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.classification	Classification algorithms.
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.gdbscan	Generalized DBSCAN Generalized DBSCAN is an abstraction of the original DBSCAN idea, that allows the use of arbitrary "neighborhood" and "core point" predicates.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical	Hierarchical agglomerative clustering (HAC).
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel	Parallelized implementations of k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.optics	OPTICS family of clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering	Clustering based outlier detection.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance	Distance-based outlier detection algorithms, such as DBOutlier and kNN.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel	Parallel implementations of distance-based outlier detectors.
de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic	Outlier detection algorithms based on intrinsic dimensionality.
de.lmu.ifi.dbs.elki.algorithm.outlier.lof	LOF family of outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel	Parallelized variants of LOF.
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial	Spatial outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.statistics	Statistical analysis algorithms.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation
tutorial.outlier	Tutorials on implementing outlier detection methods in ELKI.

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,R extends Result> Abstract base class for distance-based algorithms.
class	AbstractNumberVectorDistanceBasedAlgorithm<O,R extends Result> Abstract base class for distance-based algorithms that need to work with synthetic numerical vectors such as mean vectors.
class	DependencyDerivator<V extends NumberVector> Dependency derivator computes quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA.
class	KNNDistancesSampler<O> Provides an order of the kNN-distances for all objects within the database.
class	KNNJoin<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry> Joins in a given spatial database to each object its k-nearest neighbors.

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> Benchmarking algorithm that computes the k nearest neighbors for each query point.
class	RangeQueryBenchmarkAlgorithm<O extends NumberVector> Benchmarking algorithm that computes a range query for each point.
class	ValidateApproximativeKNNIndex<O> 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.classification

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

Modifier and Type	Class and Description
class	KNNClassifier<O> KNNClassifier classifies instances based on the class distribution among the k nearest neighbors in a database.

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> Canopy pre-clustering is a simple preprocessing step for clustering.
class	DBSCAN<O> Density-Based Clustering of Applications with Noise (DBSCAN), an algorithm to find density-connected sets in a database.
class	GriDBSCAN<V extends NumberVector> Using Grid for Accelerating Density-Based Clustering.
class	Leader<O> Leader clustering algorithm.
class	NaiveMeanShiftClustering<V extends NumberVector> Mean-shift based clustering algorithm.

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

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

Modifier and Type	Class and Description
class	LSDBC<O extends NumberVector> Locally Scaled Density Based Clustering.

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	AbstractHDBSCAN<O,R extends Result> Abstract base class for HDBSCAN variations.
class	AGNES<O> Hierarchical Agglomerative Clustering (HAC) or Agglomerative Nesting (AGNES) is a classic hierarchical clustering algorithm.
class	AnderbergHierarchicalClustering<O> This is a modification of the classic AGNES algorithm for hierarchical clustering using a nearest-neighbor heuristic for acceleration.
class	CLINK<O> CLINK algorithm for complete linkage.
class	HDBSCANLinearMemory<O> Linear memory implementation of HDBSCAN clustering.
class	MiniMax<O> Minimax Linkage clustering.
class	MiniMaxAnderberg<O> This is a modification of the classic MiniMax algorithm for hierarchical clustering using a nearest-neighbor heuristic for acceleration.
class	MiniMaxNNChain<O> MiniMax hierarchical clustering using the NNchain algorithm.
class	NNChain<O> NNchain clustering algorithm.
class	SLINK<O> Implementation of the efficient Single-Link Algorithm SLINK of R.
class	SLINKHDBSCANLinearMemory<O> Linear memory implementation of HDBSCAN clustering based on SLINK.

