  GaussianModel<V extends NumberVector>
Outlier detection based on the probability density of the single normal distribution.
class  GaussianUniformMixture<V extends NumberVector>
Outlier detection algorithm using a mixture model approach.
class  OPTICSOF<O>
OPTICS-OF outlier detection algorithm, an algorithm to find Local Outliers in a database based on ideas from OPTICSTypeAlgorithm clustering.
class  SimpleCOP<V extends NumberVector>
Algorithm to compute local correlation outlier probability.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased with annotations of type Title
Modifier and Type Class and Description
class  ABOD<V extends NumberVector>
Angle-Based Outlier Detection / Angle-Based Outlier Factor.
class  FastABOD<V extends NumberVector>
Fast-ABOD (approximateABOF) version of Angle-Based Outlier Detection / Angle-Based Outlier Factor.
class  LBABOD<V extends NumberVector>
LB-ABOD (lower-bound) version of Angle-Based Outlier Detection / Angle-Based Outlier Factor.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering with annotations of type Title
Modifier and Type Class and Description
class  CBLOF<O extends NumberVector>
Cluster-based local outlier factor (CBLOF).
class  EMOutlier<V extends NumberVector>
Outlier detection algorithm using EM Clustering.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.outlier.distance

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.distance with annotations of type Title
Modifier and Type Class and Description
class  DBOutlierDetection<O>
Simple distanced based outlier detection algorithm.
class  DBOutlierScore<O>
Compute percentage of neighbors in the given neighborhood with size d.
class  HilOut<O extends NumberVector>
Fast Outlier Detection in High Dimensional Spaces Outlier Detection using Hilbert space filling curves Reference: F.
class  KNNDD<O>
Nearest Neighbor Data Description.
class  KNNOutlier<O>
Outlier Detection based on the distance of an object to its k nearest neighbor.
class  KNNSOS<O>
kNN-based adaption of Stochastic Outlier Selection.
class  KNNWeightOutlier<O>
Outlier Detection based on the accumulated distances of a point to its k nearest neighbors.
class  ODIN<O>
Outlier detection based on the in-degree of the kNN graph.
class  ReferenceBasedOutlierDetection
Reference-Based Outlier Detection algorithm, an algorithm that computes kNN distances approximately, using reference points.
class  SOS<O>
Stochastic Outlier Selection.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic with annotations of type Title
Modifier and Type Class and Description
class  IDOS<O>
Intrinsic Dimensional Outlier Detection in High-Dimensional Data.
class  ISOS<O>
Intrinsic Stochastic Outlier Selection.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.outlier.lof

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.lof with annotations of type Title
Modifier and Type Class and Description
class  ALOCI<O extends NumberVector>
Fast Outlier Detection Using the "approximate Local Correlation Integral".
class  COF<O>
Connectivity-based Outlier Factor (COF).
class  FlexibleLOF<O>
Flexible variant of the "Local Outlier Factor" algorithm.
class  INFLO<O>
Influence Outliers using Symmetric Relationship (INFLO) using two-way search, is an outlier detection method based on LOF; but also using the reverse kNN.
class  KDEOS<O>
Generalized Outlier Detection with Flexible Kernel Density Estimates.
class  LDF<O extends NumberVector>
Outlier Detection with Kernel Density Functions.
class  LDOF<O>
Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a Database.
class  LOCI<O>
Fast Outlier Detection Using the "Local Correlation Integral".
class  LOF<O>
Algorithm to compute density-based local outlier factors in a database based on a specified parameter -lof.k.
class  LoOP<O>
LoOP: Local Outlier Probabilities Distance/density based algorithm similar to LOF to detect outliers, but with statistical methods to achieve better result stability.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.outlier.meta

