| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel |
Parallelized variants of LOF.
|
| 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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CanopyPreClustering<O>
Canopy pre-clustering is a simple preprocessing step for clustering.
|
class |
DBSCAN<O>
Density-Based Clustering of Applications with Noise (DBSCAN), an algorithm to
find density-connected sets in a database.
|
class |
NaiveMeanShiftClustering<V extends NumberVector>
Mean-shift based clustering algorithm.
|
class |
SNNClustering<O>
Shared nearest neighbor clustering.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AffinityPropagationClusteringAlgorithm<O>
Cluster analysis by affinity propagation.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ChengAndChurch<V extends NumberVector>
Perform Cheng and Church biclustering.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CASH<V extends NumberVector>
The CASH algorithm is a subspace clustering algorithm based on the Hough
transform.
|
class |
COPAC<V extends NumberVector>
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.
|
class |
ERiC<V extends NumberVector>
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.
|
class |
FourC<V extends NumberVector>
4C identifies local subgroups of data objects sharing a uniform correlation.
|
class |
HiCO<V extends NumberVector>
Implementation of the HiCO algorithm, an algorithm for detecting hierarchies
of correlation clusters.
|
class |
LMCLUS
Linear manifold clustering in high dimensional spaces by stochastic search.
|
class |
ORCLUS<V extends NumberVector>
ORCLUS: Arbitrarily ORiented projected CLUSter generation.
|
| 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).
|
| Modifier and Type | Class and Description |
|---|---|
class |
COPACNeighborPredicate<V extends NumberVector>
COPAC neighborhood predicate.
|
class |
EpsilonNeighborPredicate<O>
The default DBSCAN and OPTICS neighbor predicate, using an
epsilon-neighborhood.
|
class |
ERiCNeighborPredicate<V extends NumberVector>
ERiC neighborhood predicate.
|
class |
FourCCorePredicate
The PreDeCon core point predicate -- having at least minpts. neighbors, and a
maximum preference dimensionality of lambda.
|
class |
FourCNeighborPredicate<V extends NumberVector>
4C identifies local subgroups of data objects sharing a uniform correlation.
|
class |
GeneralizedDBSCAN
Generalized DBSCAN, density-based clustering with noise.
|
class |
LSDBC<O extends NumberVector>
Locally scaled Density Based Clustering.
|
class |
MinPtsCorePredicate
The DBSCAN default core point predicate -- having at least
MinPtsCorePredicate.minpts
neighbors. |
class |
PreDeConCorePredicate
The PreDeCon core point predicate -- having at least minpts. neighbors, and a
maximum preference dimensionality of lambda.
|
class |
PreDeConNeighborPredicate<V extends NumberVector>
Neighborhood predicate used by PreDeCon.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractHDBSCAN<O,R extends Result>
Abstract base class for HDBSCAN variations.
|
class |
AGNES<O>
Hierarchical Agglomerative Clustering (HAC) or Agglomerative Nesting (AGNES)
is a classic hierarchical clustering algorithm.
|
class |
AnderbergHierarchicalClustering<O>
This is a modification of the classic AGNES algorithm for hierarchical
clustering using a nearest-neighbor heuristic for acceleration.
|
class |
CentroidLinkageMethod
Centroid linkage clustering method, aka UPGMC: Unweighted Pair-Group Method
using Centroids.
|
class |
CLINK<O>
CLINK algorithm for complete linkage.
|
class |
GroupAverageLinkageMethod
Group-average linkage clustering method.
|
class |
HDBSCANLinearMemory<O>
Linear memory implementation of HDBSCAN clustering.
|
interface |
LinkageMethod
Abstract interface for implementing a new linkage method into hierarchical
clustering.
|
class |
MedianLinkageMethod
Median-linkage clustering method: Weighted pair group method using centroids
(WPGMC).
|
class |
SingleLinkageMethod
Single-linkage clustering method.
|
class |
SLINK<O>
Implementation of the efficient Single-Link Algorithm SLINK of R.
|
class |
SLINKHDBSCANLinearMemory<O>
Linear memory implementation of HDBSCAN clustering based on SLINK.
|
class |
WardLinkageMethod
Ward's method clustering method.
