All Classes Interface Summary Class Summary Enum Summary
| Class |
Description |
| AbstractBiclustering<M extends BiclusterModel> |
Abstract class as a convenience for different biclustering approaches.
|
| AbstractCFKMeansInitialization |
Abstract base class for CF k-means initializations.
|
| AbstractCFKMeansInitialization.Par |
Parameterization class.
|
| AbstractCutDendrogram |
Abstract base class for extracting clusters from dendrograms.
|
| AbstractCutDendrogram.Par |
Parameterization class.
|
| AbstractHDBSCAN<O> |
Abstract base class for HDBSCAN variations.
|
| AbstractHDBSCAN.HDBSCANAdapter |
Class for processing the HDBSCAN G_mpts graph.
|
| AbstractHDBSCAN.HeapMSTCollector |
Class for collecting the minimum spanning tree edges into a heap.
|
| AbstractKMeans<V extends elki.data.NumberVector,M extends Model> |
Abstract base class for k-means implementations.
|
| AbstractKMeans.Instance |
Inner instance for a run, for better encapsulation, that encapsulates the
standard flow of most (but not all) k-means variations.
|
| AbstractKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| AbstractKMeansInitialization |
Abstract base class for common k-means initializations.
|
| AbstractKMeansInitialization.Par |
Parameterization class.
|
| AbstractKMeansQualityMeasure<O extends elki.data.NumberVector> |
Base class for evaluating clusterings by information criteria (such as AIC or
BIC).
|
| AbstractOPTICS<O> |
The OPTICS algorithm for density-based hierarchical clustering.
|
| AbstractProjectedClustering<R extends Clustering<?>> |
Abstract superclass for projected clustering algorithms, like PROCLUS
and ORCLUS. |
| AbstractProjectedClustering.Par |
Parameterization class.
|
| AbstractRangeQueryNeighborPredicate<O,M,N> |
Abstract local model neighborhood predicate.
|
| AbstractRangeQueryNeighborPredicate.Instance<N,M> |
Instance for a particular data set.
|
| AffinityPropagation<O> |
Cluster analysis by affinity propagation.
|
| AffinityPropagationInitialization<O> |
Initialization methods for affinity propagation.
|
| AFKMC2 |
AFK-MC² initialization
|
| AFKMC2.Instance |
Abstract instance implementing the weight handling.
|
| AFKMC2.Par |
Parameterization class.
|
| AGNES<O> |
Hierarchical Agglomerative Clustering (HAC) or Agglomerative Nesting (AGNES)
is a classic hierarchical clustering algorithm.
|
| AGNES.Instance |
Main worker instance of AGNES.
|
| AkaikeInformationCriterion |
Akaike Information Criterion (AIC).
|
| AkaikeInformationCriterionXMeans |
Akaike Information Criterion (AIC).
|
| AlternateRefinement<O> |
Meta-Initialization for k-medoids by performing one (or many) k-means-style
iteration.
|
| AlternateRefinement.Par<O> |
Parameterization class.
|
| AlternatingKMedoids<O> |
A k-medoids clustering algorithm, implemented as EM-style batch algorithm;
known in literature as the "alternate" method.
|
| AlternatingKMedoids.Par<V> |
Parameterization class.
|
| Anderberg<O> |
This is a modification of the classic AGNES algorithm for hierarchical
clustering using a nearest-neighbor heuristic for acceleration.
|
| Anderberg.Instance |
Main worker instance of Anderberg's algorithm.
|
| AnnulusKMeans<V extends elki.data.NumberVector> |
Annulus k-means algorithm.
|
| AnnulusKMeans.Instance |
Inner instance, storing state for a single data set.
|
| AnnulusKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| AsClusterFeature |
Get the clustering feature representation.
|
| Assignment |
Point assignment.
|
| AverageInterclusterDistance |
Average intercluster distance.
|
| AverageInterclusterDistance |
Average intercluster distance.
|
| AverageInterclusterDistance.Par |
Parameterization class.
|
| AverageInterclusterDistance.Par |
Parameterization class.
|
| AverageIntraclusterDistance |
Average intracluster distance.
|
| AverageIntraclusterDistance |
Average intracluster distance.
|
| AverageIntraclusterDistance.Par |
Parameterization class.
|
| AverageIntraclusterDistance.Par |
Parameterization class.
|
| BayesianInformationCriterion |
Bayesian Information Criterion (BIC), also known as Schwarz criterion (SBC,
SBIC) for the use with evaluating k-means results.
|
| BayesianInformationCriterionXMeans |
Bayesian Information Criterion (BIC), also known as Schwarz criterion (SBC,
SBIC) for the use with evaluating k-means results.
|
| BayesianInformationCriterionZhao |
Different version of the BIC criterion.
|
| BCubed |
BCubed measures for cluster evaluation.
|
| BestOfMultipleKMeans<V extends elki.data.NumberVector,M extends MeanModel> |
Run K-Means multiple times, and keep the best run.
|
| BetulaClusterModel |
Models usable in Betula EM clustering.
|
| BetulaClusterModelFactory<M extends BetulaClusterModel> |
Factory for initializing the EM models.
|
| BetulaDiagonalGaussianModelFactory |
Factory for EM with multivariate gaussian models using diagonal matrixes.
|
| BetulaDiagonalGaussianModelFactory.Par |
Parameterization class
|
| BetulaGMM |
Clustering by expectation maximization (EM-Algorithm), also known as Gaussian
Mixture Modeling (GMM), with optional MAP regularization.
|
| BetulaGMM.Par |
Parameterizer
|
| BetulaGMMWeighted |
Clustering by expectation maximization (EM-Algorithm), also known as Gaussian
Mixture Modeling (GMM), with optional MAP regularization.
|
| BetulaGMMWeighted.Par |
Parameterizer
|
| BetulaLeafPreClustering |
BETULA-based clustering algorithm that simply treats the leafs of the CFTree
as clusters.
|
| BetulaLeafPreClustering.Par |
Parameterization class.
|
| BetulaLloydKMeans |
BIRCH/BETULA-based clustering algorithm that simply treats the leafs of the
CFTree as clusters.
|
| BetulaLloydKMeans.Par |
Parameterization class.
|
| BetulaMultivariateGaussianModelFactory |
Factory for EM with multivariate gaussian models using diagonal matrixes.
|
| BetulaMultivariateGaussianModelFactory.Par |
Parameterization class
|
| BetulaSphericalGaussianModelFactory |
Factory for EM with multivariate gaussian models using a single variance.
|
| BetulaSphericalGaussianModelFactory.Par |
Parameterization class
|
| BiclusterModel |
Wrapper class to provide the basic properties of a Bicluster.
|
| BiclusterWithInversionsModel |
This code was factored out of the Bicluster class, since not all biclusters
have inverted rows.