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.meta with annotations of type Title
Modifier and Type Class and Description
class  FeatureBagging
A simple ensemble method called "Feature bagging" for outlier detection.
class  HiCS<V extends NumberVector>
Algorithm to compute High Contrast Subspaces for Density-Based Outlier Ranking.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial with annotations of type Title
Modifier and Type Class and Description
class  CTLuGLSBackwardSearchAlgorithm<V extends NumberVector>
GLS-Backward Search is a statistical approach to detecting spatial outliers.
class  CTLuMedianAlgorithm<N>
Median Algorithm of C.
class  CTLuMoranScatterplotOutlier<N>
Moran scatterplot outliers, based on the standardized deviation from the local and global means.
class  CTLuRandomWalkEC<P>
Spatial outlier detection based on random walks.
class  CTLuScatterplotOutlier<N>
Scatterplot-outlier is a spatial outlier detection method that performs a linear regression of object attributes and their neighbors average value.
class  CTLuZTestOutlier<N>
Detect outliers by comparing their attribute value to the mean and standard deviation of their neighborhood.
class  SLOM<N,O>
SLOM: a new measure for local spatial outliers Reference: S.
class  SOF<N,O>
The Spatial Outlier Factor (SOF) is a spatial LOF variation.
class  TrimmedMeanApproach<N>
A Trimmed Mean Approach to Finding Spatial Outliers.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace with annotations of type Title
Modifier and Type Class and Description
class  AggarwalYuEvolutionary<V extends NumberVector>
Evolutionary variant (EAFOD) of the high-dimensional outlier detection algorithm by Aggarwal and Yu.
class  AggarwalYuNaive<V extends NumberVector>
BruteForce variant of the high-dimensional outlier detection algorithm by Aggarwal and Yu.
class  OutRankS1
OutRank: ranking outliers in high dimensional data.
class  SOD<V extends NumberVector>
Subspace Outlier Degree.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.projection

Classes in de.lmu.ifi.dbs.elki.algorithm.projection with annotations of type Title
Modifier and Type Class and Description
class  IntrinsicNearestNeighborAffinityMatrixBuilder<O>
Build sparse affinity matrix using the nearest neighbors only, adjusting for intrinsic dimensionality.
class  TSNE<O>
t-Stochastic Neighbor Embedding is a projection technique designed for visualization that tries to preserve the nearest neighbor structure.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.statistics

Classes in de.lmu.ifi.dbs.elki.algorithm.statistics with annotations of type Title
Modifier and Type Class and Description
class  DistanceStatisticsWithClasses<O>
Algorithm to gather statistics over the distance distribution in the data set.
class  EvaluateRankingQuality<V extends NumberVector>
Evaluate a distance function with respect to kNN queries.
class  RankingQualityHistogram<O>
Evaluate a distance function with respect to kNN queries.
• Uses of Title in de.lmu.ifi.dbs.elki.algorithm.timeseries

Classes in de.lmu.ifi.dbs.elki.algorithm.timeseries with annotations of type Title
Modifier and Type Class and Description
class  OfflineChangePointDetectionAlgorithm
Off-line change point detection algorithm detecting a change in mean, based on the cumulative sum (CUSUM), same-variance assumption, and using bootstrap sampling for significance estimation.
class  SigniTrendChangeDetection
Signi-Trend detection algorithm applies to a single time-series.
• Uses of Title in de.lmu.ifi.dbs.elki.datasource

Classes in de.lmu.ifi.dbs.elki.datasource with annotations of type Title
Modifier and Type Class and Description
class  EmptyDatabaseConnection
Pseudo database that is empty.
class  InputStreamDatabaseConnection
Database connection expecting input from an input stream such as stdin.
• Uses of Title in de.lmu.ifi.dbs.elki.datasource.filter.transform

Classes in de.lmu.ifi.dbs.elki.datasource.filter.transform with annotations of type Title
Modifier and Type Class and Description
class  PerturbationFilter<V extends NumberVector>
A filter to perturb the values by adding micro-noise.
• Uses of Title in de.lmu.ifi.dbs.elki.datasource.parser