|
class |
WeightedAverageLinkageMethod
Weighted average linkage clustering method.
|
| Modifier and Type | Field and Description |
|---|---|
static Void |
AGNES.ADDITIONAL_REFERENCE
Additional historical reference for single-linkage.
|
| Modifier and Type | Class and Description |
|---|---|
class |
HDBSCANHierarchyExtraction
Extraction of simplified cluster hierarchies, as proposed in HDBSCAN.
|
class |
SimplifiedHierarchyExtraction
Extraction of simplified cluster hierarchies, as proposed in HDBSCAN.
|
| 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 |
KMeansBisecting<V extends 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.
|
class |
KMeansElkan<V extends NumberVector>
Elkan's fast k-means by exploiting the triangle inequality.
|
class |
KMeansHamerly<V extends NumberVector>
Hamerly's fast k-means by exploiting the triangle inequality.
|
class |
KMeansLloyd<V extends NumberVector>
The standard k-means algorithm, using Lloyd-style bulk iterations.
|
class |
KMeansMacQueen<V extends NumberVector>
The original k-means algorithm, using MacQueen style incremental updates;
making this effectively an "online" (streaming) algorithm.
|
class |
KMediansLloyd<V extends NumberVector>
k-medians clustering algorithm, but using Lloyd-style bulk iterations instead
of the more complicated approach suggested by Kaufman and Rousseeuw (see
KMedoidsPAM instead). |
class |
KMedoidsPAM<V>
The original PAM algorithm or k-medoids clustering, as proposed by Kaufman
and Rousseeuw in "Partitioning Around Medoids".
|
class |
XMeans<V extends NumberVector,M extends MeanModel>
X-means: Extending K-means with Efficient Estimation on the Number of
Clusters.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KMeansPlusPlusInitialMeans<O>
K-Means++ initialization for k-means.
|
class |
PAMInitialMeans<O>
PAM initialization for k-means (and of course, PAM).
|
class |
RandomlyChosenInitialMeans<O>
Initialize K-means by randomly choosing k existing elements as cluster
centers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractKMeansQualityMeasure<O extends NumberVector>
Base class for evaluating clusterings by information criteria (such as AIC or
BIC).
|
class |
AkaikeInformationCriterion
Akaike Information Criterion (AIC).
|
class |
BayesianInformationCriterion
Bayesian Information Criterion (BIC), also known as Schwarz criterion (SBC,
SBIC) for the use with evaluating k-means results.
|
class |
BayesianInformationCriterionZhao
Different version of the BIC criterion.
|
| Modifier and Type | Method and Description |
|---|---|
static <V extends NumberVector> |
AbstractKMeansQualityMeasure.logLikelihood(Relation<V> relation,
Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction)
Computes log likelihood of an entire clustering.
|
static <V extends NumberVector> |
AbstractKMeansQualityMeasure.logLikelihoodAlternate(Relation<V> relation,
Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction)
Computes log likelihood of an entire clustering.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractOPTICS<O>
The OPTICS algorithm for density-based hierarchical clustering.
|
class |
DeLiClu<NV extends NumberVector>
DeliClu: Density-Based Hierarchical Clustering, a hierarchical algorithm to
find density-connected sets in a database.
|
class |
FastOPTICS<V extends NumberVector>
FastOPTICS algorithm (Fast approximation of OPTICS)
Note that this is not FOPTICS as in "Fuzzy OPTICS"!
|
class |
OPTICSHeap<O>
The OPTICS algorithm for density-based hierarchical clustering.
|
class |
OPTICSList<O>
The OPTICS algorithm for density-based hierarchical clustering.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CLIQUE<V extends NumberVector>
Implementation of the CLIQUE algorithm, a grid-based algorithm to identify
dense clusters in subspaces of maximum dimensionality.
|
class |
DiSH<V extends NumberVector>
Algorithm for detecting subspace hierarchies.
|
class |
DOC<V extends NumberVector>
The DOC algorithm, and it's heuristic variant, FastDOC.
|
class |
HiSC<V extends NumberVector>
Implementation of the HiSC algorithm, an algorithm for detecting hierarchies
of subspace clusters.
