Uses of Interface
elki.clustering.ClusteringAlgorithm
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Packages that use ClusteringAlgorithm Package Description elki.application.experiments Packaged experiments to make them easy to reproduce.elki.clustering Clustering algorithms.elki.clustering.affinitypropagation Affinity Propagation (AP) clustering.elki.clustering.biclustering Biclustering algorithms.elki.clustering.correlation Correlation clustering algorithms.elki.clustering.dbscan DBSCAN and its generalizations.elki.clustering.dbscan.parallel Parallel versions of Generalized DBSCAN.elki.clustering.em Expectation-Maximization clustering algorithm for Gaussian Mixture Modeling (GMM).elki.clustering.hierarchical.birch BIRCH clustering.elki.clustering.hierarchical.extraction Extraction of partitional clusterings from hierarchical results.elki.clustering.kcenter K-center clustering.elki.clustering.kmeans K-means clustering and variations.elki.clustering.kmeans.parallel Parallelized implementations of k-means.elki.clustering.kmeans.spherical Spherical k-means clustering and variations.elki.clustering.kmedoids K-medoids clustering (PAM).elki.clustering.meta Meta clustering algorithms, that get their result from other clusterings or external sources.elki.clustering.onedimensional Clustering algorithms for one-dimensional data.elki.clustering.optics OPTICS family of clustering algorithms.elki.clustering.silhouette Silhouette clustering algorithms.elki.clustering.subspace Axis-parallel subspace clustering algorithms.elki.clustering.svm elki.clustering.trivial Trivial clustering algorithms: all in one, no clusters, label clusterings.elki.clustering.uncertain Clustering algorithms for uncertain data.elki.evaluation.clustering Evaluation of clustering results.elki.outlier.clustering Clustering based outlier detection.tutorial.clustering Classes from the tutorial on implementing a custom k-means variation. -
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Uses of ClusteringAlgorithm in elki.application.experiments
Fields in elki.application.experiments with type parameters of type ClusteringAlgorithm Modifier and Type Field Description private java.lang.Class<? extends ClusteringAlgorithm<?>>
ORLibBenchmark. alg
Clustering algorithmprivate java.lang.Class<? extends ClusteringAlgorithm<?>>
ORLibBenchmark.Par. alg
Clustering algorithmConstructor parameters in elki.application.experiments with type arguments of type ClusteringAlgorithm Constructor Description ORLibBenchmark(java.net.URI file, java.lang.Class<? extends ClusteringAlgorithm<?>> alg, KMedoidsInitialization<DBID> init, int k, RandomFactory rnd)
Constructor. -
Uses of ClusteringAlgorithm in elki.clustering
Classes in elki.clustering that implement ClusteringAlgorithm Modifier and Type Class Description class
AbstractProjectedClustering<R extends Clustering<?>>
class
BetulaLeafPreClustering
BETULA-based clustering algorithm that simply treats the leafs of the CFTree as clusters.class
CanopyPreClustering<O>
Canopy pre-clustering is a simple preprocessing step for clustering.class
CFSFDP<O>
Clustering by fast search and find of density peaks (CFSFDP) is a density-based clustering method similar to mean-shift clustering.class
Leader<O>
Leader clustering algorithm.class
NaiveMeanShiftClustering<V extends NumberVector>
Mean-shift based clustering algorithm.class
SNNClustering<O>
Shared nearest neighbor clustering. -
Uses of ClusteringAlgorithm in elki.clustering.affinitypropagation
Classes in elki.clustering.affinitypropagation that implement ClusteringAlgorithm Modifier and Type Class Description class
AffinityPropagation<O>
Cluster analysis by affinity propagation. -
Uses of ClusteringAlgorithm in elki.clustering.biclustering
Classes in elki.clustering.biclustering that implement ClusteringAlgorithm Modifier and Type Class Description class
AbstractBiclustering<M extends BiclusterModel>
Abstract class as a convenience for different biclustering approaches.class
ChengAndChurch
Cheng and Church biclustering. -
Uses of ClusteringAlgorithm in elki.clustering.correlation
Classes in elki.clustering.correlation that implement ClusteringAlgorithm Modifier and Type Class Description class
CASH
The CASH algorithm is a subspace clustering algorithm based on the Hough transform.class
COPAC
COPAC is 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
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
4C identifies local subgroups of data objects sharing a uniform correlation.class