|
| BIRCHAbsorptionCriterion |
BIRCH absorption criterion.
|
| BIRCHAverageInterclusterDistance |
Average intercluster distance.
|
| BIRCHAverageInterclusterDistance.Par |
Parameterization class.
|
| BIRCHAverageIntraclusterDistance |
Average intracluster distance.
|
| BIRCHAverageIntraclusterDistance.Par |
Parameterization class.
|
| BIRCHCF |
Clustering Feature of BIRCH, only for comparison
|
| BIRCHCF.Factory |
Factory for making cluster features.
|
| BIRCHCF.Factory.Par |
Parameterization class.
|
| BIRCHDistance |
Distance function for BIRCH clustering.
|
| BIRCHKMeansPlusPlus |
K-Means++-like initialization for BIRCH k-means; this cannot be used to
initialize regular k-means, use KMeansPlusPlus instead. |
| BIRCHKMeansPlusPlus.Par |
Parameterization class.
|
| BIRCHLeafClustering |
BIRCH-based clustering algorithm that simply treats the leafs of the CFTree
as clusters.
|
| BIRCHLeafClustering.Par |
Parameterization class.
|
| BIRCHLloydKMeans |
BIRCH-based clustering algorithm that simply treats the leafs of the CFTree
as clusters.
|
| BIRCHLloydKMeans.Par |
Parameterization class.
|
| BIRCHRadiusDistance |
Average Radius (R) criterion.
|
| BIRCHRadiusDistance.Par |
Parameterization class
|
| BIRCHVarianceIncreaseDistance |
Variance increase distance.
|
| BIRCHVarianceIncreaseDistance.Par |
Parameterization class.
|
| BisectingKMeans<V extends elki.data.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.
|
| Border |
Border point assignment.
|
| BUILD<O> |
PAM initialization for k-means (and of course, for PAM).
|
| BUILD.Par<V> |
Parameterization class.
|
| ByLabelClustering |
Pseudo clustering using labels.
|
| ByLabelClustering.Par |
Parameterization class.
|
| ByLabelHierarchicalClustering |
Pseudo clustering using labels.
|
| ByLabelOrAllInOneClustering |
Trivial class that will try to cluster by label, and fall back to an
"all-in-one" clustering.
|
| CanopyPreClustering<O> |
Canopy pre-clustering is a simple preprocessing step for clustering.
|
| CentroidEuclideanDistance |
Centroid Euclidean distance.
|
| CentroidEuclideanDistance |
Centroid Euclidean distance.
|
| CentroidEuclideanDistance.Par |
Parameterization class.
|
| CentroidEuclideanDistance.Par |
Parameterization class.
|
| CentroidLinkage |
Centroid linkage — Unweighted Pair-Group Method using Centroids
(UPGMC).
|
| CentroidLinkage.Par |
Class parameterizer.
|
| CentroidManhattanDistance |
Centroid Manhattan Distance
|
| CentroidManhattanDistance |
Centroid Manhattan Distance
|
| CentroidManhattanDistance.Par |
Parameterization class.
|
| CentroidManhattanDistance.Par |
Parameterization class.
|
| CFDistance |
Distance function for BIRCH clustering.
|
| CFDistanceMatrix |
Cluster feature distance matrix, used for clustering.
|
| CFInitWeight |
Initialization weight function for k-means initialization with BETULA.
|
| CFKPlusPlusLeaves |
K-Means++-like initialization for BETULA k-means, treating the leaf
clustering features as a flat list, and called "leaves" in the publication.
|
| CFKPlusPlusLeaves.Par |
Parameterization class.
|
| CFKPlusPlusTree |
Initialize K-means by following tree paths weighted by their variance
contribution.
|
| CFKPlusPlusTree.Par |
Parameterization class.
|
| CFKPlusPlusTrunk |
Trunk strategy for initializing k-means with BETULA: only the nodes up to a
particular level are considered for k-means++ style initialization.
|
| CFKPlusPlusTrunk.Par |
Parameterization class.
|
| CFNode<L extends ClusterFeature> |
Interface for TreeNode
|
| CFRandomlyChosen |
Initialize K-means by randomly choosing k existing elements as initial
cluster centers for Clustering Features.
|
| CFRandomlyChosen.Par |
Parameterization class.
|
| CFSFDP<O> |
Clustering by fast search and find of density peaks (CFSFDP) is a
density-based clustering method similar to mean-shift clustering.
|
| CFSFDP.Par<O> |
Parameterizer
|
| CFTree |
Partial implementation of the CFTree as used by BIRCH.
|
| CFTree<L extends ClusterFeature> |
Partial implementation of the CFTree as used by BIRCH and BETULA.
|
| CFTree.Factory |
CF-Tree Factory.
|
| CFTree.Factory<L extends ClusterFeature> |
CF-Tree Factory.
|
| CFTree.Factory.Par |
Parameterization class for CFTrees.
|
| CFTree.Factory.Par<L extends ClusterFeature> |
Parameterization class for CFTrees.
|
| CFTree.LeafIterator |
Iterator over leaf nodes.
|
| CFTree.LeafIterator<L extends ClusterFeature> |
Iterator over leaf nodes.
|
| CFTree.Threshold |
Threshold update strategy.
|
| CFWeightedRandomlyChosen |
Initialize K-means by randomly choosing k existing elements as initial
cluster centers for Clustering Features.
|
| CFWeightedRandomlyChosen.Par |
Parameterization class.
|
| ChengAndChurch |
Cheng and Church biclustering.
|
| ChengAndChurch.BiclusterCandidate |
Bicluster candidate.
|
| ChengAndChurch.CellVisitor |
Visitor pattern for processing cells.
|
| CIndex<O> |
Compute the C-index of a data set.
|
| CIndex.Par<O> |
Parameterization 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. |
| CLARA.CachedDistanceQuery<V> |
Cached distance query.
|
| CLARA.Par<V> |
Parameterization 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. |
| CLARANS.Assignment |
Assignment state.
|
| CLARANS.Par<V> |
Parameterization class.
|
| CLINK<O> |
CLINK algorithm for complete linkage.
|
| CLINK.Par<O> |
Parameterization class.
|
| CLIQUE |
Implementation of the CLIQUE algorithm, a grid-based algorithm to identify
dense clusters in subspaces of maximum dimensionality.
|
| CLIQUE.Par |
Parameterization class.
|
| CLIQUESubspace |
Represents a subspace of the original data space in the CLIQUE algorithm.
|
| CLIQUEUnit |
Represents a unit in the CLIQUE algorithm.
|
| Cluster<M extends Model> |
Generic cluster class, that may or not have hierarchical information.
|
| ClusterContingencyTable |
Class storing the contingency table and related data on two clusterings.
|
| ClusterDensityMergeHistory |
Hierarchical clustering merge list, with additional coredists information.
