## Uses of Classde.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.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
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>
• ### 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
class  FlexibleBetaLinkage
Flexible-beta linkage as proposed by Lance and Williams.
class  GroupAverageLinkage
class  SingleLinkage
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.