LMCLUS
Linear manifold clustering in high dimensional spaces by stochastic search.class
ORCLUS
ORCLUS: Arbitrarily ORiented projected CLUSter generation. -
Uses of ClusteringAlgorithm in elki.clustering.dbscan
Classes in elki.clustering.dbscan that implement ClusteringAlgorithm Modifier and Type Class Description class
DBSCAN<O>
Density-Based Clustering of Applications with Noise (DBSCAN), an algorithm to find density-connected sets in a database.class
GeneralizedDBSCAN
Generalized DBSCAN, density-based clustering with noise.class
GriDBSCAN<V extends NumberVector>
Using Grid for Accelerating Density-Based Clustering.class
LSDBC<O extends NumberVector>
Locally Scaled Density Based Clustering. -
Uses of ClusteringAlgorithm in elki.clustering.dbscan.parallel
Classes in elki.clustering.dbscan.parallel that implement ClusteringAlgorithm Modifier and Type Class Description class
ParallelGeneralizedDBSCAN
Parallel version of DBSCAN clustering. -
Uses of ClusteringAlgorithm in elki.clustering.em
Classes in elki.clustering.em that implement ClusteringAlgorithm Modifier and Type Class Description class
BetulaGMM
Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.class
BetulaGMMWeighted
Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.class
EM<O,M extends MeanModel>
Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.class
KDTreeEM
Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), calculated on a kd-tree. -
Uses of ClusteringAlgorithm in elki.clustering.hierarchical.birch
Classes in elki.clustering.hierarchical.birch that implement ClusteringAlgorithm Modifier and Type Class Description class
BIRCHLeafClustering
BIRCH-based clustering algorithm that simply treats the leafs of the CFTree as clusters.class
BIRCHLloydKMeans
BIRCH-based clustering algorithm that simply treats the leafs of the CFTree as clusters. -
Uses of ClusteringAlgorithm in elki.clustering.hierarchical.extraction
Classes in elki.clustering.hierarchical.extraction that implement ClusteringAlgorithm Modifier and Type Class Description class
AbstractCutDendrogram
Abstract base class for extracting clusters from dendrograms.class
ClustersWithNoiseExtraction
Extraction of a given number of clusters with a minimum size, and noise.class
CutDendrogramByHeight
Extract a flat clustering from a full hierarchy, represented in pointer form.class
CutDendrogramByNumberOfClusters
Extract a flat clustering from a full hierarchy, represented in pointer form.class
HDBSCANHierarchyExtraction
Extraction of simplified cluster hierarchies, as proposed in HDBSCAN, and additionally also compute the GLOSH outlier scores.class
SimplifiedHierarchyExtraction
Extraction of simplified cluster hierarchies, as proposed in HDBSCAN. -
Uses of ClusteringAlgorithm in elki.clustering.kcenter
Classes in elki.clustering.kcenter that implement ClusteringAlgorithm Modifier and Type Class Description class
GreedyKCenter<O>
Greedy algorithm for k-center algorithm also known as Gonzalez clustering, or farthest-first traversal. -
Uses of ClusteringAlgorithm in elki.clustering.kmeans
Subinterfaces of ClusteringAlgorithm in elki.clustering.kmeans Modifier and Type Interface Description interface
KMeans<V extends NumberVector,M extends Model>
Some constants and options shared among kmeans family algorithms.Classes in elki.clustering.kmeans that implement ClusteringAlgorithm Modifier and Type Class Description class
AbstractKMeans<V extends NumberVector,M extends Model>
Abstract base class for k-means implementations.class
AnnulusKMeans<V extends NumberVector>
Annulus k-means algorithm.class
BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel>
Run K-Means multiple times, and keep the best run.class
BetulaLloydKMeans
BIRCH/BETULA-based clustering algorithm that simply treats the leafs of the CFTree as clusters.class
BisectingKMeans<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
CompareMeans<V extends NumberVector>
Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.class
ElkanKMeans<V extends NumberVector>
Elkan's fast k-means by exploiting the triangle inequality.class
ExponionKMeans<V extends NumberVector>
Newlings's Exponion k-means algorithm, exploiting the triangle inequality.class
FuzzyCMeans<V extends NumberVector>
Fuzzy Clustering developed by Dunn and revisited by Bezdekclass
GMeans<V extends NumberVector,M extends MeanModel>
G-Means extends K-Means and estimates the number of centers with Anderson Darling Test.