|
class |
P3C<V extends NumberVector>
P3C: A Robust Projected Clustering Algorithm.
|
class |
PreDeCon<V extends NumberVector>
PreDeCon computes clusters of subspace preference weighted connected points.
|
class |
PROCLUS<V extends NumberVector>
The PROCLUS algorithm, an algorithm to find subspace clusters in high
dimensional spaces.
|
class |
SUBCLU<V extends NumberVector>
Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily
shaped and positioned clusters in subspaces.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CenterOfMassMetaClustering<C extends Clustering<?>>
Center-of-mass meta clustering reduces uncertain objects to their center of
mass, then runs a vector-oriented clustering algorithm on this data set.
|
class |
CKMeans
Run k-means on the centers of each uncertain object.
|
class |
FDBSCAN
FDBSCAN is an adaption of DBSCAN for fuzzy (uncertain) objects.
|
class |
FDBSCANNeighborPredicate
Density-based Clustering of Applications with Noise and Fuzzy objects
(FDBSCAN) is an Algorithm to find sets in a fuzzy database that are
density-connected with minimum probability.
|
class |
RepresentativeUncertainClustering
Representative clustering of uncertain data.
|
class |
UKMeans
Uncertain K-Means clustering, using the average deviation from the center.
|
| Modifier and Type | Class and Description |
|---|---|
class |
APRIORI
The APRIORI algorithm for Mining Association Rules.
|
class |
Eclat
Eclat is a depth-first discovery algorithm for mining frequent itemsets.
|
class |
FPGrowth
FP-Growth is an algorithm for mining the frequent itemsets by using a
compressed representation of the database called
FPGrowth.FPTree. |
| Modifier and Type | Class and Description |
|---|---|
class |
COP<V extends NumberVector>
Correlation outlier probability: Outlier Detection in Arbitrarily Oriented
Subspaces
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
Outlier Detection in Arbitrarily Oriented Subspaces in: Proc. |
class |
DWOF<O>
Algorithm to compute dynamic-window outlier factors in a database based on a
specified parameter
DWOF.Parameterizer.K_ID (-dwof.k). |
class |
GaussianUniformMixture<V extends NumberVector>
Outlier detection algorithm using a mixture model approach.
|
class |
OPTICSOF<O>
Optics-OF outlier detection algorithm, an algorithm to find Local Outliers in
a database based on ideas from
OPTICSTypeAlgorithm clustering. |
class |
SimpleCOP<V extends NumberVector>
Algorithm to compute local correlation outlier probability.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ABOD<V extends NumberVector>
Angle-Based Outlier Detection / Angle-Based Outlier Factor.
|
class |
FastABOD<V extends NumberVector>
Angle-Based Outlier Detection / Angle-Based Outlier Factor.
|
class |
LBABOD<V extends NumberVector>
Angle-Based Outlier Detection / Angle-Based Outlier Factor.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SilhouetteOutlierDetection<O>
Outlier detection by using the Silhouette Coefficients.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractDBOutlier<O>
Simple distance based outlier detection algorithms.
|
class |
DBOutlierDetection<O>
Simple distanced based outlier detection algorithm.
|
class |
DBOutlierScore<O>
Compute percentage of neighbors in the given neighborhood with size d.
|
class |
HilOut<O extends NumberVector>
Fast Outlier Detection in High Dimensional Spaces
Outlier Detection using Hilbert space filling curves
Reference:
F.
|
class |
KNNOutlier<O>
Outlier Detection based on the distance of an object to its k nearest
neighbor.
|
class |
KNNWeightOutlier<O>
Outlier Detection based on the accumulated distances of a point to its k
nearest neighbors.
|
class |
LocalIsolationCoefficient<O>
The Local Isolation Coefficient is the sum of the kNN distance and the
average distance to its k nearest neighbors.
|
class |
ODIN<O>
Outlier detection based on the in-degree of the kNN graph.
|
class |
ReferenceBasedOutlierDetection
Reference-Based Outlier Detection algorithm, an algorithm that computes kNN
distances approximately, using reference points.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ParallelKNNOutlier<O>
Parallel implementation of KNN Outlier detection.
