| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.algorithm |
Algorithms suitable as a task for the
KDDTask main routine. |
| de.lmu.ifi.dbs.elki.algorithm.clustering |
Clustering algorithms
Clustering algorithms are supposed to implement the
Algorithm-Interface. |
| de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
Correlation clustering algorithms
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.subspace |
Axis-parallel subspace clustering algorithms
The clustering algorithms in this package are instances of both, projected clustering algorithms or
subspace clustering algorithms according to the classical but somewhat obsolete classification schema
of clustering algorithms for axis-parallel subspaces.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier |
Outlier detection algorithms
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.meta |
Meta outlier detection algorithms: external scores, score rescaling.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.spatial |
Spatial outlier detection algorithms
|
| de.lmu.ifi.dbs.elki.application.internal |
Internal utilities for development.
|
| de.lmu.ifi.dbs.elki.application.visualization |
Visualization applications in ELKI.
|
| de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram |
Distance functions using correlations.
|
| de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries |
Distance functions designed for time series.
|
| 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.tree.spatial.rstarvariants.util |
Utilities for R*-Tree and variants.
|
| de.lmu.ifi.dbs.elki.math |
Mathematical operations and utilities used throughout the framework.
|
| de.lmu.ifi.dbs.elki.math.linearalgebra.pca |
Principal Component Analysis (PCA) and Eigenvector processing.
|
| de.lmu.ifi.dbs.elki.result |
Result types, representation and handling
|
| de.lmu.ifi.dbs.elki.utilities.documentation |
Documentation utilities: Annotations for Title, Description, Reference
|
| de.lmu.ifi.dbs.elki.utilities.scaling.outlier |
Scaling of Outlier scores, that require a statistical analysis of the occurring values
|
| de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d |
Visualizers based on 2D projections.
|
| Modifier and Type | Class and Description |
|---|---|
class |
APRIORI
Provides the APRIORI algorithm for Mining Association Rules.
|
class |
DependencyDerivator<V extends NumberVector<V,?>,D extends Distance<D>>
Dependency derivator computes quantitatively linear dependencies among
attributes of a given dataset based on a linear correlation PCA.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DBSCAN<O,D extends Distance<D>>
DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected
sets in a database.
|
class |
DeLiClu<NV extends NumberVector<NV,?>,D extends Distance<D>>
DeLiClu provides the DeLiClu algorithm, a hierarchical algorithm to find
density-connected sets in a database.
|
class |
EM<V extends NumberVector<V,?>>
Provides the EM algorithm (clustering by expectation maximization).
|
class |
KMeans<V extends NumberVector<V,?>,D extends Distance<D>>
Provides the k-means algorithm.
|
class |
OPTICS<O,D extends Distance<D>>
OPTICS provides the OPTICS algorithm.
|
class |
SLINK<O,D extends Distance<D>>
Efficient implementation of the Single-Link Algorithm SLINK of R.
|
class |
SNNClustering<O>
Shared nearest neighbor clustering.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CASH
Provides the CASH algorithm, an subspace clustering algorithm based on the
hough transform.
|
class |
COPAC<V extends NumberVector<V,?>,D extends Distance<D>>
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 NumberVector<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<V extends NumberVector<V,?>>
4C identifies local subgroups of data objects sharing a uniform correlation.
|
class |
HiCO<V extends NumberVector<V,?>>
Implementation of the HiCO algorithm, an algorithm for detecting hierarchies
of correlation clusters.
|
class |
ORCLUS<V extends NumberVector<V,?>>
ORCLUS provides the ORCLUS algorithm, an algorithm to find clusters in high
dimensional spaces.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CLIQUE<V extends NumberVector<V,?>>
Implementation of the CLIQUE algorithm, a grid-based algorithm to identify
dense clusters in subspaces of maximum dimensionality.
|
class |
DiSH<V extends NumberVector<V,?>>
Algorithm for detecting subspace hierarchies.
|
class |
HiSC<V extends NumberVector<V,?>>
Implementation of the HiSC algorithm, an algorithm for detecting hierarchies
of subspace clusters.
