## Uses of Classde.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm

• Packages that use AbstractDistanceBasedAlgorithm
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 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.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 AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm

Subclasses of AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm
Modifier and Type Class and Description
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 AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.benchmark

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 AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering

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>
class  NaiveMeanShiftClustering<V extends NumberVector>
Mean-shift based clustering algorithm.
• ### Uses of AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan

Modifier and Type Class and Description
class  LSDBC<O extends NumberVector>
Locally Scaled Density Based Clustering.
• ### Uses of AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical

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>
class  HDBSCANLinearMemory<O>
Linear memory implementation of HDBSCAN clustering.
class  MiniMax<O>
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>
class  SLINKHDBSCANLinearMemory<O>
Linear memory implementation of HDBSCAN clustering based on SLINK.
• ### Uses of AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Modifier and Type Class and Description
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  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.
• ### Uses of AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.optics

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 AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.outlier

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 AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering

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 AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.distance

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  SOS<O>
Stochastic Outlier Selection.
• ### Uses of AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel

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 AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic

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 AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.lof

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 AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel

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 AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial

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 AbstractDistanceBasedAlgorithm in de.lmu.ifi.dbs.elki.algorithm.statistics

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  RangeQuerySelectivity<V extends NumberVector>
Evaluate the range query selectivity.
class  RankingQualityHistogram<O>
Evaluate a distance function with respect to kNN queries.
• ### Uses of AbstractDistanceBasedAlgorithm in tutorial.clustering

Subclasses of AbstractDistanceBasedAlgorithm in tutorial.clustering
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.
• ### Uses of AbstractDistanceBasedAlgorithm in tutorial.outlier

Subclasses of AbstractDistanceBasedAlgorithm in tutorial.outlier
Modifier and Type Class and Description
class  DistanceStddevOutlier<O>
A simple outlier detection algorithm that computes the standard deviation of the kNN distances.