Uses of Class
de.lmu.ifi.dbs.elki.utilities.Priority

Packages that use Priority

Package	Description
de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.classification	Classification algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms Clustering algorithms are supposed to implement the Algorithm-Interface.
de.lmu.ifi.dbs.elki.algorithm.clustering.em	Expectation-Maximization clustering algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan	Generalized DBSCAN Generalized DBSCAN is an abstraction of the original DBSCAN idea, that allows the use of arbitrary "neighborhood" and "core point" predicates.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical	Hierarchical agglomerative clustering (HAC).
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction	Extraction of partitional clusterings from hierarchical results.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage	Linkages for hierarchical clustering.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization	Initialization strategies for k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.optics	OPTICS family of clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Axis-parallel subspace clustering algorithms The clustering algorithms in this package are instances of both, projected clustering algorithms or subspace clustering algorithms according to the classical but somewhat obsolete classification schema of clustering algorithms for axis-parallel subspaces.
de.lmu.ifi.dbs.elki.algorithm.clustering.trivial	Trivial clustering algorithms: all in one, no clusters, label clusterings These methods are mostly useful for providing a reference result in evaluation.
de.lmu.ifi.dbs.elki.algorithm.itemsetmining	Algorithms for frequent itemset mining such as APRIORI.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance	Distance-based outlier detection algorithms, such as DBOutlier and kNN.
de.lmu.ifi.dbs.elki.algorithm.outlier.lof	LOF family of outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.trivial	Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
de.lmu.ifi.dbs.elki.algorithm.projection	Data projections (see also preprocessing filters for basic projections).
de.lmu.ifi.dbs.elki.algorithm.timeseries	Algorithms for change point detection in time series.
de.lmu.ifi.dbs.elki.datasource	Data normalization (and reconstitution) of data sets
de.lmu.ifi.dbs.elki.datasource.filter.normalization.columnwise	Normalizations operating on columns / variates; where each column is treated independently.
de.lmu.ifi.dbs.elki.datasource.filter.transform	Data space transformations
de.lmu.ifi.dbs.elki.distance.distancefunction	Distance functions for use within ELKI.
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski	Minkowski space Lp norms such as the popular Euclidean and Manhattan distances.
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic	Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence, F-divergence, χ²-divergence, etc.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Kernel functions.
de.lmu.ifi.dbs.elki.index.tree.metrical.covertree	Cover-tree variations.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split	Splitting strategies of nodes in an M-Tree (and variants)
de.lmu.ifi.dbs.elki.math.statistics.dependence	Statistical measures of dependence, such as correlation
de.lmu.ifi.dbs.elki.result	Result types, representation and handling

Uses of Priority in de.lmu.ifi.dbs.elki.algorithm

Classes in de.lmu.ifi.dbs.elki.algorithm with annotations of type Priority

Modifier and Type	Class and 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	DummyAlgorithm<O extends NumberVector> Dummy algorithm, which just iterates over all points once, doing a 10NN query each.
class	KNNJoin<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry> Joins in a given spatial database to each object its k-nearest neighbors.

Uses of Priority in de.lmu.ifi.dbs.elki.algorithm.classification

Classes in de.lmu.ifi.dbs.elki.algorithm.classification with annotations of type Priority

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

Uses of Priority in de.lmu.ifi.dbs.elki.algorithm.clustering

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering with annotations of type Priority

Modifier and Type	Class and Description
class	DBSCAN<O> Density-Based Clustering of Applications with Noise (DBSCAN), an algorithm to find density-connected sets in a database.

Uses of Priority in de.lmu.ifi.dbs.elki.algorithm.clustering.em

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.em with annotations of type Priority

Modifier and Type	Class and Description
class	EM<V extends NumberVector,M extends MeanModel> Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.

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

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan with annotations of type Priority

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

Uses of Priority in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical with annotations of type Priority

Modifier and Type	Class and Description
class	AnderbergHierarchicalClustering<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 de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction with annotations of type Priority

Modifier and Type	Class and 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 de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage with annotations of type Priority

Modifier and Type	Class and 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 de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with annotations of type Priority

Modifier and Type	Class and Description
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	KMeansCompare<V extends NumberVector> Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.
class	KMeansSort<V extends NumberVector> Sort-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means (with sorting).
class	KMedoidsFastPAM<V> FastPAM: An improved version of PAM, that is usually O(k) times faster.
class	KMedoidsFastPAM1<V> FastPAM1: A version of PAM that is O(k) times faster, i.e., now in O((n-k)²).
class	KMedoidsPAM<V> The original Partitioning Around Medoids (PAM) algorithm or k-medoids clustering, as proposed by Kaufman and Rousseeuw in "Clustering by means of Medoids".

Uses of Priority in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization with annotations of type Priority

Modifier and Type	Class and Description
class	ParkInitialMeans<O> Initialization method proposed by Park and Jun.
class	RandomNormalGeneratedInitialMeans Initialize k-means by generating random vectors (normal distributed with \(N(\mu,\sigma)\) in each dimension).
class	RandomUniformGeneratedInitialMeans Initialize k-means by generating random vectors (uniform, within the value range of the data set).