|
class |
PreDeCon<V extends NumberVector<V,?>>
PreDeCon computes clusters of subspace preference weighted connected points.
|
class |
PROCLUS<V extends NumberVector<V,?>>
Provides the PROCLUS algorithm, an algorithm to find subspace clusters in
high dimensional spaces.
|
class |
SUBCLU<V extends NumberVector<V,?>>
Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily
shaped and positioned clusters in subspaces.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ABOD<V extends NumberVector<V,?>>
Angle-Based Outlier Detection
Outlier detection using variance analysis on angles, especially for high
dimensional data sets.
|
class |
AbstractAggarwalYuOutlier<V extends NumberVector<?,?>>
Abstract base class for the sparse-grid-cell based outlier detection of
Aggarwal and Yu.
|
class |
AggarwalYuEvolutionary<V extends NumberVector<?,?>>
EAFOD provides the evolutionary outlier detection algorithm, an algorithm to
detect outliers for high dimensional data.
|
class |
AggarwalYuNaive<V extends NumberVector<?,?>>
BruteForce provides a naive brute force algorithm in which all k-subsets of
dimensions are examined and calculates the sparsity coefficient to find
outliers.
|
class |
DBOutlierDetection<O,D extends Distance<D>>
Simple distanced based outlier detection algorithm.
|
class |
DBOutlierScore<O,D extends Distance<D>>
Compute percentage of neighbors in the given neighborhood with size d.
|
class |
GaussianUniformMixture<V extends NumberVector<V,?>>
Outlier detection algorithm using a mixture model approach.
|
class |
INFLO<O,D extends NumberDistance<D,?>>
INFLO provides the Mining Algorithms (Two-way Search Method) for Influence
Outliers using Symmetric Relationship
Reference:
Jin, W., Tung, A., Han, J., and Wang, W. 2006 Ranking outliers using symmetric neighborhood relationship In Proc. |
class |
KNNOutlier<O,D extends NumberDistance<D,?>>
Outlier Detection based on the distance of an object to its k nearest
neighbor.
|
class |
KNNWeightOutlier<O,D extends NumberDistance<D,?>>
Outlier Detection based on the accumulated distances of a point to its k
nearest neighbors.
|
class |
LDOF<O,D extends NumberDistance<D,?>>
Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a
Database.
|
class |
LOCI<O,D extends NumberDistance<D,?>>
Fast Outlier Detection Using the "Local Correlation Integral".
|
class |
LOF<O,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 |
LoOP<O,D extends NumberDistance<D,?>>
LoOP: Local Outlier Probabilities
Distance/density based algorithm similar to LOF to detect outliers, but with
statistical methods to achieve better result stability.
|
class |
OPTICSOF<O,D extends NumberDistance<D,?>>
OPTICSOF provides the Optics-of algorithm, an algorithm to find Local
Outliers in a database.
|
class |
ReferenceBasedOutlierDetection<V extends NumberVector<?,?>,D extends NumberDistance<D,?>>
provides the Reference-Based Outlier Detection algorithm, an algorithm that
computes kNN distances approximately, using reference points.
|
class |
SOD<V extends NumberVector<V,?>> |
| Modifier and Type | Class and Description |
|---|---|
class |
FeatureBagging
A simple ensemble method called "Feature bagging" for outlier detection.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CTLuGLSBackwardSearchAlgorithm<V extends NumberVector<?,?>,D extends NumberDistance<D,?>>
GLS-Backward Search is a statistical approach to detecting spatial outliers.
|
class |
CTLuMeanMultipleAttributes<N,O extends NumberVector<?,?>>
Mean Approach is used to discover spatial outliers with multiple attributes.
|
class |
CTLuMedianAlgorithm<N>
Median Algorithm of C.
|
class |
CTLuMedianMultipleAttributes<N,O extends NumberVector<?,?>>
Median Approach is used to discover spatial outliers with multiple
attributes.
|
class |
CTLuMoranScatterplotOutlier<N>
Moran scatterplot outliers, based on the standardized deviation from the
local and global means.
