| 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 |
MaterializeDistances<O>
Algorithm to materialize all the distances in a data set.
|
class |
NullAlgorithm
Null Algorithm, which does nothing.
|
| 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.
|
| 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 |
SNNClustering<O>
Shared nearest neighbor clustering.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AffinityPropagationClusteringAlgorithm<O>
Cluster analysis by affinity propagation.
|
| 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.
|
| 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).
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractHDBSCAN<O,R extends Result>
Abstract base class for HDBSCAN variations.
|
class |
HDBSCANLinearMemory<O>
Linear memory implementation of HDBSCAN clustering.
|
class |
SLINK<O>
Implementation of the efficient Single-Link Algorithm SLINK of R.
|
class |
SLINKHDBSCANLinearMemory<O>
Linear memory implementation of HDBSCAN clustering based on SLINK.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KMeansLloyd<V extends NumberVector>
The standard k-means algorithm, using Lloyd-style bulk iterations.
|
class |
KMeansMacQueen<V extends NumberVector>
The original k-means algorithm, using MacQueen style incremental updates;
making this effectively an "online" (streaming) algorithm.
|
class |
KMediansLloyd<V extends NumberVector>
k-medians clustering algorithm, but using Lloyd-style bulk iterations instead
of the more complicated approach suggested by Kaufman and Rousseeuw (see
KMedoidsPAM instead). |
class |
KMedoidsPAM<V>
The original PAM algorithm or k-medoids clustering, as proposed by Kaufman
and Rousseeuw in "Partitioning Around Medoids".
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractOPTICS<O>
The OPTICS algorithm for density-based hierarchical clustering.
|
class |
DeLiClu<NV extends NumberVector>
DeliClu: Density-Based Hierarchical Clustering, a hierarchical algorithm to
find density-connected sets in a database.
|
class |
OPTICSHeap<O>
The OPTICS algorithm for density-based hierarchical clustering.
|
class |
OPTICSList<O>
The OPTICS algorithm for density-based hierarchical clustering.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CLIQUE<V extends NumberVector>
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>
The DOC algorithm, and it's heuristic variant, FastDOC.
|
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.
|
| 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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FDBSCAN
FDBSCAN is an adaption of DBSCAN for fuzzy (uncertain) objects.
|
class |
FDBSCANNeighborPredicate
Density-based Clustering of Applications with Noise and Fuzzy objects
(FDBSCAN) is an Algorithm to find sets in a fuzzy database that are
density-connected with minimum probability.
|
| Modifier and Type | Class and Description |
|---|---|
class |
APRIORI
The APRIORI algorithm for Mining Association Rules.
|
| Modifier and Type | Class and Description |
|---|---|
class |
COP<V extends NumberVector>
Correlation outlier probability: Outlier Detection in Arbitrarily Oriented
Subspaces
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
Outlier Detection in Arbitrarily Oriented Subspaces in: Proc. |
class |
DWOF<O>
Algorithm to compute dynamic-window outlier factors in a database based on a
specified parameter
DWOF.Parameterizer.K_ID (-dwof.k). |
class |
GaussianModel<V extends NumberVector>
Outlier have smallest GMOD_PROB: the outlier scores is the
probability density of the assumed 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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ABOD<V extends NumberVector>
Angle-Based Outlier Detection / Angle-Based Outlier Factor.
|
class |
FastABOD<V extends NumberVector>
Angle-Based Outlier Detection / Angle-Based Outlier Factor.
|
class |
LBABOD<V extends NumberVector>
Angle-Based Outlier Detection / Angle-Based Outlier Factor.
|
| Modifier and Type | Class and Description |
|---|---|
class |
EMOutlier<V extends NumberVector>
outlier detection algorithm using EM Clustering.
|
| 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 |
KNNOutlier<O>
Outlier Detection based on the distance of an object to its k nearest
neighbor.
|
class |
KNNWeightOutlier<O>
Outlier Detection based on the accumulated distances of a point to its k
nearest neighbors.
|
class |
ReferenceBasedOutlierDetection
Reference-Based Outlier Detection algorithm, an algorithm that computes kNN
distances approximately, using reference points.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ALOCI<O extends NumberVector>
Fast Outlier Detection Using the "approximate Local Correlation Integral".
|
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 |
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.Parameterizer.K_ID (-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.
|
| 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.
|
| 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:
Sanjay Chawla and Pei Sun SLOM: a new measure for local spatial outliers in Knowledge and Information Systems 9(4), 412-429, 2006 This implementation works around some corner cases in SLOM, in particular when an object has none or a single neighbor only (albeit the results will still not be too useful then), which will result in divisions by zero. |
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.
|
| 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.
|
| 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.
|
| 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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
PerturbationFilter<V extends NumberVector>
A filter to perturb the values by adding micro-noise.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BitVectorLabelParser
Parser for parsing one BitVector per line, bits separated by whitespace.
|
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.
|
class |
TermFrequencyParser<V extends SparseNumberVector>
A parser to load term frequency data, which essentially are sparse vectors
with text keys.
|
| Modifier and Type | Class and Description |
|---|---|
class |
HistogramIntersectionDistanceFunction
Intersection distance for color histograms.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AsciiDistanceParser
Parser for parsing one distance value per line.
|
class |
DiskCacheBasedDoubleDistanceFunction
Distance function that is based on double distances given by a distance
matrix of an external binary matrix file.
|
class |
DiskCacheBasedFloatDistanceFunction
Distance function that is based on float distances given by a distance matrix
of an external binary matrix file.
|
class |
FileBasedDoubleDistanceFunction
Distance function that is based on double distances given by a distance
matrix of an external ASCII file.
|
class |
FileBasedFloatDistanceFunction
Distance function that is based on float distances given by a distance matrix
of an external ASCII file.
|
| 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.
|
| 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.
|
| 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 | Class and Description |
|---|---|
class |
HiSCPreferenceVectorIndex<V extends NumberVector>
Preprocessor for HiSC preference vector assignment to objects of a certain
database.
|
| 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 |
MTree<O>
MTree is a metrical index structure based on the concepts of the M-Tree.
|
| Modifier and Type | Class and Description |
|---|---|
class |
RStarTree
RStarTree is a spatial index structure based on the concepts of the R*-Tree.
|
| Modifier and Type | Class and Description |
|---|---|
class |
VAFile<V extends NumberVector>
Vector-approximation file (VAFile)
Reference:
Weber, R. and Blott, S.
|
| 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 |
NormalizingEigenPairFilter
The NormalizingEigenPairFilter normalizes all eigenvectors s.t.
|
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".
|
class |
WeightedCovarianceMatrixBuilder
CovarianceMatrixBuilder with weights. |
| Modifier and Type | Class and Description |
|---|---|
class |
COPVectorVisualization
Visualize error vectors as produced by COP.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.