|
| ClusterDistanceMatrix |
Shared code for algorithms that work on a pairwise cluster distance matrix.
|
| ClusterFeature |
Interface for basic ClusteringFeature functions
|
| ClusterFeature.Factory<F extends ClusterFeature> |
Cluster feature factory
|
| Clustering<M extends Model> |
Result class for clusterings.
|
| ClusteringAdjustedRandIndexSimilarity |
Measure the similarity of clusters via the Adjusted Rand Index.
|
| ClusteringAdjustedRandIndexSimilarity.Par |
Parameterization class.
|
| 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. |
| ClusteringAlgorithmUtil |
Utility functionality for writing clustering algorithms.
|
| ClusteringBCubedF1Similarity |
Measure the similarity of clusters via the BCubed F1 Index.
|
| ClusteringBCubedF1Similarity.Par |
Parameterization class.
|
| ClusteringDistanceSimilarity |
Distance and similarity measure for clusterings.
|
| ClusteringFeature |
Clustering Feature of BIRCH
|
| ClusteringFowlkesMallowsSimilarity |
Measure the similarity of clusters via the Fowlkes-Mallows Index.
|
| ClusteringFowlkesMallowsSimilarity.Par |
Parameterization class.
|
| ClusteringRandIndexSimilarity |
Measure the similarity of clusters via the Rand Index.
|
| ClusteringRandIndexSimilarity.Par |
Parameterization class.
|
| ClusteringVectorDumper |
Output a clustering result in a simple and compact ascii format:
whitespace separated cluster indexes
|
| ClusteringVectorDumper.Par |
Parameterization class.
|
| ClusteringVectorParser |
|
| ClusteringVectorParser.Par |
Parameterization class.
|
| ClusterIntersectionSimilarity |
Measure the similarity of clusters via the intersection size.
|
| ClusterIntersectionSimilarity.Par |
Parameterization class.
|
| ClusterJaccardSimilarity |
Measure the similarity of clusters via the Jaccard coefficient.
|
| ClusterJaccardSimilarity.Par |
Parameterization class.
|
| ClusterMergeHistory |
Merge history representing a hierarchical clustering.
|
| ClusterMergeHistoryBuilder |
Class to help building a pointer hierarchy.
|
| ClusterModel |
Generic cluster model.
|
| ClusterOrder |
Class to store the result of an ordering clustering algorithm such as OPTICS.
|
| ClusterPairSegmentAnalysis |
Evaluate clustering results by building segments for their pairs: shared
pairs and differences.
|
| ClusterPrototypeMergeHistory |
Cluster merge history with additional cluster prototypes (for HACAM,
MedoidLinkage, and MiniMax clustering)
|
| ClusterRadius |
Evaluate a clustering by the (weighted) cluster radius.
|
| ClusterRadius.Par |
Parameterization class.
|
| ClustersWithNoiseExtraction |
Extraction of a given number of clusters with a minimum size, and noise.
|
| ClustersWithNoiseExtraction.Par |
Parameterization class.
|
| CompareMeans<V extends elki.data.NumberVector> |
Compare-Means: Accelerated k-means by exploiting the triangle inequality and
pairwise distances of means to prune candidate means.
|
| CompareMeans.Instance |
Inner instance, storing state for a single data set.
|
| CompareMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| CompleteLinkage |
Complete-linkage ("maximum linkage") clustering method.
|
| CompleteLinkage.Par |
Class parameterizer.
|
| ConcordantPairsGammaTau |
Compute the Gamma Criterion of a data set.
|
| ConcordantPairsGammaTau.Par |
Parameterization 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.
|
| COPAC.Par |
Parameterization class.
|
| COPAC.Settings |
Class to wrap the COPAC settings.
|
| COPACNeighborPredicate |
COPAC neighborhood predicate.
|
| COPACNeighborPredicate.COPACModel |
Model used by COPAC for core point property.
|
| COPACNeighborPredicate.Instance |
Instance for a particular data set.
|
| COPACNeighborPredicate.Par |
Parameterization class.
|
| Core |
Core point assignment.
|
| CoreObjectsModel |
Cluster model using "core" objects.
|
| CorePredicate<T> |
Predicate for GeneralizedDBSCAN to evaluate whether a point is a core point
or not.
|
| CorePredicate.Instance<T> |
Instance for a particular data set.
|
| CorrelationClusterOrder |
Cluster order entry for correlation-based OPTICS variants.
|
| CorrelationModel |
Cluster model using a filtered PCA result and an centroid.
|
| CutDendrogramByHeight |
Extract a flat clustering from a full hierarchy, represented in pointer form.
|
| CutDendrogramByHeight.Par |
Parameterization class.
|
| CutDendrogramByHeightExtractor |
Extract clusters from a hierarchical clustering, during the evaluation phase.
|
| CutDendrogramByHeightExtractor.Par |
Parameterization class.
|
| CutDendrogramByNumberOfClusters |
Extract a flat clustering from a full hierarchy, represented in pointer form.
|
| CutDendrogramByNumberOfClusters.Par |
Parameterization class.
|
| CutDendrogramByNumberOfClustersExtractor |
Extract clusters from a hierarchical clustering, during the evaluation phase.
|
| CutDendrogramByNumberOfClustersExtractor.Par |
Parameterization class.
|
| DaviesBouldinIndex |
Compute the Davies-Bouldin index of a data set.
|
| DaviesBouldinIndex.Par |
Parameterization class.
|
| DBCV<O> |
Compute the Density-Based Clustering Validation Index.
|
| DBCV.Par<O> |
Parameterization class.
|
| DBSCAN<O> |
Density-Based Clustering of Applications with Noise (DBSCAN), an algorithm to
find density-connected sets in a database.
|
| DBSCAN.Par<O> |
Parameterization class.
|
| DendrogramModel |
Model for dendrograms, provides the height of this subtree.
|
| DiagonalGaussianModel |
Simpler model for a single Gaussian cluster, without covariances.
|
| DiagonalGaussianModelFactory |
Factory for EM with multivariate gaussian models using diagonal matrixes.
|
| DiameterCriterion |
Average Radius (R) criterion.
|
| DiameterCriterion.Par |
Parameterization class
|
| DimensionModel |
Cluster model additionally providing a cluster dimensionality.
|
| DistanceBasedInitializationWithMedian<O> |
Distance based initialization.
|
| DOC |
DOC is a sampling based subspace clustering algorithm.
|
| DOC.Par |
Parameterization class.
|
| EagerPAM<O> |
Variation of PAM that eagerly performs all swaps that yield an improvement
during an iteration.
|
| EagerPAM.Instance |
Instance for a single dataset.
|
| EagerPAM.Par<O> |
Parameterization class.
|
| EditDistance |
Edit distance measures.