|
class |
ParallelKNNWeightOutlier<O>
Parallel implementation of KNN Weight Outlier detection.
|
| Modifier and Type | Class and Description |
|---|---|
class |
IDOS<O>
Intrinsic Dimensional Outlier Detection in High-Dimensional Data.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ALOCI<O extends NumberVector>
Fast Outlier Detection Using the "approximate Local Correlation Integral".
|
class |
COF<O>
Connectivity-based outlier factor (COF).
|
class |
FlexibleLOF<O>
Flexible variant of the "Local Outlier Factor" algorithm.
|
class |
INFLO<O>
Influence Outliers using Symmetric Relationship (INFLO) using two-way search,
is an outlier detection method based on LOF; but also using the reverse kNN.
|
class |
KDEOS<O>
Generalized Outlier Detection with Flexible Kernel Density Estimates.
|
class |
LDF<O extends NumberVector>
Outlier Detection with Kernel Density Functions.
|
class |
LDOF<O>
Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a
Database.
|
class |
LOCI<O>
Fast Outlier Detection Using the "Local Correlation Integral".
|
class |
LOF<O>
Algorithm to compute density-based local outlier factors in a database based
on a specified parameter
LOF.Parameterizer.K_ID (-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.
|
class |
SimplifiedLOF<O>
A simplified version of the original LOF algorithm, which does not use the
reachability distance, yielding less stable results on inliers.
|
class |
VarianceOfVolume<O extends SpatialComparable>
Variance of Volume for outlier detection.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ParallelLOF<O>
Parallel implementation of Local Outlier Factor using processors.
|
class |
ParallelSimplifiedLOF<O>
Parallel implementation of Simplified-LOF Outlier detection using processors.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FeatureBagging
A simple ensemble method called "Feature bagging" for outlier detection.
|
class |
HiCS<V extends NumberVector>
Algorithm to compute High Contrast Subspaces for Density-Based Outlier
Ranking.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CTLuGLSBackwardSearchAlgorithm<V extends NumberVector>
GLS-Backward Search is a statistical approach to detecting spatial outliers.
|
class |
CTLuMeanMultipleAttributes<N,O extends NumberVector>
Mean Approach is used to discover spatial outliers with multiple attributes.
|
class |
CTLuMedianAlgorithm<N>
Median Algorithm of C.
|
class |
CTLuMedianMultipleAttributes<N,O extends NumberVector>
Median Approach is used to discover spatial outliers with multiple
attributes.
|
class |
CTLuMoranScatterplotOutlier<N>
Moran scatterplot outliers, based on the standardized deviation from the
local and global means.
|
class |
CTLuRandomWalkEC<P>
Spatial outlier detection based on random walks.
|
class |
CTLuScatterplotOutlier<N>
Scatterplot-outlier is a spatial outlier detection method that performs a
linear regression of object attributes and their neighbors average value.
|
class |
CTLuZTestOutlier<N>
Detect outliers by comparing their attribute value to the mean and standard
deviation of their neighborhood.
|
class |
SLOM<N,O>
SLOM: a new measure for local spatial outliers
Reference:
Sanjay Chawla and Pei Sun SLOM: a new measure for local spatial outliers in Knowledge and Information Systems 9(4), 412-429, 2006 This implementation works around some corner cases in SLOM, in particular when an object has none or a single neighbor only (albeit the results will still not be too useful then), which will result in divisions by zero. |
class |
SOF<N,O>
The Spatial Outlier Factor (SOF) is a spatial
LOF variation. |
class |
TrimmedMeanApproach<N>
A Trimmed Mean Approach to Finding Spatial Outliers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractAggarwalYuOutlier<V extends NumberVector>
Abstract base class for the sparse-grid-cell based outlier detection of
Aggarwal and Yu.
|
class |
AggarwalYuEvolutionary<V extends NumberVector>
Evolutionary variant (EAFOD) of the high-dimensional outlier detection
algorithm by Aggarwal and Yu.
|
class |
AggarwalYuNaive<V extends NumberVector>
BruteForce variant of the high-dimensional outlier detection algorithm by
Aggarwal and Yu.