Classes in de.lmu.ifi.dbs.elki.datasource.parser with annotations of type Title
Modifier and Type Class and Description
class  ArffParser
Parser to load WEKA .arff files into ELKI.
class  BitVectorLabelParser
Parser for parsing one BitVector per line, bits separated by whitespace.
class  LibSVMFormatParser<V extends SparseNumberVector>
Parser to read libSVM format files.
class  SparseNumberVectorLabelParser<V extends SparseNumberVector>
Parser for parsing one point per line, attributes separated by whitespace.
class  StringParser
Parser that loads a text file for use with string similarity measures.
• Uses of Title in de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries with annotations of type Title
Modifier and Type Class and Description
class  DerivativeDTWDistanceFunction
Derivative Dynamic Time Warping distance for numerical vectors.
class  DTWDistanceFunction
Dynamic Time Warping distance (DTW) for numerical vectors.
class  EDRDistanceFunction
Edit Distance on Real Sequence distance for numerical vectors.
class  ERPDistanceFunction
Edit Distance With Real Penalty distance for numerical vectors.
class  LCSSDistanceFunction
Longest Common Subsequence distance for numerical vectors.
• Uses of Title in de.lmu.ifi.dbs.elki.index.preprocessed.knn

Classes in de.lmu.ifi.dbs.elki.index.preprocessed.knn with annotations of type Title
Modifier and Type Class and Description
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  PartitionApproximationMaterializeKNNPreprocessor<O>
A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
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.
• Uses of Title in de.lmu.ifi.dbs.elki.index.preprocessed.localpca

Classes in de.lmu.ifi.dbs.elki.index.preprocessed.localpca with annotations of type Title
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.
• Uses of Title in de.lmu.ifi.dbs.elki.index.preprocessed.preference

Classes in de.lmu.ifi.dbs.elki.index.preprocessed.preference with annotations of type Title
Modifier and Type Class and Description
class  HiSCPreferenceVectorIndex<V extends NumberVector>
Preprocessor for HiSC preference vector assignment to objects of a certain database.
• Uses of Title in de.lmu.ifi.dbs.elki.index.preprocessed.snn

Classes in de.lmu.ifi.dbs.elki.index.preprocessed.snn with annotations of type Title
Modifier and Type Class and Description
class  SharedNearestNeighborPreprocessor<O>
A preprocessor for annotation of the ids of nearest neighbors to each database object.
• Uses of Title in de.lmu.ifi.dbs.elki.index.projected

Classes in de.lmu.ifi.dbs.elki.index.projected with annotations of type Title
Modifier and Type Class and Description
class  PINN<O extends NumberVector>
Projection-Indexed nearest-neighbors (PINN) is an index to retrieve the nearest neighbors in high dimensional spaces by using a random projection based index.
• Uses of Title in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree with annotations of type Title
Modifier and Type Class and Description
class  MTree<O>
MTree is a metrical index structure based on the concepts of the M-Tree.
• Uses of Title in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar with annotations of type Title
Modifier and Type Class and Description
class  RStarTree
RStarTree is a spatial index structure based on the concepts of the R*-Tree.
• Uses of Title in de.lmu.ifi.dbs.elki.index.vafile

Classes in de.lmu.ifi.dbs.elki.index.vafile with annotations of type Title
Modifier and Type Class and Description
class  VAFile<V extends NumberVector>
Vector-approximation file (VAFile) Reference: R.
• Uses of Title in de.lmu.ifi.dbs.elki.math.linearalgebra

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra with annotations of type Title
Modifier and Type Class and Description
class  VMath
Class providing basic vector mathematics, for low-level vectors stored as double[].
• Uses of Title in de.lmu.ifi.dbs.elki.math.linearalgebra.pca

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with annotations of type Title
Modifier and Type Class and Description
class  WeightedCovarianceMatrixBuilder
• Uses of Title in de.lmu.ifi.dbs.elki.math.linearalgebra.pca.filter

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.pca.filter with annotations of type Title
Modifier and Type Class and Description
class  DropEigenPairFilter
The "drop" filter looks for the largest drop in normalized relative eigenvalues.
class  FirstNEigenPairFilter
The FirstNEigenPairFilter marks the n highest eigenpairs as strong eigenpairs, where n is a user specified number.
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  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  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".
• Uses of Title in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier

Classes in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier with annotations of type Title
Modifier and Type Class and Description
class  COPVectorVisualization
Visualize error vectors as produced by COP.