Uses of Class
elki.utilities.Priority
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Packages that use Priority Package Description elki.algorithm Miscellaneous algorithms.elki.classification Classification algorithms.elki.clustering Clustering algorithms.elki.clustering.dbscan DBSCAN and its generalizations.elki.clustering.em Expectation-Maximization clustering algorithm for Gaussian Mixture Modeling (GMM).elki.clustering.hierarchical Hierarchical agglomerative clustering (HAC).elki.clustering.hierarchical.extraction Extraction of partitional clusterings from hierarchical results.elki.clustering.hierarchical.linkage Linkages for hierarchical clustering.elki.clustering.kmeans K-means clustering and variations.elki.clustering.kmeans.initialization Initialization strategies for k-means.elki.clustering.kmeans.spherical Spherical k-means clustering and variations.elki.clustering.kmedoids K-medoids clustering (PAM).elki.clustering.kmedoids.initialization elki.clustering.optics OPTICS family of clustering algorithms.elki.clustering.silhouette Silhouette clustering algorithms.elki.clustering.subspace Axis-parallel subspace clustering algorithms.elki.clustering.trivial Trivial clustering algorithms: all in one, no clusters, label clusterings.elki.database.ids.integer Integer-based DBID implementation -- do not use directly - always useDBIDUtil
.elki.database.query Database queries - computing distances, neighbors, similarities - API and general documentation.elki.datasource Data normalization (and reconstitution) of data sets.elki.datasource.filter.normalization.columnwise Normalizations operating on columns / variates; where each column is treated independently.elki.datasource.filter.transform Data space transformations.elki.distance Distance functions for use within ELKI.elki.distance.minkowski Minkowski space Lp norms such as the popular Euclidean and Manhattan distances.elki.distance.probabilistic Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence, F-divergence, χ²-divergence, etc.elki.index.tree.betula.distance Distance functions for BETULA and BIRCH.elki.index.tree.betula.features Different variants of Betula and BIRCH cluster features.elki.index.tree.metrical.covertree Cover-tree variations.elki.index.tree.metrical.mtreevariants.strategies.split Splitting strategies of nodes in an M-tree (and variants).elki.index.tree.spatial.kd K-d-tree and variants.elki.itemsetmining Algorithms for frequent itemset mining such as APRIORI.elki.math.statistics.dependence Statistical measures of dependence, such as correlation.elki.outlier.distance Distance-based outlier detection algorithms, such as DBOutlier and kNN.elki.outlier.lof LOF family of outlier detection algorithms.elki.outlier.trivial Trivial outlier detection algorithms: no outliers, all outliers, label outliers.elki.projection Data projections (see also preprocessing filters for basic projections).elki.result Result types, representation and handling.elki.similarity.kernel Kernel functions.elki.timeseries Algorithms for change point detection in time series. -
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Uses of Priority in elki.algorithm
Classes in elki.algorithm with annotations of type Priority Modifier and Type Class Description class
DependencyDerivator<V extends NumberVector>
Dependency derivator computes quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA.class
KNNJoin
Joins in a given spatial database to each object its k-nearest neighbors. -
Uses of Priority in elki.classification
Classes in elki.classification with annotations of type Priority Modifier and Type Class Description class
KNNClassifier<O>
KNNClassifier classifies instances based on the class distribution among the k nearest neighbors in a database. -
Uses of Priority in elki.clustering
Classes in elki.clustering with annotations of type Priority Modifier and Type Class Description class
BetulaLeafPreClustering
BETULA-based clustering algorithm that simply treats the leafs of the CFTree as clusters. -
Uses of Priority in elki.clustering.dbscan
Classes in elki.clustering.dbscan with annotations of type Priority 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
LSDBC<O extends NumberVector>
Locally Scaled Density Based Clustering. -
Uses of Priority in elki.clustering.em
Classes in elki.clustering.em with annotations of type Priority Modifier and Type Class Description 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. -
Uses of Priority in elki.clustering.hierarchical
Classes in elki.clustering.hierarchical with annotations of type Priority Modifier and Type Class Description class
Anderberg<O>
This is a modification of the classic AGNES algorithm for hierarchical clustering using a nearest-neighbor heuristic for acceleration.class
MiniMaxAnderberg<O>
This is a modification of the classic MiniMax algorithm for hierarchical clustering using a nearest-neighbor heuristic for acceleration.class
SLINK<O>
Implementation of the efficient Single-Link Algorithm SLINK of R. -
Uses of Priority in elki.clustering.hierarchical.extraction
Classes in elki.clustering.hierarchical.extraction with annotations of type Priority Modifier and Type Class Description class
ClustersWithNoiseExtraction
Extraction of a given number of clusters with a minimum size, and noise.class
SimplifiedHierarchyExtraction
Extraction of simplified cluster hierarchies, as proposed in HDBSCAN. -
