Uses of Class
de.lmu.ifi.dbs.elki.utilities.documentation.References

Packages that use References

Package	Description
de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
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.hierarchical	Hierarchical agglomerative clustering (HAC).
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch	BIRCH clustering.
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.kmeans.quality	Quality measures for k-Means results.
de.lmu.ifi.dbs.elki.algorithm.clustering.optics	OPTICS family of clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest	Association rule interestingness measures.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance	Distance-based outlier detection algorithms, such as DBOutlier and kNN.
de.lmu.ifi.dbs.elki.algorithm.timeseries	Algorithms for change point detection in time series.
de.lmu.ifi.dbs.elki.data.uncertain	Uncertain data objects.
de.lmu.ifi.dbs.elki.distance.distancefunction	Distance functions for use within ELKI.
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.index.idistance	iDistance is a distance based indexing technique, using a reference points embedding.
de.lmu.ifi.dbs.elki.index.lsh.hashfamilies	Hash function families for LSH
de.lmu.ifi.dbs.elki.math	Mathematical operations and utilities used throughout the framework
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing
de.lmu.ifi.dbs.elki.math.statistics	Statistical tests and methods
de.lmu.ifi.dbs.elki.math.statistics.dependence	Statistical measures of dependence, such as correlation
de.lmu.ifi.dbs.elki.math.statistics.distribution	Standard distributions, with random generation functionalities
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality	Methods for estimating the intrinsic dimensionality.
de.lmu.ifi.dbs.elki.math.statistics.tests	Statistical tests
de.lmu.ifi.dbs.elki.utilities.scaling.outlier	Scaling of outlier scores, that require a statistical analysis of the occurring values

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

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

Modifier and Type	Class and Description
class	KNNDistancesSampler<O> Provides an order of the kNN-distances for all objects within the database.

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

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

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 References in de.lmu.ifi.dbs.elki.algorithm.clustering.em

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

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 References in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical

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

Modifier and Type	Class and Description
class	AGNES<O> Hierarchical Agglomerative Clustering (HAC) or Agglomerative Nesting (AGNES) is a classic hierarchical clustering algorithm.
class	MiniMax<O> Minimax Linkage clustering.
class	MiniMaxNNChain<O> MiniMax hierarchical clustering using the NNchain algorithm.
class	NNChain<O> NNchain clustering algorithm.

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

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

Modifier and Type	Class and Description
class	BIRCHLeafClustering BIRCH-based clustering algorithm that simply treats the leafs of the CFTree as clusters.
class	CFTree Partial implementation of the CFTree as used by BIRCH.

Uses of References 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 References

Modifier and Type	Class and Description
class	CompleteLinkage Complete-linkage ("maximum linkage") clustering method.
class	MinimumVarianceLinkage Minimum increase in variance (MIVAR) linkage.
class	WardLinkage Ward's method clustering method.

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

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

Modifier and Type	Class and Description
class	CLARA<V> Clustering Large Applications (CLARA) is a clustering method for large data sets based on PAM, partitioning around medoids (KMedoidsPAM) based on sampling.
class	KMeansAnnulus<V extends NumberVector> Annulus k-means algorithm.
class	KMeansLloyd<V extends NumberVector> The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).
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".
class	KMedoidsPark<V> A k-medoids clustering algorithm, implemented as EM-style bulk algorithm.

Uses of References 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 References

Modifier and Type	Class and Description
class	OstrovskyInitialMeans<O> 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).
class	PAMInitialMeans<O> PAM initialization for k-means (and of course, for PAM).
class	RandomlyChosenInitialMeans<O> Initialize K-means by randomly choosing k existing elements as initial cluster centers.

Uses of References in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality

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

Modifier and Type	Class and Description
class	AkaikeInformationCriterion Akaike Information Criterion (AIC).

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

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

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

Uses of References in de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest

Classes in de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest with annotations of type References

Modifier and Type	Class and Description
class	GiniIndex Gini-index based interestingness measure, using the weighted squared conditional probabilities compared to the non-conditional priors.
class	Jaccard Jaccard interestingness measure: \[\tfrac{\text{support}(A \cup B)}{\text{support}(A \cap B)} =\tfrac{P(A \cap B)}{P(A)+P(B)-P(A \cap B)} =\tfrac{P(A \cap B)}{P(A \cup B)}\] Reference: P.

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

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

Modifier and Type	Class and Description
class	KNNSOS<O> kNN-based adaption of Stochastic Outlier Selection.

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

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

Modifier and Type	Class and Description
class	OfflineChangePointDetectionAlgorithm Off-line change point detection algorithm detecting a change in mean, based on the cumulative sum (CUSUM), same-variance assumption, and using bootstrap sampling for significance estimation.