|
class |
CTLuRandomWalkEC<N,D extends NumberDistance<D,?>>
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,D extends NumberDistance<D,?>>
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 2005 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,D extends NumberDistance<D,?>>
The Spatial Outlier Factor (SOF) is a spatial
LOF variation. |
class |
TrimmedMeanApproach<N>
A Trimmed Mean Approach to Finding Spatial Outliers.
|
| Modifier and Type | Method and Description |
|---|---|
private static List<Pair<Reference,List<Class<?>>>> |
DocumentReferences.sortedReferences() |
| Modifier and Type | Class and Description |
|---|---|
class |
KNNExplorer<O extends NumberVector<?,?>,D extends NumberDistance<D,?>>
User application to explore the k Nearest Neighbors for a given data set and
distance function.
|
| Modifier and Type | Class and Description |
|---|---|
class |
HistogramIntersectionDistanceFunction
Intersection distance for color histograms.
|
class |
HSBHistogramQuadraticDistanceFunction
Distance function for HSB color histograms based on a quadratic form and
color similarity.
|
class |
RGBHistogramQuadraticDistanceFunction
Distance function for RGB color histograms based on a quadratic form and
color similarity.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DTWDistanceFunction
Provides the Dynamic Time Warping distance for FeatureVectors.
|
class |
EDRDistanceFunction
Provides the Edit Distance on Real Sequence distance for FeatureVectors.
|
class |
ERPDistanceFunction
Provides the Edit Distance With Real Penalty distance for FeatureVectors.
|
class |
LCSSDistanceFunction
Provides the Longest Common Subsequence distance for FeatureVectors.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MTree<O,D extends Distance<D>>
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 |
TopologicalSplitter
Encapsulates the required parameters for a topological split of a R*-Tree.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ConvexHull2D
Classes to compute the convex hull of a set of points in 2D, using the
classic Grahams scan.
|
class |
Mean
Compute the mean using a numerically stable online algorithm.
|
class |
MeanVariance
Do some simple statistics (mean, variance) using a numerically stable online
algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
WeightedCovarianceMatrixBuilder<V extends NumberVector<? extends V,?>>
CovarianceMatrixBuilder with weights. |
| Modifier and Type | Class and Description |
|---|---|
class |
KMLOutputHandler
Class to handle KML output.
|
| Modifier and Type | Method and Description |
|---|---|
static Reference |
DocumentationUtil.getReference(Class<?> c)
Get the reference annotation of a class, or
null. |
| Modifier and Type | Class and Description |
|---|---|
class |
HeDESNormalizationOutlierScaling
Normalization used by HeDES
|
class |
MinusLogGammaScaling
Scaling that can map arbitrary values to a probability in the range of [0:1],
by assuming a Gamma distribution on the data and evaluating the Gamma CDF.
|
class |
MinusLogStandardDeviationScaling
Scaling that can map arbitrary values to a probability in the range of [0:1].
|
class |
MixtureModelOutlierScalingFunction
Tries to fit a mixture model (exponential for inliers and gaussian for
outliers) to the outlier score distribution.
|
class |
MultiplicativeInverseScaling
Scaling function to invert values basically by computing 1/x, but in a variation
that maps the values to the [0:1] interval and avoiding division by 0.
|
class |
OutlierGammaScaling
Scaling that can map arbitrary values to a probability in the range of [0:1]
by assuming a Gamma distribution on the values.
|
class |
OutlierMinusLogScaling
Scaling function to invert values by computing -1 * Math.log(x)
Useful for example for scaling
ABOD, but see
MinusLogStandardDeviationScaling and MinusLogGammaScaling for
more advanced scalings for this algorithm. |
class |
SigmoidOutlierScalingFunction
Tries to fit a sigmoid to the outlier scores and use it to convert the values
to probability estimates in the range of 0.0 to 1.0
|
class |
SqrtStandardDeviationScaling
Scaling that can map arbitrary values to a probability in the range of [0:1].
|
class |
StandardDeviationScaling
Scaling that can map arbitrary values to a probability in the range of [0:1].
|
| Modifier and Type | Class and Description |
|---|---|
class |
BubbleVisualization<NV extends NumberVector<NV,?>>
Generates a SVG-Element containing bubbles.
|