Uses of Priority in elki.clustering.hierarchical.linkage
Classes in elki.clustering.hierarchical.linkage with annotations of type Priority Modifier and Type Class Description class
CompleteLinkage
Complete-linkage ("maximum linkage") clustering method.class
FlexibleBetaLinkage
Flexible-beta linkage as proposed by Lance and Williams.class
GroupAverageLinkage
Group-average linkage clustering method (UPGMA).class
SingleLinkage
Single-linkage ("minimum") clustering method.class
WardLinkage
Ward's method clustering method.class
WeightedAverageLinkage
Weighted average linkage clustering method (WPGMA). -
Uses of Priority in elki.clustering.kmeans
Classes in elki.clustering.kmeans with annotations of type Priority Modifier and Type Class Description class
ExponionKMeans<V extends NumberVector>
Newlings's Exponion k-means algorithm, exploiting the triangle inequality.class
ShallotKMeans<V extends NumberVector>
Borgelt's Shallot k-means algorithm, exploiting the triangle inequality. -
Uses of Priority in elki.clustering.kmeans.initialization
Classes in elki.clustering.kmeans.initialization with annotations of type Priority Modifier and Type Class Description class
RandomNormalGenerated
Initialize k-means by generating random vectors (normal distributed with \(N(\mu,\sigma)\) in each dimension).class
RandomUniformGenerated
Initialize k-means by generating random vectors (uniform, within the value range of the data set). -
Uses of Priority in elki.clustering.kmeans.spherical
Classes in elki.clustering.kmeans.spherical with annotations of type Priority 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. -
Uses of Priority in elki.clustering.kmedoids
Classes in elki.clustering.kmedoids with annotations of type Priority 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
EagerPAM<O>
Variation of PAM that eagerly performs all swaps that yield an improvement during an iteration.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
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
SingleAssignmentKMedoids<O>
K-medoids clustering by using the initialization only, then assigning each object to the nearest neighbor. -
Uses of Priority in elki.clustering.kmedoids.initialization
Classes in elki.clustering.kmedoids.initialization with annotations of type Priority Modifier and Type Class Description class
ParkJun<O>
Initialization method proposed by Park and Jun. -
Uses of Priority in elki.clustering.optics
Classes in elki.clustering.optics with annotations of type Priority 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 Priority in elki.clustering.silhouette
Classes in elki.clustering.silhouette with annotations of type Priority Modifier and Type Class Description class
FasterMSC<O>
Fast and Eager Medoid Silhouette Clustering. -
Uses of Priority in elki.clustering.subspace
Classes in elki.clustering.subspace with annotations of type Priority Modifier and Type Class Description class
P3C
P3C: A Robust Projected Clustering Algorithm. -
Uses of Priority in elki.clustering.trivial
Classes in elki.clustering.trivial with annotations of type Priority 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 Priority in elki.database.ids.integer
Classes in elki.database.ids.integer with annotations of type Priority Modifier and Type Class Description class
TrivialDBIDFactory
Trivial DBID management, that never reuses IDs and just gives them out in sequence. -
Uses of Priority in elki.database.query
Classes in elki.database.query with annotations of type Priority Modifier and Type Class Description class
DisableQueryOptimizer
Dummy implementation to disable automatic optimization. -
Uses of Priority in elki.datasource
Classes in elki.datasource with annotations of type Priority Modifier and Type Class Description class
FileBasedDatabaseConnection
File based database connection based on the parser to be set. -
Uses of Priority in elki.datasource.filter.normalization.columnwise
Classes in elki.datasource.filter.normalization.columnwise with annotations of type Priority Modifier and Type Class Description class
AttributeWiseMinMaxNormalization<V extends NumberVector>
Class to perform and undo a normalization on real vectors with respect to a given minimum and maximum in each dimension.class
AttributeWiseVarianceNormalization<V extends NumberVector>
Class to perform and undo a normalization on real vectors with respect to given mean and standard deviation in each dimension. -
Uses of Priority in elki.datasource.filter.transform
Classes in elki.datasource.filter.transform with annotations of type Priority Modifier and Type Class Description class
FastMultidimensionalScalingTransform<I,O extends NumberVector>
Rescale the data set using multidimensional scaling, MDS.class
GlobalPrincipalComponentAnalysisTransform<O extends NumberVector>
Apply Principal Component Analysis (PCA) to the data set. -
Uses of Priority in elki.distance
Classes in elki.distance with annotations of type Priority Modifier and Type Class Description class
ArcCosineDistance
Arcus cosine distance function for feature vectors.class
CanberraDistance
Canberra distance function, a variation of Manhattan distance.class
ClarkDistance
Clark distance function for vector spaces.class
CosineDistance
Cosine distance function for feature vectors.class
RandomStableDistance
This is a dummy distance providing random values (obviously not metrical), useful mostly for unit tests and baseline evaluations: obviously this distance provides no benefit whatsoever. -