Implemented as specialization of XMeans.class
HamerlyKMeans<V extends NumberVector>
Hamerly's fast k-means by exploiting the triangle inequality.class
HartiganWongKMeans<V extends NumberVector>
Hartigan and Wong k-means clustering.class
KDTreeFilteringKMeans<V extends NumberVector>
Filtering or "blacklisting" K-means with k-d-tree acceleration.class
KDTreePruningKMeans<V extends NumberVector>
Pruning K-means with k-d-tree acceleration.class
KMeansMinusMinus<V extends NumberVector>
k-means--: A Unified Approach to Clustering and Outlier Detection.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 (seePAM
instead).class
LloydKMeans<V extends NumberVector>
The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).class
MacQueenKMeans<V extends NumberVector>
The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.class
ShallotKMeans<V extends NumberVector>
Borgelt's Shallot k-means algorithm, exploiting the triangle inequality.class
SimplifiedElkanKMeans<V extends NumberVector>
Simplified version of Elkan's k-means by exploiting the triangle inequality.class
SingleAssignmentKMeans<V extends NumberVector>
Pseudo-k-means variations, that assigns each object to the nearest center.class
SortMeans<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
XMeans<V extends NumberVector,M extends MeanModel>
X-means: Extending K-means with Efficient Estimation on the Number of Clusters.class
YinYangKMeans<V extends NumberVector>
Yin-Yang k-Means Clustering. -
Uses of ClusteringAlgorithm in elki.clustering.kmeans.parallel
Classes in elki.clustering.kmeans.parallel that implement ClusteringAlgorithm Modifier and Type Class Description class
ParallelLloydKMeans<V extends NumberVector>
Parallel implementation of k-Means clustering. -
Uses of ClusteringAlgorithm in elki.clustering.kmeans.spherical
Classes in elki.clustering.kmeans.spherical that implement ClusteringAlgorithm Modifier and Type Class Description class
EuclideanSphericalElkanKMeans<V extends NumberVector>
Elkan's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.class
EuclideanSphericalHamerlyKMeans<V extends NumberVector>
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.class
EuclideanSphericalSimplifiedElkanKMeans<V extends NumberVector>
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.class
SphericalElkanKMeans<V extends NumberVector>
Elkan's fast k-means by exploiting the triangle inequality.class
SphericalHamerlyKMeans<V extends NumberVector>
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.class
SphericalKMeans<V extends NumberVector>
The standard spherical k-means algorithm.class
SphericalSimplifiedElkanKMeans<V extends NumberVector>
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.class
SphericalSimplifiedHamerlyKMeans<V extends NumberVector>
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.class
SphericalSingleAssignmentKMeans<V extends NumberVector>
Pseudo-k-Means variations, that assigns each object to the nearest center. -
Uses of ClusteringAlgorithm in elki.clustering.kmedoids
Subinterfaces of ClusteringAlgorithm in elki.clustering.kmedoids Modifier and Type Interface Description interface
KMedoidsClustering<O>
Interface for clustering algorithms that produce medoids.Classes in elki.clustering.kmedoids that implement ClusteringAlgorithm Modifier and Type Class Description class
AlternatingKMedoids<O>
A k-medoids clustering algorithm, implemented as EM-style batch algorithm; known in literature as the "alternate" method.class
CLARA<V>
Clustering Large Applications (CLARA) is a clustering method for large data sets based on PAM, partitioning around medoids (PAM
) based on sampling.class
CLARANS<O>
CLARANS: a method for clustering objects for spatial data mining is inspired by PAM (partitioning around medoids,PAM
) and CLARA and also based on sampling.class
EagerPAM<O>
Variation of PAM that eagerly performs all swaps that yield an improvement during an iteration.class
FastCLARA<V>
Clustering Large Applications (CLARA) with theFastPAM
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
FasterCLARA<O>
Clustering Large Applications (CLARA) with theFastPAM
improvements, to increase scalability in the number of clusters.class
FasterPAM<O>