|
| ElkanKMeans<V extends elki.data.NumberVector> |
Elkan's fast k-means by exploiting the triangle inequality.
|
| ElkanKMeans.Instance |
Inner instance, storing state for a single data set.
|
| ElkanKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| EM<O,M extends MeanModel> |
Clustering by expectation maximization (EM-Algorithm), also known as Gaussian
Mixture Modeling (GMM), with optional MAP regularization.
|
| EM.Par<O,M extends MeanModel> |
Parameterization class.
|
| EMClusterModel<O,M extends Model> |
Models usable in EM clustering.
|
| EMClusterModelFactory<O,M extends Model> |
Factory for initializing the EM models.
|
| EMModel |
Cluster model of an EM cluster, providing a mean and a full covariance
Matrix.
|
| Entropy |
Entropy based measures, implemented using natural logarithms.
|
| EpsilonNeighborPredicate<O> |
The default DBSCAN and OPTICS neighbor predicate, using an
epsilon-neighborhood.
|
| EpsilonNeighborPredicate.Instance |
Instance for a particular data set.
|
| 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.
|
| ERiC.Par |
Parameterization class.
|
| ERiC.Settings |
Class to wrap the ERiC settings.
|
| ERiCNeighborPredicate |
ERiC neighborhood predicate.
|
| ERiCNeighborPredicate.Par |
Parameterization class.
|
| EuclideanDistanceCriterion |
Distance criterion.
|
| EuclideanSphericalElkanKMeans<V extends elki.data.NumberVector> |
Elkan's fast k-means by exploiting the triangle inequality in the
corresponding Euclidean space.
|
| EuclideanSphericalElkanKMeans.Instance |
Inner instance, storing state for a single data set.
|
| EuclideanSphericalElkanKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| EuclideanSphericalHamerlyKMeans<V extends elki.data.NumberVector> |
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting
the triangle inequality in the corresponding Euclidean space.
|
| EuclideanSphericalHamerlyKMeans.Instance |
Inner instance, storing state for a single data set.
|
| EuclideanSphericalHamerlyKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| EuclideanSphericalSimplifiedElkanKMeans<V extends elki.data.NumberVector> |
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting
the triangle inequality in the corresponding Euclidean space.
|
| EuclideanSphericalSimplifiedElkanKMeans.Instance |
Inner instance, storing state for a single data set.
|
| EuclideanSphericalSimplifiedElkanKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| EvaluateClustering |
Evaluate a clustering result by comparing it to an existing cluster label.
|
| EvaluateClustering.Par |
Parameterization class.
|
| EvaluateClustering.ScoreResult |
Result object for outlier score judgements.
|
| ExponionKMeans<V extends elki.data.NumberVector> |
Newlings's Exponion k-means algorithm, exploiting the triangle inequality.
|
| ExponionKMeans.Instance |
Inner instance, storing state for a single data set.
|
| ExponionKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| ExternalClustering |
|
| ExternalClustering.Par |
Parameterization class
|
| FarthestPoints<O> |
K-Means initialization by repeatedly choosing the farthest point (by the
minimum distance to earlier points).
|
| FarthestPoints.Par<O> |
Parameterization class.
|
| FarthestSumPoints<O> |
K-Means initialization by repeatedly choosing the farthest point (by the
sum of distances to previous objects).
|
| FarthestSumPoints.Par<V> |
Parameterization class.
|
| FastCLARA<V> |
Clustering Large Applications (CLARA) with the FastPAM
improvements, to increase scalability in the number of clusters. |
| FastCLARA.Par<V> |
Parameterization 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.
|
| FastCLARANS.Assignment |
Assignment state.
|
| FastCLARANS.Par<V> |
Parameterization class.
|
| FastDOC |
The heuristic variant of the DOC algorithm, FastDOC
|
| FastDOC.Par |
Parameterization class.
|
| FasterCLARA<O> |
Clustering Large Applications (CLARA) with the FastPAM
improvements, to increase scalability in the number of clusters. |
| FasterCLARA.Par<V> |
Parameterization class.
|
| FasterMSC<O> |
Fast and Eager Medoid Silhouette Clustering.
|
| FasterMSC.Par<O> |
Parameterization class.
|
| FasterPAM<O> |
Variation of FastPAM that eagerly performs any swap that yields an
improvement during an iteration.
|
| FasterPAM.Instance |
Instance for a single dataset.
|
| FasterPAM.Par<O> |
Parameterization class.
|
| FastMSC<O> |
Fast Medoid Silhouette Clustering.
|
| FastMSC.Par<O> |
Parameterization class.
|
| FastMSC.Record |
Data stored per point.
|
| FastOPTICS<V extends elki.data.NumberVector> |
FastOPTICS algorithm (Fast approximation of OPTICS)
|
| FastPAM<O> |
FastPAM: An improved version of PAM, that is usually O(k) times faster.
|
| FastPAM.Instance |
Instance for a single dataset.
|
| FastPAM.Par<V> |
Parameterization class.
|
| FastPAM1<O> |
FastPAM1: A version of PAM that is O(k) times faster, i.e., now in O((n-k)²).
|
| FastPAM1.Instance |
Instance for a single dataset.
|
| FastPAM1.Par<V> |
Parameterization class.
|
| FirstK<O> |
Initialize K-means by using the first k objects as initial means.
|
| FirstK.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| FlexibleBetaLinkage |
Flexible-beta linkage as proposed by Lance and Williams.
|
| FlexibleBetaLinkage.Par |
Parameterization class.
|
| FourC |
4C identifies local subgroups of data objects sharing a uniform correlation.
|
| FourC.Par |
Parameterization class.
|
| FourC.Settings |
Class wrapping the 4C parameter settings.
|
| FourC.Settings.Par |
Parameterization class for 4C settings.
|
| FourCCorePredicate |
The 4C core point predicate.
|
| FourCCorePredicate.Instance |
Instance for a particular data set.
|
| FourCCorePredicate.Par |
Parameterization class
|
| FourCNeighborPredicate |
4C identifies local subgroups of data objects sharing a uniform correlation.
|
| FourCNeighborPredicate.Instance |
Instance for a particular data set.
|
| FourCNeighborPredicate.Par |
Parameterization class.
|
| FuzzyCMeans<V extends elki.data.NumberVector> |
Fuzzy Clustering developed by Dunn and revisited by Bezdek
|
| FuzzyCMeans.Par |
Parameterization class.
|
| GeneralizedDBSCAN |
Generalized DBSCAN, density-based clustering with noise.
|
| GeneralizedDBSCAN.Instance<T> |
Instance for a particular data set.
|
| GeneralizedDBSCAN.Par |
Parameterization class
|
| GeneralizedOPTICS |
A trivial generalization of OPTICS that is not restricted to numerical
distances, and serves as a base for several other algorithms (HiCO, HiSC).
|
| GeneralizedOPTICS.Instance<R> |
Instance for processing a single data set.
|
| GeometricLinkage |
Geometric linkages, in addition to the combination with
Lance-Williams-Equations, these linkages can also be computed by aggregating
data points (for vector data only).
|
| GMeans<V extends elki.data.NumberVector,M extends MeanModel> |
G-Means extends K-Means and estimates the number of centers with Anderson
Darling Test.