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,M extends Model> 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,M extends Model> Abstract base class for k-means implementations.
class	BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel> Run K-Means multiple times, and keep the best run.
class	CLARA<V> Clustering Large Applications (CLARA) is a clustering method for large data sets based on PAM, partitioning around medoids (KMedoidsPAM) based on sampling.
class	CLARANS<V> CLARANS: a method for clustering objects for spatial data mining is inspired by PAM (partitioning around medoids, KMedoidsPAM) and CLARA and also based on sampling.
class	FastCLARA<V> Clustering Large Applications (CLARA) with the KMedoidsFastPAM improvements, to increase scalability in the number of clusters.
class	FastCLARANS<V> A faster variation of CLARANS, that can explore O(k) as many swaps at a similar cost by considering all medoids for each candidate non-medoid.
class	KMeansAnnulus<V extends NumberVector> Annulus k-means algorithm.
class	KMeansBisecting<V extends NumberVector,M extends MeanModel> 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	KMeansCompare<V extends NumberVector> Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.
class	KMeansElkan<V extends NumberVector> Elkan's fast k-means by exploiting the triangle inequality.
class	KMeansExponion<V extends NumberVector> Newlings's exponion k-means algorithm, exploiting the triangle inequality.
class	KMeansHamerly<V extends NumberVector> Hamerly's fast k-means by exploiting the triangle inequality.
class	KMeansLloyd<V extends NumberVector> The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).
class	KMeansMacQueen<V extends NumberVector> The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
class	KMeansMinusMinus<V extends NumberVector> k-means--: A Unified Approach to Clustering and Outlier Detection.
class	KMeansSimplifiedElkan<V extends NumberVector> Simplified version of Elkan's k-means by exploiting the triangle inequality.
class	KMeansSort<V extends NumberVector> Sort-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means (with sorting).
class	KMediansLloyd<V extends NumberVector> k-medians clustering algorithm, but using Lloyd-style bulk iterations instead of the more complicated approach suggested by Kaufman and Rousseeuw (see KMedoidsPAM instead).
class	KMedoidsFastPAM<V> FastPAM: An improved version of PAM, that is usually O(k) times faster.
class	KMedoidsFastPAM1<V> FastPAM1: A version of PAM that is O(k) times faster, i.e., now in O((n-k)²).
class	KMedoidsPAM<V> The original Partitioning Around Medoids (PAM) algorithm or k-medoids clustering, as proposed by Kaufman and Rousseeuw in "Clustering by means of Medoids".
class	KMedoidsPAMReynolds<V> The Partitioning Around Medoids (PAM) algorithm with some additional optimizations proposed by Reynolds et al.
class	KMedoidsPark<V> A k-medoids clustering algorithm, implemented as EM-style bulk algorithm.
class	SingleAssignmentKMeans<V extends NumberVector> Pseudo-k-Means variations, that assigns each object to the nearest center.
class	XMeans<V extends NumberVector,M extends MeanModel> X-means: Extending K-means with Efficient Estimation on the Number of Clusters.

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

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

Modifier and Type	Class and Description
class	ParallelLloydKMeans<V extends NumberVector> Parallel implementation of k-Means clustering.

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

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

Modifier and Type	Class and Description
class	AbstractOPTICS<O> The OPTICS algorithm for density-based hierarchical clustering.
class	DeLiClu<V extends NumberVector> DeliClu: Density-Based Hierarchical Clustering A hierarchical algorithm to find density-connected sets in a database, closely related to OPTICS but exploiting the structure of a R-tree for acceleration.
class	OPTICSHeap<O> The OPTICS algorithm for density-based hierarchical clustering.
class	OPTICSList<O> The OPTICS algorithm for density-based hierarchical clustering.

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	COP<V extends NumberVector> Correlation outlier probability: Outlier Detection in Arbitrarily Oriented Subspaces Reference: Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek Outlier Detection in Arbitrarily Oriented Subspaces Proc.
class	DWOF<O> Algorithm to compute dynamic-window outlier factors in a database based on a specified parameter k, which specifies the number of the neighbors to be considered during the calculation of the DWOF score.
class	OPTICSOF<O> OPTICS-OF outlier detection algorithm, an algorithm to find Local Outliers in a database based on ideas from OPTICSTypeAlgorithm clustering.
class	SimpleCOP<V extends NumberVector> Algorithm to compute local correlation outlier probability.

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

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

Modifier and Type	Class and Description
class	CBLOF<O extends NumberVector> Cluster-based local outlier factor (CBLOF).
class	SilhouetteOutlierDetection<O> Outlier detection by using the Silhouette Coefficients.