Variation of FastPAM that eagerly performs any swap that yields an improvement during an iteration.class
FastPAM<O>
FastPAM: An improved version of PAM, that is usually O(k) times faster.class
FastPAM1<O>
FastPAM1: A version of PAM that is O(k) times faster, i.e., now in O((n-k)²).class
PAM<O>
The original Partitioning Around Medoids (PAM) algorithm or k-medoids clustering, as proposed by Kaufman and Rousseeuw; a largely equivalent method was also proposed by Whitaker in the operations research domain, and is well known by the name "fast interchange" there.class
ReynoldsPAM<O>
The Partitioning Around Medoids (PAM) algorithm with some additional optimizations proposed by Reynolds et al.class
SingleAssignmentKMedoids<O>
K-medoids clustering by using the initialization only, then assigning each object to the nearest neighbor. -
Uses of ClusteringAlgorithm in elki.clustering.meta
Classes in elki.clustering.meta that implement ClusteringAlgorithm Modifier and Type Class Description class
ExternalClustering
Read an external clustering result from a file, such as produced byClusteringVectorDumper
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Uses of ClusteringAlgorithm in elki.clustering.onedimensional
Classes in elki.clustering.onedimensional that implement ClusteringAlgorithm Modifier and Type Class Description class
KNNKernelDensityMinimaClustering
Cluster one-dimensional data by splitting the data set on local minima after performing kernel density estimation. -
Uses of ClusteringAlgorithm in elki.clustering.optics
Classes in elki.clustering.optics that implement ClusteringAlgorithm Modifier and Type Class Description class
OPTICSXi
Extract clusters from OPTICS plots using the original ξ (Xi) extraction, which defines steep areas if the reachability drops below 1-ξ, respectively increases to 1+ξ, of the current value, then constructs valleys that begin with a steep down, and end with a matching steep up area. -
Uses of ClusteringAlgorithm in elki.clustering.silhouette
Classes in elki.clustering.silhouette that implement ClusteringAlgorithm Modifier and Type Class Description class
FasterMSC<O>
Fast and Eager Medoid Silhouette Clustering.class
FastMSC<O>
Fast Medoid Silhouette Clustering.class
PAMMEDSIL<O>
Clustering to optimize the Medoid Silhouette coefficient with a PAM-based swap heuristic.class
PAMSIL<O>
Clustering to optimize the Silhouette coefficient with a PAM-based swap heuristic. -
Uses of ClusteringAlgorithm in elki.clustering.subspace
Subinterfaces of ClusteringAlgorithm in elki.clustering.subspace Modifier and Type Interface Description interface
SubspaceClusteringAlgorithm<M extends SubspaceModel>
Interface for subspace clustering algorithms that use a model derived fromSubspaceModel
, that can then be post-processed for outlier detection.Classes in elki.clustering.subspace that implement ClusteringAlgorithm Modifier and Type Class Description class
CLIQUE
Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.class
DiSH
Algorithm for detecting subspace hierarchies.class
DOC
DOC is a sampling based subspace clustering algorithm.class
FastDOC
The heuristic variant of the DOC algorithm, FastDOCclass
P3C
P3C: A Robust Projected Clustering Algorithm.class
PreDeCon
PreDeCon computes clusters of subspace preference weighted connected points.class
PROCLUS
The PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces.class
SUBCLU<V extends NumberVector>
Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces. -
Uses of ClusteringAlgorithm in elki.clustering.svm
Classes in elki.clustering.svm that implement ClusteringAlgorithm Modifier and Type Class Description class
SupportVectorClustering
Support Vector Clustering works on SVDD, which tries to find the smallest sphere enclosing all objects in kernel space. -
Uses of ClusteringAlgorithm in elki.clustering.trivial
Classes in elki.clustering.trivial that implement ClusteringAlgorithm Modifier and Type Class Description class
ByLabelClustering
Pseudo clustering using labels.class
ByLabelHierarchicalClustering
Pseudo clustering using labels.class
ByLabelOrAllInOneClustering
Trivial class that will try to cluster by label, and fall back to an "all-in-one" clustering.class
ByModelClustering