Implemented as specialization of XMeans.
|
| GMeans.Par<V extends elki.data.NumberVector,M extends MeanModel> |
Parameterization class.
|
| GreedyG<O> |
Initialization method for k-medoids that combines the Greedy (PAM
BUILD) with "alternate" refinement steps. |
| GreedyG.Par<V> |
Parameterization class.
|
| GreedyKCenter<O> |
Greedy algorithm for k-center algorithm also known as Gonzalez clustering,
or farthest-first traversal.
|
| GreedyKCenter.Par<O> |
Parameterization class
|
| GriDBSCAN<V extends elki.data.NumberVector> |
Using Grid for Accelerating Density-Based Clustering.
|
| GriDBSCAN.Instance<V extends elki.data.NumberVector> |
Instance, for a single run.
|
| GroupAverageLinkage |
Group-average linkage clustering method (UPGMA).
|
| GroupAverageLinkage.Par |
Class parameterizer.
|
| HACAM<O> |
Hierarchical Agglomerative Clustering Around Medoids (HACAM) is a
hierarchical clustering method that merges the clusters with the smallest
distance to the medoid of the union.
|
| HACAM.Instance |
Main worker instance of AGNES.
|
| HACAM.Variant |
Variants of the HACAM method.
|
| HamerlyKMeans<V extends elki.data.NumberVector> |
Hamerly's fast k-means by exploiting the triangle inequality.
|
| HamerlyKMeans.Instance |
Inner instance, storing state for a single data set.
|
| HamerlyKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| HartiganWongKMeans<V extends elki.data.NumberVector> |
Hartigan and Wong k-means clustering.
|
| HartiganWongKMeans.Instance |
Instance for a particular data set.
|
| HartiganWongKMeans.Parameterizer<V extends elki.data.NumberVector> |
Parameterization class.
|
| HDBSCANHierarchyExtraction |
Extraction of simplified cluster hierarchies, as proposed in HDBSCAN,
and additionally also compute the GLOSH outlier scores.
|
| HDBSCANHierarchyExtraction.Par |
Parameterization class.
|
| HDBSCANHierarchyExtraction.TempCluster |
Temporary cluster.
|
| HDBSCANHierarchyExtractionEvaluator |
Extract clusters from a hierarchical clustering, during the evaluation phase.
|
| HDBSCANHierarchyExtractionEvaluator.Par |
Parameterization class.
|
| HDBSCANLinearMemory<O> |
Linear memory implementation of HDBSCAN clustering.
|
| HiCO |
Implementation of the HiCO algorithm, an algorithm for detecting hierarchies
of correlation clusters.
|
| HiCO.Par |
Parameterization class.
|
| HierarchicalClusteringAlgorithm |
Interface for hierarchical clustering algorithms.
|
| HiSC |
Implementation of the HiSC algorithm, an algorithm for detecting hierarchies
of subspace clusters.
|
| HiSC.Par |
Parameterization class.
|
| InterclusterWeight |
Initialization via n2 * D2²(cf1, cf2), which supposedly is closes to the idea
of k-means++ initialization.
|
| KDTreeEM |
Clustering by expectation maximization (EM-Algorithm), also known as Gaussian
Mixture Modeling (GMM), calculated on a kd-tree.
|
| KDTreeEM.KDTree |
KDTree class with the statistics needed for EM clustering.
|
| KDTreeEM.Par |
Parameterization class.
|
| KDTreeFilteringKMeans<V extends elki.data.NumberVector> |
Filtering or "blacklisting" K-means with k-d-tree acceleration.
|
| KDTreeFilteringKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| KDTreePruningKMeans<V extends elki.data.NumberVector> |
Pruning K-means with k-d-tree acceleration.
|
| KDTreePruningKMeans.KDNode |
Node of the k-d-tree used internally.
|
| KDTreePruningKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| KDTreePruningKMeans.Split |
Splitting strategies for constructing the k-d-tree.
|
| KMC2 |
K-MC² initialization
|
| KMC2.Instance |
Abstract instance implementing the weight handling.
|
| KMC2.Par |
Parameterization class.
|
| KMeans<V extends elki.data.NumberVector,M extends Model> |
Some constants and options shared among kmeans family algorithms.
|
| KMeansInitialization |
Interface for initializing K-Means
|
| KMeansMinusMinus<V extends elki.data.NumberVector> |
k-means--: A Unified Approach to Clustering and Outlier Detection.
|
| KMeansMinusMinus.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| KMeansModel |
Trivial subclass of the MeanModel that indicates the clustering to be
produced by k-means (so the Voronoi cell visualization is sensible). |
| KMeansPlusPlus<O> |
K-Means++ initialization for k-means.
|
| KMeansPlusPlus.Instance<T> |
Abstract instance implementing the weight handling.
|
| KMeansPlusPlus.MedoidsInstance |
Instance for k-medoids.
|
| KMeansPlusPlus.NumberVectorInstance |
Instance for k-means, number vector based.
|
| KMeansPlusPlus.Par<V> |
Parameterization class.
|
| KMeansProcessor<V extends elki.data.NumberVector> |
Parallel k-means implementation.
|
| KMeansProcessor.Instance<V extends elki.data.NumberVector> |
Instance to process part of the data set, for a single iteration.
|
| KMeansQualityMeasure<O extends elki.data.NumberVector> |
Interface for computing the quality of a K-Means clustering.
|
| KMediansLloyd<V extends elki.data.NumberVector> |
k-medians clustering algorithm, but using Lloyd-style bulk iterations instead
of the more complicated approach suggested by Kaufman and Rousseeuw (see
PAM instead). |
| KMediansLloyd.Instance |
Inner instance, storing state for a single data set.
|
| KMediansLloyd.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| KMedoidsClustering<O> |
Interface for clustering algorithms that produce medoids.
|
| KMedoidsInitialization<O> |
Interface for initializing K-Medoids.
|
| KMedoidsKMedoidsInitialization<O> |
Initialize k-medoids with k-medoids, for methods such as PAMSIL.