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

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

Modifier and Type	Class and Description
class	AbstractDBOutlier<O> Simple distance based outlier detection algorithms.
class	DBOutlierDetection<O> Simple distanced based outlier detection algorithm.
class	DBOutlierScore<O> Compute percentage of neighbors in the given neighborhood with size d.
class	HilOut<O extends NumberVector> Fast Outlier Detection in High Dimensional Spaces Outlier Detection using Hilbert space filling curves Reference: F.
class	KNNDD<O> Nearest Neighbor Data Description.
class	KNNOutlier<O> Outlier Detection based on the distance of an object to its k nearest neighbor.
class	KNNSOS<O> kNN-based adaption of Stochastic Outlier Selection.
class	KNNWeightOutlier<O> Outlier Detection based on the accumulated distances of a point to its k nearest neighbors.
class	LocalIsolationCoefficient<O> The Local Isolation Coefficient is the sum of the kNN distance and the average distance to its k nearest neighbors.
class	ODIN<O> Outlier detection based on the in-degree of the kNN graph.
class	ReferenceBasedOutlierDetection Reference-Based Outlier Detection algorithm, an algorithm that computes kNN distances approximately, using reference points.
class	SOS<O> Stochastic Outlier Selection.

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

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

Modifier and Type	Class and Description
class	ParallelKNNOutlier<O> Parallel implementation of KNN Outlier detection.
class	ParallelKNNWeightOutlier<O> Parallel implementation of KNN Weight Outlier detection.

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

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

Modifier and Type	Class and Description
class	IDOS<O> Intrinsic Dimensional Outlier Detection in High-Dimensional Data.
class	IntrinsicDimensionalityOutlier<O> Use intrinsic dimensionality for outlier detection.
class	ISOS<O> Intrinsic Stochastic Outlier Selection.

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	COF<O> Connectivity-based Outlier Factor (COF).
class	INFLO<O> Influence Outliers using Symmetric Relationship (INFLO) using two-way search, is an outlier detection method based on LOF; but also using the reverse kNN.
class	KDEOS<O> Generalized Outlier Detection with Flexible Kernel Density Estimates.
class	LDF<O extends NumberVector> Outlier Detection with Kernel Density Functions.
class	LDOF<O> Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a Database.
class	LOCI<O> Fast Outlier Detection Using the "Local Correlation Integral".
class	LOF<O> Algorithm to compute density-based local outlier factors in a database based on a specified parameter -lof.k.
class	SimpleKernelDensityLOF<O extends NumberVector> A simple variant of the LOF algorithm, which uses a simple kernel density estimation instead of the local reachability density.
class	SimplifiedLOF<O> A simplified version of the original LOF algorithm, which does not use the reachability distance, yielding less stable results on inliers.
class	VarianceOfVolume<O extends SpatialComparable> Variance of Volume for outlier detection.

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

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

Modifier and Type	Class and Description
class	ParallelLOF<O> Parallel implementation of Local Outlier Factor using processors.
class	ParallelSimplifiedLOF<O> Parallel implementation of Simplified-LOF Outlier detection using processors.

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> GLS-Backward Search is a statistical approach to detecting spatial outliers.
class	CTLuRandomWalkEC<P> 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<O> Evaluate a distance functions performance by computing the average precision at k, when ranking the objects by distance.
class	DistanceQuantileSampler<O> Compute a quantile of a distance sample, useful for choosing parameters for algorithms.
class	DistanceStatisticsWithClasses<O> Algorithm to gather statistics over the distance distribution in the data set.
class	EstimateIntrinsicDimensionality<O> Estimate global average intrinsic dimensionality of a data set.
class	EvaluateRankingQuality<V extends NumberVector> Evaluate a distance function with respect to kNN queries.
class	EvaluateRetrievalPerformance<O> Evaluate a distance functions performance by computing the mean average precision, ROC, and NN classification performance when ranking the objects by distance.
class	HopkinsStatisticClusteringTendency The Hopkins Statistic of Clustering Tendency measures the probability that a data set is generated by a uniform data distribution.
class	RangeQuerySelectivity<V extends NumberVector> Evaluate the range query selectivity.
class	RankingQualityHistogram<O> 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> This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.
class	NaiveAgglomerativeHierarchicalClustering2<O> This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.
class	NaiveAgglomerativeHierarchicalClustering3<O> This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.
class	NaiveAgglomerativeHierarchicalClustering4<O> 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> A simple outlier detection algorithm that computes the standard deviation of the kNN distances.