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

Packages that use DistanceBasedAlgorithm

Package	Description
de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.benchmark	Benchmarking pseudo algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
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.lof	LOF family of outlier detection algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial	Spatial 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.)
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.
tutorial.outlier

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

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

Modifier and Type	Class and Description
class	AbstractDistanceBasedAlgorithm<O,D extends Distance<D>,R extends Result> Provides an abstract algorithm already setting the distance function.
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<?>,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.

Uses of DistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.benchmark

Classes in de.lmu.ifi.dbs.elki.algorithm.benchmark that implement DistanceBasedAlgorithm

Modifier and Type	Class and Description
class	KNNBenchmarkAlgorithm<O,D extends Distance<D>> Benchmarking algorithm that computes the k nearest neighbors for each query point.
class	RangeQueryBenchmarkAlgorithm<O extends NumberVector<?>,D extends NumberDistance<D,?>> Benchmarking algorithm that computes a range query for each point.
class	ValidateApproximativeKNNIndex<O,D extends Distance<D>> Algorithm to validate the quality of an approximative kNN index, by performing a number of queries and comparing them to the results obtained by exact indexing (e.g. linear scanning).

Uses of DistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering

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

Modifier and Type	Class and Description
class	CanopyPreClustering<O,D extends Distance<D>> Canopy pre-clustering is a simple preprocessing step for clustering.
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<?>,D extends Distance<D>> DeLiClu provides the DeLiClu algorithm, a hierarchical algorithm to find density-connected sets in a database.
class	NaiveMeanShiftClustering<V extends NumberVector<?>,D extends NumberDistance<D,?>> Mean-shift based clustering algorithm.
class	OPTICS<O,D extends Distance<D>> OPTICS provides the OPTICS algorithm.

Uses of DistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

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

Modifier and Type	Class and Description
class	HiCO<V extends NumberVector<?>> Implementation of the HiCO algorithm, an algorithm for detecting hierarchies of correlation clusters.

Uses of DistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical

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

Modifier and Type	Class and Description
class	NaiveAgglomerativeHierarchicalClustering<O,D extends NumberDistance<D,?>> This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.
class	SLINK<O,D extends Distance<D>> Implementation of the efficient Single-Link Algorithm SLINK of R.

Uses of DistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Subinterfaces of DistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Modifier and Type	Interface and Description
interface	KMeans<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> Some constants and options shared among kmeans family algorithms.

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

Modifier and Type	Class and Description
class	AbstractKMeans<V extends NumberVector<?>,D extends Distance<D>,M extends MeanModel<V>> Abstract base class for k-means implementations.
class	BestOfMultipleKMeans<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> Run K-Means multiple times, and keep the best run.
class	KMeansBatchedLloyd<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, using Lloyd-style bulk iterations.
class	KMeansBisecting<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> The bisecting k-means algorithm works by starting with an initial partitioning into two clusters, then repeated splitting of the largest cluster to get additional clusters.
class	KMeansHybridLloydMacQueen<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, alternating between MacQueen-style incremental processing and Lloyd-Style batch steps.
class	KMeansLloyd<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, using Lloyd-style bulk iterations.
class	KMeansMacQueen<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, using MacQueen style incremental updates.
class	KMediansLloyd<V extends NumberVector<?>,D extends Distance<D>> Provides the k-medians clustering algorithm, using Lloyd-style bulk iterations.
class	KMedoidsEM<V,D extends NumberDistance<D,?>> Provides the k-medoids clustering algorithm, using a "bulk" variation of the "Partitioning Around Medoids" approach.
class	KMedoidsPAM<V,D extends NumberDistance<D,?>> Provides the k-medoids clustering algorithm, using the "Partitioning Around Medoids" approach.

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

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

Modifier and Type	Class and Description
class	HiSC<V extends NumberVector<?>> Implementation of the HiSC algorithm, an algorithm for detecting hierarchies of subspace clusters.

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

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

Modifier and Type	Class and Description
class	AbstractDBOutlier<O,D extends Distance<D>> Simple distance based outlier detection algorithms.
class	COP<V extends NumberVector<?>,D extends NumberDistance<D,?>> Correlation outlier probability: Outlier Detection in Arbitrarily Oriented Subspaces Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek Outlier Detection in Arbitrarily Oriented Subspaces in: Proc.
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	DWOF<O,D extends NumberDistance<D,?>> Algorithm to compute dynamic-window outlier factors in a database based on a specified parameter DWOF.Parameterizer.K_ID (-dwof.k).
class	HilOut<O extends NumberVector<?>> Fast Outlier Detection in High Dimensional Spaces Outlier Detection using Hilbert space filling curves Reference: F.
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	ODIN<O,D extends Distance<D>> Outlier detection based on the in-degree of the kNN graph.
class	OPTICSOF<O,D extends NumberDistance<D,?>> OPTICSOF provides the Optics-of algorithm, an algorithm to find Local Outliers in a database.
class	SimpleCOP<V extends NumberVector<?>,D extends NumberDistance<D,?>> Algorithm to compute local correlation outlier probability.

Uses of DistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.lof

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

Modifier and Type	Class and Description
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	LDF<O extends NumberVector<?>,D extends NumberDistance<D,?>> Outlier Detection with Kernel Density Functions.
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.Parameterizer.K_ID (-lof.k).
class	SimpleKernelDensityLOF<O extends NumberVector<?>,D extends NumberDistance<D,?>> A simple variant of the LOF algorithm, which uses a simple kernel density estimation instead of the local reachability density.
class	SimplifiedLOF<O,D extends NumberDistance<D,?>> A simplified version of the original LOF algorithm, which does not use the reachability distance, yielding less stable results on inliers.

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

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

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	CTLuRandomWalkEC<N,D extends NumberDistance<D,?>> Spatial outlier detection based on random walks.

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

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

Modifier and Type	Class and Description
class	AveragePrecisionAtK<V,D extends NumberDistance<D,?>> Evaluate a distance functions performance by computing the average precision at k, when ranking the objects by distance.
class	DistanceStatisticsWithClasses<O,D extends NumberDistance<D,?>> Algorithm to gather statistics over the distance distribution in the data set.
class	EvaluateRankingQuality<V extends NumberVector<?>,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 DistanceBasedAlgorithm in tutorial.clustering

Classes in tutorial.clustering that implement DistanceBasedAlgorithm

Modifier and Type	Class and Description
class	NaiveAgglomerativeHierarchicalClustering1<O,D extends NumberDistance<D,?>> This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.
class	NaiveAgglomerativeHierarchicalClustering2<O,D extends NumberDistance<D,?>> This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.
class	NaiveAgglomerativeHierarchicalClustering3<O,D extends NumberDistance<D,?>> This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.
class	NaiveAgglomerativeHierarchicalClustering4<O,D extends NumberDistance<D,?>> This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.
class	SameSizeKMeansAlgorithm<V extends NumberVector<?>> K-means variation that produces equally sized clusters.

Uses of DistanceBasedAlgorithm in tutorial.outlier

Classes in tutorial.outlier that implement DistanceBasedAlgorithm

Modifier and Type	Class and Description
class	DistanceStddevOutlier<O,D extends NumberDistance<D,?>> A simple outlier detection algorithm that computes the standard deviation of the kNN distances.