Uses of Interface
de.lmu.ifi.dbs.elki.algorithm.Algorithm

Packages that use Algorithm

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.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

Classes in de.lmu.ifi.dbs.elki.algorithm that implement Algorithm

Modifier and Type	Class and Description
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

Subinterfaces of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that implement Algorithm

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.

Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace that implement Algorithm

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.

Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial that implement Algorithm

Modifier and Type	Class and Description
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

Subinterfaces of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier

Modifier and Type	Interface and Description
interface	OutlierAlgorithm Generic super interface for outlier detection algorithms.

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier that implement Algorithm

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	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,?>>

Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.meta

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.meta that implement Algorithm

Modifier and Type	Class and Description
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

Modifier and Type	Field and Description
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

Constructor and Description
RescaleMetaOutlierAlgorithm(Algorithm algorithm, ScalingFunction scaling) Constructor.

Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial that implement Algorithm

Modifier and Type	Class and Description
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

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.trivial that implement Algorithm

Modifier and Type	Class and Description
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

Classes in de.lmu.ifi.dbs.elki.algorithm.statistics that implement Algorithm

Modifier and Type	Class and Description
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

Fields in de.lmu.ifi.dbs.elki.workflow with type parameters of type Algorithm

Modifier and Type	Field and Description
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

Constructor and Description
AlgorithmStep(List<Algorithm> algorithms) Constructor.