|
class |
OutRankS1
OutRank: ranking outliers in high dimensional data.
|
class |
OUTRES<V extends NumberVector>
Adaptive outlierness for subspace outlier ranking (OUTRES).
|
class |
SOD<V extends NumberVector>
Subspace Outlier Degree.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LibSVMOneClassOutlierDetection<V extends NumberVector>
Outlier-detection using one-class support vector machines.
|
| Modifier and Type | Class and Description |
|---|---|
class |
HopkinsStatisticClusteringTendency
The Hopkins Statistic of Clustering Tendency measures the probability that a
data set is generated by a uniform data distribution.
|
| Modifier and Type | Field and Description |
|---|---|
static String |
AbstractApplication.REFERENCE
Information for citation and version.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ComputeKNNOutlierScores<O extends NumberVector>
Application that runs a series of kNN-based algorithms on a data set, for
building an ensemble in a second step.
|
class |
GreedyEnsembleExperiment
Class to load an outlier detection summary file, as produced by
ComputeKNNOutlierScores, and compute a naive ensemble for it. |
class |
VisualizePairwiseGainMatrix
Class to load an outlier detection summary file, as produced by
ComputeKNNOutlierScores, and compute a matrix with the pairwise
gains. |
| Modifier and Type | Method and Description |
|---|---|
private static List<Pair<Reference,TreeSet<Object>>> |
DocumentReferences.sortedReferences() |
| Modifier and Type | Method and Description |
|---|---|
private static void |
DocumentReferences.addReference(Object cls,
Reference ref,
List<Pair<Reference,TreeSet<Object>>> refs,
Map<Reference,TreeSet<Object>> map) |
| Modifier and Type | Method and Description |
|---|---|
private static void |
DocumentReferences.addReference(Object cls,
Reference ref,
List<Pair<Reference,TreeSet<Object>>> refs,
Map<Reference,TreeSet<Object>> map) |
private static void |
DocumentReferences.addReference(Object cls,
Reference ref,
List<Pair<Reference,TreeSet<Object>>> refs,
Map<Reference,TreeSet<Object>> map) |
private static Document |
DocumentReferences.documentReferences(List<Pair<Reference,TreeSet<Object>>> refs) |
private static void |
DocumentReferences.documentReferencesWiki(List<Pair<Reference,TreeSet<Object>>> refs,
PrintStream refstreamW) |
private static void |
DocumentReferences.inspectClass(Class<?> cls,
List<Pair<Reference,TreeSet<Object>>> refs,
Map<Reference,TreeSet<Object>> map) |
private static void |
DocumentReferences.inspectClass(Class<?> cls,
List<Pair<Reference,TreeSet<Object>>> refs,
Map<Reference,TreeSet<Object>> map) |
private static void |
DocumentReferences.inspectPackage(Package p,
List<Pair<Reference,TreeSet<Object>>> refs,
Map<Reference,TreeSet<Object>> map) |
private static void |
DocumentReferences.inspectPackage(Package p,
List<Pair<Reference,TreeSet<Object>>> refs,
Map<Reference,TreeSet<Object>> map) |
| Modifier and Type | Class and Description |
|---|---|
class |
UnweightedDiscreteUncertainObject
Unweighted implementation of discrete uncertain objects.
|
class |
WeightedDiscreteUncertainObject
Weighted version of discrete uncertain objects.
|
| Modifier and Type | Class and Description |
|---|---|
(package private) class |
IntegerDBIDArrayQuickSort
Class to sort an integer DBID array, using a modified quicksort.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LinearDiscriminantAnalysisFilter<V extends NumberVector>
Linear Discriminant Analysis (LDA) / Fisher's linear discriminant.
|
class |
PerturbationFilter<V extends NumberVector>
A filter to perturb the values by adding micro-noise.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BrayCurtisDistanceFunction
Bray-Curtis distance function / Sørensen–Dice coefficient for continuous
spaces.
|
class |
CanberraDistanceFunction
Canberra distance function, a variation of Manhattan distance.
|
class |
ClarkDistanceFunction
Clark distance function for vector spaces.
|
class |
Kulczynski1DistanceFunction
Kulczynski similarity 1, in distance form.
