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Packages that use Algorithm | |
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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.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.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.algorithm.outlier.trivial | Trivial outlier detection algorithms: no outliers, all outliers, label outliers. |
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.workflow | Work flow packages, e.g. following the usual KDD model, closely related to CRISP-DM |
Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm |
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Classes in de.lmu.ifi.dbs.elki.algorithm that implement Algorithm | |
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class |
AbstractAlgorithm<R extends Result>
This class serves also as a model of implementing an algorithm within this framework. |
class |
AbstractDistanceBasedAlgorithm<O,D extends Distance<D>,R extends Result>
Provides an abstract algorithm already setting the distance function. |
class |
AbstractPrimitiveDistanceBasedAlgorithm<O,D extends Distance<D>,R extends Result>
Provides an abstract algorithm already setting the distance function. |
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. |
class |
DummyAlgorithm<O extends NumberVector<?,?>>
Dummy Algorithm, which just iterates over all points once, doing a 10NN query each. |
class |
KNNDistanceOrder<O,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<O,D extends NumberDistance<D,?>>
Algorithm to materialize all the distances in a data set. |
class |
NullAlgorithm
Null Algorithm, which does nothing. |
Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering |
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Subinterfaces of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering | |
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interface |
ClusteringAlgorithm<C extends Clustering<? extends Model>>
Interface for Algorithms that are capable to provide a Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm -Interface. |
interface |
OPTICSTypeAlgorithm<D extends Distance<D>>
Interface for OPTICS type algorithms, that can be analysed by OPTICS Xi etc. |
Classes in de.lmu.ifi.dbs.elki.algorithm.clustering that implement Algorithm | |
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class |
AbstractProjectedClustering<R extends Clustering<Model>,V extends NumberVector<V,?>>
Abstract superclass for projected clustering algorithms, like PROCLUS
and ORCLUS . |
class |
AbstractProjectedDBSCAN<R extends Clustering<Model>,V extends NumberVector<V,?>>
Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor. |
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 |
OPTICSXi<N extends NumberDistance<N,?>>
Class to handle OPTICS Xi extraction. |
class |
SLINK<O,D extends Distance<D>>
Efficient implementation of the Single-Link Algorithm SLINK of R. |
class |
SNNClustering<O>
Shared nearest neighbor clustering. |
Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that implement Algorithm | |
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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. |
Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace that implement Algorithm | |
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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. |
Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial that implement Algorithm | |
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class |
ByLabelClustering
Pseudo clustering using labels. |
class |
ByLabelHierarchicalClustering
Pseudo clustering using labels. |
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 Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier |
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Subinterfaces of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier | |
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interface |
OutlierAlgorithm
Generic super interface for outlier detection algorithms. |
Classes in de.lmu.ifi.dbs.elki.algorithm.outlier that implement Algorithm | |
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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 |
AbstractDBOutlier<O,D extends Distance<D>>
Simple distance based outlier detection algorithms. |
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 |
EMOutlier<V extends NumberVector<V,?>>
outlier detection algorithm using EM Clustering. |
class |
GaussianModel<V extends NumberVector<V,?>>
Outlier have smallest GMOD_PROB: the outlier scores is the probability density of the assumed distribution. |
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 |
OnlineLOF<O,D extends NumberDistance<D,?>>
Incremental version of the LOF Algorithm, supports insertions and
removals. |
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,?>>
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Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.meta |
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Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.meta that implement Algorithm | |
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class |
ExternalDoubleOutlierScore
External outlier detection scores, loading outlier scores from an external file. |
class |
FeatureBagging
A simple ensemble method called "Feature bagging" for outlier detection. |
class |
RescaleMetaOutlierAlgorithm
Scale another outlier score using the given scaling function. |
Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.meta declared as Algorithm | |
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private Algorithm |
RescaleMetaOutlierAlgorithm.algorithm
Holds the algorithm to run. |
private Algorithm |
RescaleMetaOutlierAlgorithm.Parameterizer.algorithm
Holds the algorithm to run. |
Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.meta with parameters of type Algorithm | |
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RescaleMetaOutlierAlgorithm(Algorithm algorithm,
ScalingFunction scaling)
Constructor. |
Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial |
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Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial that implement Algorithm | |
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class |
AbstractDistanceBasedSpatialOutlier<N,O,D extends NumberDistance<D,?>>
Abstract base class for distance-based spatial outlier detection methods. |
class |
AbstractNeighborhoodOutlier<O>
Abstract base class for spatial outlier detection methods using a spatial neighborhood. |
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. |
Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.trivial |
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Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.trivial that implement Algorithm | |
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class |
ByLabelOutlier
Trivial algorithm that marks outliers by their label. |
class |
TrivialAllOutlier
Trivial method that claims all objects to be outliers. |
class |
TrivialNoOutlier
Trivial method that claims to find no outliers. |
Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.statistics |
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Classes in de.lmu.ifi.dbs.elki.algorithm.statistics that implement Algorithm | |
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class |
DistanceStatisticsWithClasses<O,D extends NumberDistance<D,?>>
Algorithm to gather statistics over the distance distribution in the data set. |
class |
EvaluateRankingQuality<V extends NumberVector<V,?>,D extends NumberDistance<D,?>>
Evaluate a distance function with respect to kNN queries. |
class |
RankingQualityHistogram<O,D extends NumberDistance<D,?>>
Evaluate a distance function with respect to kNN queries. |
Uses of Algorithm in de.lmu.ifi.dbs.elki.workflow |
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Fields in de.lmu.ifi.dbs.elki.workflow with type parameters of type Algorithm | |
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private List<Algorithm> |
AlgorithmStep.algorithms
Holds the algorithm to run. |
protected List<Algorithm> |
AlgorithmStep.Parameterizer.algorithms
Holds the algorithm to run. |
Constructor parameters in de.lmu.ifi.dbs.elki.workflow with type arguments of type Algorithm | |
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AlgorithmStep(List<Algorithm> algorithms)
Constructor. |
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