Pseudo clustering using annotated models.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 ClusteringAlgorithm in elki.clustering.uncertain
Classes in elki.clustering.uncertain that implement ClusteringAlgorithm Modifier and Type Class Description class
CenterOfMassMetaClustering<C extends Clustering<?>>
Center-of-mass meta clustering reduces uncertain objects to their center of mass, then runs a vector-oriented clustering algorithm on this data set.class
CKMeans
Run k-means on the centers of each uncertain object.class
FDBSCAN
FDBSCAN is an adaption of DBSCAN for fuzzy (uncertain) objects.class
RepresentativeUncertainClustering
Representative clustering of uncertain data.class
UKMeans
Uncertain K-Means clustering, using the average deviation from the center.Fields in elki.clustering.uncertain declared as ClusteringAlgorithm Modifier and Type Field Description protected ClusteringAlgorithm<C>
CenterOfMassMetaClustering. inner
The algorithm to be wrapped and run.protected ClusteringAlgorithm<C>
CenterOfMassMetaClustering.Par. inner
Field to store the algorithm.protected ClusteringAlgorithm<?>
RepresentativeUncertainClustering. metaAlgorithm
The algorithm for meta-clustering.protected ClusteringAlgorithm<?>
RepresentativeUncertainClustering.Par. metaAlgorithm
Field to store the inner algorithm for meta-clusteringprotected ClusteringAlgorithm<?>
RepresentativeUncertainClustering.Par. samplesAlgorithm
Field to store the algorithm.protected ClusteringAlgorithm<?>
RepresentativeUncertainClustering. samplesAlgorithm
The algorithm to be wrapped and run.Constructors in elki.clustering.uncertain with parameters of type ClusteringAlgorithm Constructor Description CenterOfMassMetaClustering(ClusteringAlgorithm<C> inner)
Constructor, quite trivial.RepresentativeUncertainClustering(ClusteringDistanceSimilarity distance, ClusteringAlgorithm<?> metaAlgorithm, ClusteringAlgorithm<?> samplesAlgorithm, int numsamples, RandomFactory random, double alpha, boolean keep)
Constructor, quite trivial. -
Uses of ClusteringAlgorithm in elki.evaluation.clustering
Fields in elki.evaluation.clustering declared as ClusteringAlgorithm Modifier and Type Field Description private ClusteringAlgorithm<?>
EvaluateClustering.Par. referencealg
Reference algorithm.private ClusteringAlgorithm<?>
EvaluateClustering. referencealg
Reference algorithm.Constructors in elki.evaluation.clustering with parameters of type ClusteringAlgorithm Constructor Description EvaluateClustering(ClusteringAlgorithm<?> referencealg, boolean noiseSpecialHandling, boolean selfPairing)
Constructor. -
Uses of ClusteringAlgorithm in elki.outlier.clustering
Fields in elki.outlier.clustering declared as ClusteringAlgorithm Modifier and Type Field Description protected ClusteringAlgorithm<?>
SilhouetteOutlierDetection. clusterer
Clustering algorithm to useprotected ClusteringAlgorithm<?>
SilhouetteOutlierDetection.Par. clusterer
Clustering algorithm to use(package private) ClusteringAlgorithm<?>
NoiseAsOutliers. clustering
Clustering algorithm to use(package private) ClusteringAlgorithm<?>
NoiseAsOutliers.Par. clustering
Clustering algorithm to run.protected ClusteringAlgorithm<Clustering<MeanModel>>
CBLOF. clusteringAlgorithm
The clustering algorithm to use.protected ClusteringAlgorithm<Clustering<MeanModel>>
CBLOF.Par. clusteringAlgorithm
The clustering algorithm to use.Constructors in elki.outlier.clustering with parameters of type ClusteringAlgorithm Constructor Description CBLOF(NumberVectorDistance<? super O> distance, ClusteringAlgorithm<Clustering<MeanModel>> clusteringAlgorithm, double alpha, double beta)
Constructor.NoiseAsOutliers(ClusteringAlgorithm<?> clustering)
Constructor.SilhouetteOutlierDetection(Distance<? super O> distance, ClusteringAlgorithm<?> clusterer, NoiseHandling noiseOption)
Constructor. -
Uses of ClusteringAlgorithm in tutorial.clustering
Classes in tutorial.clustering that implement ClusteringAlgorithm Modifier and Type Class Description class
CFSFDP<O>
Tutorial code for Clustering by fast search and find of density peaks.class
SameSizeKMeans<V extends NumberVector>
K-means variation that produces equally sized clusters.
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