|
class |
LorentzianDistanceFunction
Lorentzian distance function for vector spaces.
|
| Modifier and Type | Method and Description |
|---|---|
(package private) static void |
BrayCurtisDistanceFunction.secondReference()
Dummy method, just to attach a second reference.
|
(package private) static void |
BrayCurtisDistanceFunction.thirdReference()
Dummy method, just to attach a third reference.
|
| Modifier and Type | Class and Description |
|---|---|
class |
HistogramIntersectionDistanceFunction
Intersection distance for color histograms.
|
class |
HSBHistogramQuadraticDistanceFunction
Distance function for HSB color histograms based on a quadratic form and
color similarity.
|
class |
RGBHistogramQuadraticDistanceFunction
Distance function for RGB color histograms based on a quadratic form and
color similarity.
|
| Modifier and Type | Method and Description |
|---|---|
double |
LngLatDistanceFunction.minDist(SpatialComparable mbr1,
SpatialComparable mbr2) |
double |
LatLngDistanceFunction.minDist(SpatialComparable mbr1,
SpatialComparable mbr2) |
double |
DimensionSelectingLatLngDistanceFunction.minDist(SpatialComparable mbr1,
SpatialComparable mbr2) |
| Modifier and Type | Class and Description |
|---|---|
class |
HistogramMatchDistanceFunction
Distance function based on histogram matching, i.e.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ChiSquaredDistanceFunction
Chi-Squared distance function, symmetric version.
|
class |
HellingerDistanceFunction
Hellinger kernel / Hellinger distance are used with SIFT vectors, and also
known as Bhattacharyya distance / coefficient.
|
class |
JeffreyDivergenceDistanceFunction
Jeffrey Divergence Distance for
NumberVectors. |
class |
KullbackLeiblerDivergenceAsymmetricDistanceFunction
Kullback-Leibler (asymmetric!)
|
class |
KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction
Kullback-Leibler (asymmetric!)
|
class |
SqrtJensenShannonDivergenceDistanceFunction
The square root of Jensen-Shannon divergence is metric.
|
| Modifier and Type | Class and Description |
|---|---|
class |
HammingDistanceFunction
Computes the Hamming distance of arbitrary vectors - i.e. counting, on how
many places they differ.
|
class |
JaccardSimilarityDistanceFunction<O extends FeatureVector<?>>
A flexible extension of Jaccard similarity to non-binary vectors.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LevenshteinDistanceFunction
Classic Levenshtein distance on strings.
|
class |
NormalizedLevenshteinDistanceFunction
Levenshtein distance on strings, normalized by string length.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DerivativeDTWDistanceFunction
Derivative Dynamic Time Warping distance for numerical vectors.
|
class |
DTWDistanceFunction
Dynamic Time Warping distance (DTW) for numerical vectors.
|
class |
EDRDistanceFunction
Edit Distance on Real Sequence distance for numerical vectors.
|
class |
ERPDistanceFunction
Edit Distance With Real Penalty distance for numerical vectors.
|
class |
LCSSDistanceFunction
Longest Common Subsequence distance for numerical vectors.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Kulczynski1SimilarityFunction
Kulczynski similarity 1.
|
class |
Kulczynski2SimilarityFunction
Kulczynski similarity 2.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ClusteringAdjustedRandIndexSimilarityFunction
Measure the similarity of clusters via the Adjusted Rand Index.
|
class |
ClusteringBCubedF1SimilarityFunction
Measure the similarity of clusters via the BCubed F1 Index.
|
class |
ClusteringFowlkesMallowsSimilarityFunction
Measure the similarity of clusters via the Fowlkes-Mallows Index.
|
class |
ClusteringRandIndexSimilarityFunction
Measure the similarity of clusters via the Rand Index.
|
class |
ClusterJaccardSimilarityFunction
Measure the similarity of clusters via the Jaccard coefficient.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BCubed
BCubed measures.
|
class |
EditDistance
Edit distance measures.
|
class |
Entropy
Entropy based measures.
|
class |
SetMatchingPurity
Set matching purity measures.