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

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.optics with annotations of type Priority

Modifier and Type	Class and Description
class	OPTICSXi Extract clusters from OPTICS Plots using the original Xi extraction.

Uses of Priority in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with annotations of type Priority

Modifier and Type	Class and Description
class	P3C<V extends NumberVector> P3C: A Robust Projected Clustering Algorithm.

Uses of Priority in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial with annotations of type Priority

Modifier and Type	Class and 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 de.lmu.ifi.dbs.elki.algorithm.itemsetmining

Classes in de.lmu.ifi.dbs.elki.algorithm.itemsetmining with annotations of type Priority

Modifier and Type	Class and Description
class	FPGrowth FP-Growth is an algorithm for mining the frequent itemsets by using a compressed representation of the database called FPGrowth.FPTree.

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

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.distance with annotations of type Priority

Modifier and Type	Class and Description
class	KNNOutlier<O> Outlier Detection based on the distance of an object to its k nearest neighbor.

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

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.lof with annotations of type Priority

Modifier and Type	Class and 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 Distance/density based algorithm similar to LOF to detect outliers, but with statistical methods to achieve better result stability.

Uses of Priority in de.lmu.ifi.dbs.elki.algorithm.outlier.trivial

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.trivial with annotations of type Priority

Modifier and Type	Class and 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 de.lmu.ifi.dbs.elki.algorithm.projection

Classes in de.lmu.ifi.dbs.elki.algorithm.projection with annotations of type Priority

Modifier and Type	Class and Description
class	BarnesHutTSNE<O> tSNE using Barnes-Hut-Approximation.

Uses of Priority in de.lmu.ifi.dbs.elki.algorithm.timeseries

Classes in de.lmu.ifi.dbs.elki.algorithm.timeseries with annotations of type Priority

Modifier and Type	Class and Description
class	SigniTrendChangeDetection Signi-Trend detection algorithm applies to a single time-series.

Uses of Priority in de.lmu.ifi.dbs.elki.datasource

Classes in de.lmu.ifi.dbs.elki.datasource with annotations of type Priority

Modifier and Type	Class and Description
class	FileBasedDatabaseConnection File based database connection based on the parser to be set.

Uses of Priority in de.lmu.ifi.dbs.elki.datasource.filter.normalization.columnwise

Classes in de.lmu.ifi.dbs.elki.datasource.filter.normalization.columnwise with annotations of type Priority

Modifier and Type	Class and 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 de.lmu.ifi.dbs.elki.datasource.filter.transform

Classes in de.lmu.ifi.dbs.elki.datasource.filter.transform with annotations of type Priority

Modifier and Type	Class and 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 de.lmu.ifi.dbs.elki.distance.distancefunction

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction with annotations of type Priority

Modifier and Type	Class and Description
class	ArcCosineDistanceFunction Arcus cosine distance function for feature vectors.
class	CanberraDistanceFunction Canberra distance function, a variation of Manhattan distance.
class	ClarkDistanceFunction Clark distance function for vector spaces.
class	CosineDistanceFunction Cosine distance function for feature vectors.
class	RandomStableDistanceFunction 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 de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski with annotations of type Priority

Modifier and Type	Class and Description
class	EuclideanDistanceFunction Euclidean distance for NumberVectors.
class	LPNormDistanceFunction Lp-Norm (Minkowski norms) are a family of distances for NumberVectors.

Uses of Priority in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic with annotations of type Priority

Modifier and Type	Class and Description
class	ChiDistanceFunction χ distance function, symmetric version.
class	ChiSquaredDistanceFunction χ² distance function, symmetric version.

Uses of Priority in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

Classes in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with annotations of type Priority

Modifier and Type	Class and Description
class	LinearKernelFunction Linear Kernel function that computes a similarity between the two feature vectors x and y defined by \(x^T\cdot y\).
class	RadialBasisFunctionKernelFunction Gaussian radial basis function kernel (RBF Kernel).

Uses of Priority in de.lmu.ifi.dbs.elki.index.tree.metrical.covertree

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.covertree with annotations of type Priority

Modifier and Type	Class and Description
class	SimplifiedCoverTree<O> Simplified cover tree data structure (in-memory).

Uses of Priority in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split with annotations of type Priority

Modifier and Type	Class and 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 de.lmu.ifi.dbs.elki.math.statistics.dependence

Classes in de.lmu.ifi.dbs.elki.math.statistics.dependence with annotations of type Priority

Modifier and Type	Class and Description
class	HSMDependenceMeasure Compute the "interestingness" of dimension connections using the hough transformation.
class	SURFINGDependenceMeasure Compute the similarity of dimensions using the SURFING score.

Uses of Priority in de.lmu.ifi.dbs.elki.result

Classes in de.lmu.ifi.dbs.elki.result with annotations of type Priority

Modifier and Type	Class and Description
class	AutomaticVisualization Handler to process and visualize a Result.
class	ResultWriter Result handler that feeds the data into a TextWriter.