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

Packages that use Algorithm
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
 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
 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
 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
 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
 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
 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
 interface OutlierAlgorithm
          Generic super interface for outlier detection algorithms.
 

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier that implement Algorithm
 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
 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
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
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
 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
 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
 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
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
AlgorithmStep(List<Algorithm> algorithms)
          Constructor.
 


Release 0.4.0 (2011-09-20_1324)