This could also be used to initialize, e.g., PAM with CLARA.
|
| KMedoidsKMedoidsInitialization.Par<O> |
Parameterization class.
|
| KNNKernelDensityMinimaClustering |
Cluster one-dimensional data by splitting the data set on local minima after
performing kernel density estimation.
|
| KNNKernelDensityMinimaClustering.Mode |
Estimation mode.
|
| KNNKernelDensityMinimaClustering.Par |
Parameterization class.
|
| LAB<O> |
Linear approximative BUILD (LAB) initialization for FastPAM (and k-means).
|
| LAB.Par<V> |
Parameterization class.
|
| Leader<O> |
Leader clustering algorithm.
|
| LinearEquationModel |
Cluster model containing a linear equation system for the cluster.
|
| LinearMemoryNNChain<O extends elki.data.NumberVector> |
NNchain clustering algorithm with linear memory, for particular linkages
(that can be aggregated) and numerical vector data only.
|
| LinearMemoryNNChain.Instance<O extends elki.data.NumberVector> |
Main worker instance of NNChain.
|
| Linkage |
Abstract interface for implementing a new linkage method into hierarchical
clustering.
|
| LloydKMeans<V extends elki.data.NumberVector> |
The standard k-means algorithm, using bulk iterations and commonly attributed
to Lloyd and Forgy (independently).
|
| LloydKMeans.Instance |
Inner instance, storing state for a single data set.
|
| LloydKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| LMCLUS |
Linear manifold clustering in high dimensional spaces by stochastic search.
|
| LMCLUS.Par |
Parameterization class
|
| LMCLUS.Separation |
Class to represent a linear manifold separation
|
| LogClusterSizes |
This class will log simple statistics on the clusters detected, such as the
cluster sizes and the number of clusters.
|
| LSDBC<O extends elki.data.NumberVector> |
Locally Scaled Density Based Clustering.
|
| LSDBC.Par<O extends elki.data.NumberVector> |
Parameterization class
|
| MacQueenKMeans<V extends elki.data.NumberVector> |
The original k-means algorithm, using MacQueen style incremental updates;
making this effectively an "online" (streaming) algorithm.
|
| MacQueenKMeans.Instance |
Inner instance, storing state for a single data set.
|
| MacQueenKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| MaximumMatchingAccuracy |
Calculates the accuracy of a clustering based on the maximum set matching
found by the Hungarian algorithm.
|
| MeanModel |
Cluster model that stores a mean for the cluster.
|
| MedianLinkage |
Median-linkage — weighted pair group method using centroids (WPGMC).
|
| MedianLinkage.Par |
Class parameterizer.
|
| MedoidLinkage<O> |
Medoid linkage uses the distance of medoids as criterion.
|
| MedoidLinkage.Instance |
Main worker instance of AGNES.
|
| MedoidModel |
Cluster model that stores a mean for the cluster.
|
| MiniMax<O> |
Minimax Linkage clustering.
|
| MiniMax.Instance |
Main worker instance of MiniMax.
|
| MiniMaxAnderberg<O> |
This is a modification of the classic MiniMax algorithm for hierarchical
clustering using a nearest-neighbor heuristic for acceleration.
|
| MiniMaxAnderberg.Instance |
Main worker instance of MiniMax.
|
| MiniMaxNNChain<O> |
MiniMax hierarchical clustering using the NNchain algorithm.
|
| MiniMaxNNChain.Instance |
Main worker instance of MiniMaxNNChain.
|
| MinimumVarianceLinkage |
Minimum increase in variance (MIVAR) linkage.
|
| MinimumVarianceLinkage.Par |
Class parameterizer.
|
| MinPtsCorePredicate |
|
| MinPtsCorePredicate.Instance |
Instance for a particular data set.
|
| MinPtsCorePredicate.Par |
Parameterization class
|
| Model |
Base interface for Model classes.
|
| ModelUtil |
Utility classes for dealing with cluster models.
|
| MultiBorder |
Multiple border point assignment.
|
| MultivariateGaussianModel |
Model for a single multivariate Gaussian cluster with arbitrary rotation.
|
| MultivariateGaussianModelFactory |
Factory for EM with multivariate Gaussian models (with covariance; also known
as Gaussian Mixture Modeling, GMM).
|
| NaiveMeanShiftClustering<V extends elki.data.NumberVector> |
Mean-shift based clustering algorithm.
|
| NeighborPredicate<T> |
Get the neighbors of an object
|
| NeighborPredicate.Instance<T> |
Instance for a particular data set.
|
| NNChain<O> |
NNchain clustering algorithm.
|
| NNChain.Instance |
Main worker instance of NNChain.
|
| NoiseHandling |
Options for handling noise in internal measures.
|
| OPTICSHeap<O> |
The OPTICS algorithm for density-based hierarchical clustering.
|
| OPTICSHeap.Par<O> |
Parameterization class.
|
| OPTICSHeapEntry |
Entry in the priority heap.
|
| OPTICSList<O> |
The OPTICS algorithm for density-based hierarchical clustering.
|
| OPTICSList.Par<O> |
Parameterization class.
|
| OPTICSModel |
Model for an OPTICS cluster
|
| OPTICSToHierarchical |
Convert a OPTICS ClusterOrder to a hierarchical clustering.
|
| OPTICSToHierarchical.Par |
Parameterization class
|
| OPTICSTypeAlgorithm |
Interface for OPTICS type algorithms, that can be analyzed by OPTICS Xi etc.
|
| 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.
|
| OPTICSXi.ClusterHierarchyBuilder |
Class to build the hierarchical clustering result structure.
|
| OPTICSXi.Par |
Parameterization class.
|
| OPTICSXi.SteepArea |
Data structure to represent a steep-down-area for the xi method.
|
| OPTICSXi.SteepAreaResult |
Result containing the xi-steep areas.
|
| OPTICSXi.SteepDownArea |
Data structure to represent a steep-down-area for the xi method.
|
| OPTICSXi.SteepScanPosition |
Position when scanning for steep areas
|
| OPTICSXi.SteepUpArea |
Data structure to represent a steep-down-area for the xi method.
|
| ORCLUS |
ORCLUS: Arbitrarily ORiented projected CLUSter generation.
|
| ORCLUS.ORCLUSCluster |
Encapsulates the attributes of a cluster.
|
| ORCLUS.Par |
Parameterization class.
|
| ORCLUS.ProjectedEnergy |
Encapsulates the projected energy for a cluster.
|
| Ostrovsky |
Ostrovsky initial means, a variant of k-means++ that is expected to give
slightly better results on average, but only works for k-means and not for,
e.g., PAM (k-medoids).
|
| Ostrovsky.Par |
Parameterization class.
|
| P3C |
P3C: A Robust Projected Clustering Algorithm.
|
| P3C.ClusterCandidate |
This class is used to represent potential clusters.
|
| P3C.Par |
Parameterization class.
|
| P3C.Signature |
P3C Cluster signature.
|
| PairCounting |
Pair-counting measures, with support for "noise" clusters and self-pairing
support.
|
| PairSetsIndex |
The Pair Sets Index calculates an index based on the maximum matching of
relative cluster sizes by the Hungarian algorithm.
