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

Packages that use Algorithm

de.lmu.ifi.dbs.elki	ELKI framework "Environment for Developing KDD-Applications Supported by Index-Structures" KDDTask is the main class of the ELKI-Framework for command-line interaction.
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.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.evaluation	Functionality for the evaluation of algorithms.

Uses of Algorithm in de.lmu.ifi.dbs.elki

Fields in de.lmu.ifi.dbs.elki declared as Algorithm

private Algorithm<O,Result>

KDDTask.algorithm Holds the algorithm to run.

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

private ClassParameter<Algorithm<O,Result>>

KDDTask.ALGORITHM_PARAM Parameter to specify the algorithm to be applied, must extend Algorithm.

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

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

class	AbstractAlgorithm<O extends DatabaseObject,R extends Result> AbstractAlgorithm sets the values for flags verbose and time.
class	APRIORI Provides the APRIORI algorithm for Mining Association Rules.
class	DependencyDerivator<V extends RealVector<V,?>,D extends Distance<D>> Dependency derivator computes quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA.
class	DistanceBasedAlgorithm<O extends DatabaseObject,D extends Distance<D>,R extends Result> Provides an abstract algorithm already setting the distance function.
class	DummyAlgorithm<V extends NumberVector<V,?>> Dummy Algorithm, which just iterates over all points once, doing a 10NN query each.
class	KNNDistanceOrder<O extends DatabaseObject,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<V extends RealVector<V,?>,D extends NumberDistance<D,N>,N extends Number> Algorithm to materialize all the distances in a data set.
class	NullAlgorithm<V extends NumberVector<V,?>> 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

interface

ClusteringAlgorithm<C extends Clustering<? extends Model>,O extends DatabaseObject> Interface for Algorithms that are capable to provide a Clustering as Result.

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

class	ByLabelClustering<O extends DatabaseObject> Pseudo clustering using labels.
class	ByLabelHierarchicalClustering<O extends DatabaseObject> Pseudo clustering using labels.
class	DBSCAN<O extends DatabaseObject,D extends Distance<D>> DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database.
class	DeLiClu<O extends NumberVector<O,?>,D extends Distance<D>> DeLiClu provides the DeLiClu algorithm, a hierarchical algorithm to find density-connected sets in a database.
class	EM<V extends RealVector<V,?>> Provides the EM algorithm (clustering by expectation maximization).
class	KMeans<D extends Distance<D>,V extends RealVector<V,?>> Provides the k-means algorithm.
class	OPTICS<O extends DatabaseObject,D extends Distance<D>> OPTICS provides the OPTICS algorithm.
class	ProjectedDBSCAN<V extends RealVector<V,?>> Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
class	SLINK<O extends DatabaseObject,D extends Distance<D>> Efficient implementation of the Single-Link Algorithm SLINK of R.
class	SNNClustering<O extends DatabaseObject,D extends Distance<D>> Shared nearest neighbor clustering.
class	TrivialAllInOne<O extends DatabaseObject> Trivial pseudo-clustering that just considers all points to be one big cluster.
class	TrivialAllNoise<O extends DatabaseObject> Trivial pseudo-clustering that just considers all points to be noise.

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

class	CASH Provides the CASH algorithm, an subspace clustering algorithm based on the hough transform.
class	COPAC<V extends RealVector<V,?>> 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 RealVector<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<O extends RealVector<O,?>> 4C identifies local subgroups of data objects sharing a uniform correlation.
class	ORCLUS<V extends RealVector<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

class	CLIQUE<V extends RealVector<V,?>> Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.
class	DiSH<V extends RealVector<V,?>> Algorithm for detecting subspace hierarchies.
class	PreDeCon<V extends RealVector<V,?>> PreDeCon computes clusters of subspace preference weighted connected points.
class	PROCLUS<V extends RealVector<V,?>> Provides the PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces.
class	ProjectedClustering<V extends RealVector<V,?>> Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.

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

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

class	ABOD<V extends RealVector<V,?>> Angle-Based Outlier Detection Outlier detection using variance analysis on angles, especially for high dimensional data sets.
class	LOCI<O extends DatabaseObject,D extends NumberDistance<D,?>> Fast Outlier Detection Using the "Local Correlation Integral".
class	LOF<O extends DatabaseObject,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	SOD<V extends RealVector<V,Double>,D extends Distance<D>>

Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.statistics

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

class	DistanceStatisticsWithClasses<V extends RealVector<V,?>,D extends NumberDistance<D,?>> Algorithm to gather statistics over the distance distribution in the data set.
class	EvaluateRankingQuality<V extends RealVector<V,?>,D extends NumberDistance<D,?>> Evaluate a distance function with respect to kNN queries.
class	RankingQualityHistogram<V extends RealVector<V,?>,D extends NumberDistance<D,?>> Evaluate a distance function with respect to kNN queries.

Uses of Algorithm in de.lmu.ifi.dbs.elki.evaluation

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

class

ComputeROCCurve<O extends DatabaseObject> Compute a ROC curve to evaluate a ranking algorithm and compute the corresponding ROCAUC value.

Fields in de.lmu.ifi.dbs.elki.evaluation declared as Algorithm

private Algorithm<O,Result>

ComputeROCCurve.algorithm Holds the algorithm to run.

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

private ClassParameter<Algorithm<O,Result>>

ComputeROCCurve.ALGORITHM_PARAM Parameter to specify the algorithm to be applied, must extend Algorithm.