Uses of Priority in elki.distance.minkowski
Classes in elki.distance.minkowski with annotations of type Priority Modifier and Type Class Description class
EuclideanDistance
Euclidean distance forNumberVector
s.class
LPNormDistance
Lp-Norm (Minkowski norms) are a family of distances forNumberVector
s. -
Uses of Priority in elki.distance.probabilistic
Classes in elki.distance.probabilistic with annotations of type Priority Modifier and Type Class Description class
ChiDistance
χ distance function, symmetric version.class
ChiSquaredDistance
χ² distance function, symmetric version. -
Uses of Priority in elki.index.tree.betula.distance
Classes in elki.index.tree.betula.distance with annotations of type Priority Modifier and Type Class Description class
BIRCHAverageInterclusterDistance
Average intercluster distance.class
BIRCHAverageIntraclusterDistance
Average intracluster distance.class
BIRCHRadiusDistance
Average Radius (R) criterion.class
BIRCHVarianceIncreaseDistance
Variance increase distance. -
Uses of Priority in elki.index.tree.betula.features
Classes in elki.index.tree.betula.features with annotations of type Priority Modifier and Type Class Description static class
BIRCHCF.Factory
Factory for making cluster features. -
Uses of Priority in elki.index.tree.metrical.covertree
Classes in elki.index.tree.metrical.covertree with annotations of type Priority Modifier and Type Class Description class
CoverTree<O>
Cover tree data structure (in-memory). -
Uses of Priority in elki.index.tree.metrical.mtreevariants.strategies.split
Classes in elki.index.tree.metrical.mtreevariants.strategies.split with annotations of type Priority Modifier and Type Class Description class
MLBDistSplit<E extends MTreeEntry,N extends AbstractMTreeNode<?,N,E>>
Encapsulates the required methods for a split of a node in an M-Tree.class
MSTSplit<E extends MTreeEntry,N extends AbstractMTreeNode<?,N,E>>
Splitting algorithm using the minimum spanning tree (MST), as proposed by the Slim-Tree variant. -
Uses of Priority in elki.index.tree.spatial.kd
Classes in elki.index.tree.spatial.kd with annotations of type Priority Modifier and Type Class Description class
MinimalisticMemoryKDTree<O extends NumberVector>
Simple implementation of a static in-memory K-D-tree.class
SmallMemoryKDTree<O extends NumberVector>
Simple implementation of a static in-memory K-D-tree. -
Uses of Priority in elki.itemsetmining
Classes in elki.itemsetmining with annotations of type Priority Modifier and Type Class Description class
FPGrowth
FP-Growth is an algorithm for mining the frequent itemsets by using a compressed representation of the database calledFPGrowth.FPTree
. -
Uses of Priority in elki.math.statistics.dependence
Classes in elki.math.statistics.dependence with annotations of type Priority Modifier and Type Class Description class
DCor
Distance correlation.class
HoughSpaceMeasure
HSM: Compute the "interestingness" of dimension connections using the Hough transformation.class
PearsonCorrelationDependence
Pearson product-moment correlation coefficient.class
SURFINGDependence
Compute the similarity of dimensions using the SURFING score. -
Uses of Priority in elki.outlier.distance
Classes in elki.outlier.distance with annotations of type Priority Modifier and Type Class Description class
KNNOutlier<O>
Outlier Detection based on the distance of an object to its k nearest neighbor. -
Uses of Priority in elki.outlier.lof
Classes in elki.outlier.lof with annotations of type Priority Modifier and Type Class Description class
LOF<O>
Algorithm to compute density-based local outlier factors in a database based on a specified parameter-lof.k
.class
LoOP<O>
LoOP: Local Outlier Probabilities -
Uses of Priority in elki.outlier.trivial
Classes in elki.outlier.trivial with annotations of type Priority Modifier and Type Class Description class
ByLabelOutlier
Trivial algorithm that marks outliers by their label.class
TrivialAllOutlier
Trivial method that claims all objects to be outliers.class
TrivialAverageCoordinateOutlier
Trivial method that takes the average of all dimensions (for one-dimensional data that is just the actual value!)class
TrivialNoOutlier
Trivial method that claims to find no outliers. -
Uses of Priority in elki.projection
Classes in elki.projection with annotations of type Priority Modifier and Type Class Description class
BarnesHutTSNE<O>
t-SNE using Barnes-Hut-Approximation. -
Uses of Priority in elki.result
Classes in elki.result with annotations of type Priority Modifier and Type Class Description class
AutomaticVisualization
Handler to process and visualize a Result.class
ResultWriter
Result handler that feeds the data into a TextWriter. -
Uses of Priority in elki.similarity.kernel
Classes in elki.similarity.kernel with annotations of type Priority Modifier and Type Class Description class
LinearKernel
Linear Kernel function that computes a similarity between the two feature vectors x and y defined by \(x^T\cdot y\).class
RadialBasisFunctionKernel
Gaussian radial basis function kernel (RBF Kernel). -
Uses of Priority in elki.timeseries
Classes in elki.timeseries with annotations of type Priority Modifier and Type Class Description class
SigniTrendChangeDetection
Signi-Trend detection algorithm applies to a single time-series.
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