|
| 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.
|
| PAM.Instance |
Instance for a single dataset.
|
| PAM.Par<O> |
Parameterization class.
|
| PAMMEDSIL<O> |
Clustering to optimize the Medoid Silhouette coefficient with a PAM-based
swap heuristic.
|
| PAMMEDSIL.Instance |
Instance for a single dataset.
|
| PAMMEDSIL.Par<O> |
Parameterization class.
|
| PAMSIL<O> |
Clustering to optimize the Silhouette coefficient with a PAM-based swap
heuristic.
|
| PAMSIL.Instance |
Instance for a single dataset.
|
| PAMSIL.Par<O> |
Parameterization class.
|
| ParallelGeneralizedDBSCAN |
Parallel version of DBSCAN clustering.
|
| ParallelGeneralizedDBSCAN.Instance<T> |
Instance for a particular data set.
|
| ParallelGeneralizedDBSCAN.Par |
Parameterization class
|
| ParallelLloydKMeans<V extends elki.data.NumberVector> |
Parallel implementation of k-Means clustering.
|
| ParkJun<O> |
Initialization method proposed by Park and Jun.
|
| ParkJun.Par<V> |
Parameterization class.
|
| PBMIndex |
Compute the PBM index of a clustering
|
| PBMIndex.Par |
Parameterization class.
|
| PreDeCon |
PreDeCon computes clusters of subspace preference weighted connected points.
|
| PreDeCon.Par |
Parameterization class.
|
| PreDeCon.Settings |
Class containing all the PreDeCon settings.
|
| PreDeCon.Settings.Par |
Parameterization class.
|
| PreDeConCorePredicate |
The PreDeCon core point predicate -- having at least minpts. neighbors, and a
maximum preference dimensionality of lambda.
|
| PreDeConCorePredicate.Instance |
Instance for a particular data set.
|
| PreDeConCorePredicate.Par |
Parameterization class
|
| PreDeConNeighborPredicate |
Neighborhood predicate used by PreDeCon.
|
| PreDeConNeighborPredicate.Instance |
Instance for a particular data set.
|
| PreDeConNeighborPredicate.Par |
Parameterization class.
|
| PreDeConNeighborPredicate.PreDeConModel |
Model used by PreDeCon for core point property.
|
| Predefined |
Run k-means with prespecified initial means.
|
| Predefined.Par |
Parameterization class.
|
| PROCLUS |
The PROCLUS algorithm, an algorithm to find subspace clusters in high
dimensional spaces.
|
| PROCLUS.DoubleIntInt |
Simple triple.
|
| PROCLUS.Par |
Parameterization class.
|
| PROCLUS.PROCLUSCluster |
Encapsulates the attributes of a cluster.
|
| PrototypeDendrogramModel |
Hierarchical cluster, with prototype.
|
| PrototypeModel<V> |
Cluster model that stores a prototype for each cluster.
|
| RadiusCriterion |
Average Radius (R) criterion.
|
| RadiusCriterion.Par |
Parameterization class
|
| RadiusDistance |
Average Radius (R) criterion.
|
| RadiusDistance.Par |
Parameterization class
|
| RandomlyChosen<O> |
Initialize K-means by randomly choosing k existing elements as initial
cluster centers.
|
| RandomlyChosen.Par<V> |
Parameterization class.
|
| RandomNormalGenerated |
Initialize k-means by generating random vectors (normal distributed
with \(N(\mu,\sigma)\) in each dimension).
|
| RandomNormalGenerated.Par |
Parameterization class.
|
| RandomProjectedNeighborsAndDensities |
Random Projections used for computing neighbors and density estimates.
|
| RandomProjectedNeighborsAndDensities.Par |
Parameterization class.
|
| RandomUniformGenerated |
Initialize k-means by generating random vectors (uniform, within the value
range of the data set).
|
| RandomUniformGenerated.Par |
Parameterization class.
|
| ReferenceClustering<M extends Model> |
Reference clustering.
|
| ReynoldsPAM<O> |
The Partitioning Around Medoids (PAM) algorithm with some additional
optimizations proposed by Reynolds et al.
|
| ReynoldsPAM.Instance |
Instance for a single dataset.
|
| ReynoldsPAM.Par<V> |
Parameterization class.
|
| SampleKMeans<V extends elki.data.NumberVector> |
Initialize k-means by running k-means on a sample of the data set only.
|
| Segment |
A segment represents a set of pairs that share the same clustering
properties.
|
| Segments |
Creates segments of two or more clusterings.
|
| SetMatchingPurity |
Set matching purity measures.
|
| ShallotKMeans<V extends elki.data.NumberVector> |
Borgelt's Shallot k-means algorithm, exploiting the triangle inequality.
|
| ShallotKMeans.Instance |
Inner instance, storing state for a single data set.
|
| ShallotKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| Silhouette<O> |
Compute the silhouette of a data set.
|
| Silhouette.Par<O> |
Parameterization class.
|
| SimilarityBasedInitializationWithMedian<O> |
Similarity based initialization.
|
| SimilarityNeighborPredicate<O> |
The DBSCAN neighbor predicate for a Similarity, using all
neighbors with a minimum similarity.
|
| SimilarityNeighborPredicate.Instance |
Instance for a particular data set.
|
| SimplePrototypeModel<V> |
Cluster model that stores a prototype for each cluster.
|
| SimplifiedElkanKMeans<V extends elki.data.NumberVector> |
Simplified version of Elkan's k-means by exploiting the triangle inequality.
|
| SimplifiedElkanKMeans.Instance |
Inner instance, storing state for a single data set.
|
| SimplifiedElkanKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| SimplifiedHierarchyExtraction |
Extraction of simplified cluster hierarchies, as proposed in HDBSCAN.
|
| SimplifiedHierarchyExtraction.Par |
Parameterization class.
|
| SimplifiedHierarchyExtraction.TempCluster |
Temporary cluster.
|
| SimplifiedHierarchyExtractionEvaluator |
Extract clusters from a hierarchical clustering, during the evaluation phase.
|
| SimplifiedHierarchyExtractionEvaluator.Par |
Parameterization class.
|
| SimplifiedSilhouette |
Compute the simplified silhouette of a data set.
|
| SimplifiedSilhouette.Par |
Parameterization class.
|
| SingleAssignmentKMeans<V extends elki.data.NumberVector> |
Pseudo-k-means variations, that assigns each object to the nearest center.
|
| SingleAssignmentKMeans.Instance |
Inner instance, storing state for a single data set.
|
| SingleAssignmentKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| SingleAssignmentKMedoids<O> |
K-medoids clustering by using the initialization only, then assigning each
object to the nearest neighbor.
|
| SingleAssignmentKMedoids.Instance |
Instance for a single dataset.