Uses of References in de.lmu.ifi.dbs.elki.data.uncertain

Classes in de.lmu.ifi.dbs.elki.data.uncertain with annotations of type References

Modifier and Type	Class and Description
class	UnweightedDiscreteUncertainObject Unweighted implementation of discrete uncertain objects.
class	WeightedDiscreteUncertainObject Weighted version of discrete uncertain objects.

Uses of References in de.lmu.ifi.dbs.elki.distance.distancefunction

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

Modifier and Type	Class and Description
class	BrayCurtisDistanceFunction Bray-Curtis distance function / Sørensen–Dice coefficient for continuous vector spaces (not only binary data).

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

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

Modifier and Type	Class and Description
class	ChiDistanceFunction χ distance function, symmetric version.
class	FisherRaoDistanceFunction Fisher-Rao riemannian metric for (discrete) probability distributions.
class	HellingerDistanceFunction Hellinger metric / affinity / kernel, Bhattacharyya coefficient, fidelity similarity, Matusita distance, Hellinger-Kakutani metric on a probability distribution.
class	JeffreyDivergenceDistanceFunction Jeffrey Divergence for NumberVectors is a symmetric, smoothened version of the KullbackLeiblerDivergenceAsymmetricDistanceFunction.
class	JensenShannonDivergenceDistanceFunction Jensen-Shannon Divergence for NumberVectors is a symmetric, smoothened version of the KullbackLeiblerDivergenceAsymmetricDistanceFunction.

Uses of References in de.lmu.ifi.dbs.elki.index.idistance

Classes in de.lmu.ifi.dbs.elki.index.idistance with annotations of type References

Modifier and Type	Class and Description
class	InMemoryIDistanceIndex<O> In-memory iDistance index, a metric indexing method using a reference point embedding.

Uses of References in de.lmu.ifi.dbs.elki.index.lsh.hashfamilies

Classes in de.lmu.ifi.dbs.elki.index.lsh.hashfamilies with annotations of type References

Modifier and Type	Class and Description
class	CosineHashFunctionFamily Hash function family to use with Cosine distance, using simplified hash functions where the projection is only drawn from +-1, instead of Gaussian distributions.

Uses of References in de.lmu.ifi.dbs.elki.math

Classes in de.lmu.ifi.dbs.elki.math with annotations of type References

Modifier and Type	Class and Description
class	MeanVariance Do some simple statistics (mean, variance) using a numerically stable online algorithm.
class	StatisticalMoments Track various statistical moments, including mean, variance, skewness and kurtosis.

Uses of References in de.lmu.ifi.dbs.elki.math.linearalgebra.pca

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with annotations of type References

Modifier and Type	Class and Description
class	RANSACCovarianceMatrixBuilder RANSAC based approach to a more robust covariance matrix computation.

Uses of References in de.lmu.ifi.dbs.elki.math.statistics

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

Modifier and Type	Class and Description
class	ProbabilityWeightedMoments Estimate the L-Moments of a sample.

Uses of References in de.lmu.ifi.dbs.elki.math.statistics.dependence

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

Modifier and Type	Class and Description
class	HiCSDependenceMeasure Use the statistical tests as used by HiCS to measure dependence of variables.
class	SURFINGDependenceMeasure Compute the similarity of dimensions using the SURFING score.

Uses of References in de.lmu.ifi.dbs.elki.math.statistics.distribution

Methods in de.lmu.ifi.dbs.elki.math.statistics.distribution with annotations of type References

Modifier and Type	Method and Description
static double	GammaDistribution.nextRandom(double k, double theta, java.util.Random random) Generate a random value with the generators parameters.

Uses of References in de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality

Classes in de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality with annotations of type References

Modifier and Type	Class and Description
class	LMomentsEstimator Probability weighted moments based estimator using L-Moments.
class	PWM2Estimator Probability weighted moments based estimator, using the second moment.
class	PWMEstimator Probability weighted moments based estimator.
class	ZipfEstimator Zipf estimator (qq-estimator) of the intrinsic dimensionality.

Uses of References in de.lmu.ifi.dbs.elki.math.statistics.tests

Classes in de.lmu.ifi.dbs.elki.math.statistics.tests with annotations of type References

Modifier and Type	Class and Description
class	StandardizedTwoSampleAndersonDarlingTest Perform a two-sample Anderson-Darling rank test, and standardize the statistic according to Scholz and Stephens.

Uses of References in de.lmu.ifi.dbs.elki.utilities.scaling.outlier

Classes in de.lmu.ifi.dbs.elki.utilities.scaling.outlier with annotations of type References

Modifier and Type	Class and Description
class	COPOutlierScaling CDF based outlier score scaling.