|
| SingleAssignmentKMedoids.Par<O> |
Parameterization class.
|
| SingleLinkage |
Single-linkage ("minimum") clustering method.
|
| SingleLinkage.Par |
Class parameterizer.
|
| SLINK<O> |
Implementation of the efficient Single-Link Algorithm SLINK of R.
|
| SLINK.Par<O> |
Parameterization class.
|
| SLINKHDBSCANLinearMemory<O> |
Linear memory implementation of HDBSCAN clustering based on SLINK.
|
| SNNClustering<O> |
Shared nearest neighbor clustering.
|
| SortMeans<V extends elki.data.NumberVector> |
Sort-Means: Accelerated k-means by exploiting the triangle inequality and
pairwise distances of means to prune candidate means (with sorting).
|
| SortMeans.Instance |
Inner instance, storing state for a single data set.
|
| SortMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| SphericalAFKMC2 |
Spherical K-Means++ initialization with markov chains.
|
| SphericalAFKMC2.Instance |
Abstract instance implementing the weight handling.
|
| SphericalAFKMC2.Par |
Parameterization class.
|
| SphericalElkanKMeans<V extends elki.data.NumberVector> |
Elkan's fast k-means by exploiting the triangle inequality.
|
| SphericalElkanKMeans.Instance |
Inner instance, storing state for a single data set.
|
| SphericalElkanKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| SphericalGaussianModel |
Simple spherical Gaussian cluster (scaled identity matrixes).
|
| SphericalGaussianModelFactory |
Factory for EM with multivariate gaussian models using a single variance.
|
| SphericalHamerlyKMeans<V extends elki.data.NumberVector> |
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting
the triangle inequality.
|
| SphericalHamerlyKMeans.Instance |
Inner instance, storing state for a single data set.
|
| SphericalHamerlyKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| SphericalKMeans<V extends elki.data.NumberVector> |
The standard spherical k-means algorithm.
|
| SphericalKMeans.Instance |
Instance for a particular data set.
|
| SphericalKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| SphericalKMeansPlusPlus<O> |
Spherical K-Means++ initialization for k-means.
|
| SphericalKMeansPlusPlus.Instance |
Abstract instance implementing the weight handling.
|
| SphericalKMeansPlusPlus.Par<V> |
Parameterization class.
|
| SphericalSimplifiedElkanKMeans<V extends elki.data.NumberVector> |
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting
the triangle inequality.
|
| SphericalSimplifiedElkanKMeans.Instance |
Inner instance, storing state for a single data set.
|
| SphericalSimplifiedElkanKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| SphericalSimplifiedHamerlyKMeans<V extends elki.data.NumberVector> |
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting
the triangle inequality.
|
| SphericalSimplifiedHamerlyKMeans.Instance |
Inner instance, storing state for a single data set.
|
| SphericalSimplifiedHamerlyKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| SphericalSingleAssignmentKMeans<V extends elki.data.NumberVector> |
Pseudo-k-Means variations, that assigns each object to the nearest center.
|
| SphericalSingleAssignmentKMeans.Instance |
Instance for a particular data set.
|
| SphericalSingleAssignmentKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| SquaredErrors |
Evaluate a clustering by reporting the squared errors (SSE, SSQ), as used by
k-means.
|
| SquaredErrors.Par |
Parameterization class.
|
| SquaredEuclideanWeight |
Use the squared Euclidean distance only for distance measurement.
|
| SUBCLU<V extends elki.data.NumberVector> |
Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily
shaped and positioned clusters in subspaces.
|
| SUBCLU.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| Subspace |
Represents a subspace of the original data space.
|
| SubspaceClusteringAlgorithm<M extends SubspaceModel> |
Interface for subspace clustering algorithms that use a model derived from
SubspaceModel, that can then be post-processed for outlier detection. |
| SubspaceModel |
Model for Subspace Clusters.
|
| TextbookMultivariateGaussianModel |
Numerically problematic implementation of the GMM model, using the textbook
algorithm.
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| TextbookMultivariateGaussianModelFactory |
Factory for EM with multivariate Gaussian model, using the textbook
algorithm.
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| TextbookSphericalGaussianModel |
Simple spherical Gaussian cluster.
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| TextbookSphericalGaussianModelFactory |
Factory for EM with multivariate gaussian models using a single variance.
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| TrivialAllInOne |
Trivial pseudo-clustering that just considers all points to be one big
cluster.
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| TrivialAllNoise |
Trivial pseudo-clustering that just considers all points to be noise.
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| TwoPassMultivariateGaussianModel |
Model for a single Gaussian cluster, using two-passes for slightly better
numerics.
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| TwoPassMultivariateGaussianModelFactory |
Factory for EM with multivariate Gaussian models (with covariance; also known
as Gaussian Mixture Modeling, GMM).
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| VarianceIncreaseDistance |
Variance increase distance.
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| VarianceIncreaseDistance |
Variance increase distance.
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| VarianceIncreaseDistance.Par |
Parameterization class.
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| VarianceIncreaseDistance.Par |
Parameterization class.
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| VarianceRatioCriterion |
Compute the Variance Ratio Criterion of a data set, also known as
Calinski-Harabasz index.
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| VarianceRatioCriterion.Par |
Parameterization class.
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| VarianceWeight |
Variance-based weighting scheme for k-means clustering with BETULA.
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| VIIFeature |
Clustering Feature of stable BIRCH with a single variance per cluster
feature
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| VIIFeature.Factory |
Factory for making cluster features.
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| VIIFeature.Factory.Par |
Parameterization class.
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| VVIFeature |
Clustering Feature of stable BIRCH with variance per dimension
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| VVIFeature.Factory |
Factory for making cluster features.
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| VVIFeature.Factory.Par |
Parameterization class.
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| VVVFeature |
Clustering Feature of stable BIRCH with covariance instead of variance
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| VVVFeature.Factory |
Factory for making cluster features.
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| VVVFeature.Factory.Par |
Parameterization class.
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| WardLinkage |
Ward's method clustering method.
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| WardLinkage.Par |
Class parameterizer.
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| WeightedAverageLinkage |
Weighted average linkage clustering method (WPGMA).
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| WeightedAverageLinkage.Par |
Class parameterizer.
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| WithinClusterMeanDistance |
Class for computing the average overall distance.
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| WithinClusterVariance |
Class for computing the variance in a clustering result (sum-of-squares).
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| XMeans<V extends elki.data.NumberVector,M extends MeanModel> |
X-means: Extending K-means with Efficient Estimation on the Number of
Clusters.
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| YinYangKMeans<V extends elki.data.NumberVector> |
Yin-Yang k-Means Clustering.
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| YinYangKMeans.Instance |
Instance for a particular data set.
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| YinYangKMeans.Par<V extends elki.data.NumberVector> |
Parameterization class.
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