|
| Modifier and Type | Method and Description |
|---|---|
double |
SetMatchingPurity.f1Measure()
Get the set matching F1-Measure
Steinbach, M. and Karypis, G. and Kumar, V.
|
double |
SetMatchingPurity.fMeasureFirst()
Get the Van Rijsbergen’s F measure (asymmetric) for first clustering
E.
|
double |
SetMatchingPurity.fMeasureSecond()
Get the Van Rijsbergen’s F measure (asymmetric) for second clustering
E.
|
double |
PairCounting.fowlkesMallows()
Computes the pair-counting Fowlkes-mallows (flat only, non-hierarchical!)
|
double |
Entropy.normalizedVariationOfInformation()
Get the normalized variation of information (normalized, 0 = equal) NVI = 1
- NMI_Joint
Nguyen, X.
|
double |
SetMatchingPurity.purity()
Get the set matchings purity (first:second clustering) (normalized, 1 =
equal)
|
double |
PairCounting.randIndex()
Computes the Rand index (RI).
|
| Modifier and Type | Class and Description |
|---|---|
class |
EvaluateCIndex<O>
Compute the C-index of a data set.
|
class |
EvaluateConcordantPairs<O>
Compute the Gamma Criterion of a data set.
|
class |
EvaluateDaviesBouldin
Compute the Davies-Bouldin index of a data set.
|
class |
EvaluatePBMIndex
Compute the PBM of a data set
Reference:
M.
|
class |
EvaluateSilhouette<O>
Compute the silhouette of a data set.
|
class |
EvaluateVarianceRatioCriteria<O>
Compute the Variance Ratio Criteria of a data set.
|
| Modifier and Type | Method and Description |
|---|---|
double |
EvaluateConcordantPairs.computeTau(long c,
long d,
double m,
long wd,
long bd)
Compute the Tau correlation measure
|
| Modifier and Type | Class and Description |
|---|---|
class |
Segments
Creates segments of two or more clusterings.
|
| Modifier and Type | Class and Description |
|---|---|
class |
OutlierSmROCCurve
Smooth ROC curves are a variation of classic ROC curves that takes the scores
into account.
|
| Modifier and Type | Class and Description |
|---|---|
class |
InMemoryIDistanceIndex<O>
In-memory iDistance index, a metric indexing method using a reference point
embedding.
|
| Modifier and Type | Field and Description |
|---|---|
static Void |
InMemoryIDistanceIndex.SECOND_REFERENCE
Second reference, for documentation generation.
|
| 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.
|
class |
EuclideanHashFunctionFamily
2-stable hash function family for Euclidean distances.
|
class |
ManhattanHashFunctionFamily
2-stable hash function family for Euclidean distances.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CosineLocalitySensitiveHashFunction
Random projection family to use with sparse vectors.
|
class |
MultipleProjectionsLocalitySensitiveHashFunction
LSH hash function for vector space data.
|
| Modifier and Type | Class and Description |
|---|---|
class |
RandomProjectedNeighborsAndDensities<V extends NumberVector>
Random Projections used for computing neighbors and density estimates.
|
| Modifier and Type | Class and Description |
|---|---|
class |
NaiveProjectedKNNPreprocessor<O extends NumberVector>
Compute the approximate k nearest neighbors using 1 dimensional projections.
|
class |
RandomSampleKNNPreprocessor<O>
Class that computed the kNN only on a random sample.
|
class |
SpacefillingKNNPreprocessor<O extends NumberVector>
Compute the nearest neighbors approximatively using space filling curves.
|
class |
SpacefillingMaterializeKNNPreprocessor<O extends NumberVector>
Compute the nearest neighbors approximatively using space filling curves.
|
| Modifier and Type | Class and Description |
|---|---|
class |
PINN<O extends NumberVector>
Projection-Indexed nearest-neighbors (PINN) is an index to retrieve the
nearest neighbors in high dimensional spaces by using a random projection
based index.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CoverTree<O>
Cover tree data structure (in-memory).
|
| Modifier and Type | Class and Description |
|---|---|
class |
MTree<O>
MTree is a metrical index structure based on the concepts of the M-Tree.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MinimumEnlargementInsert<O,N extends AbstractMTreeNode<O,N,E>,E extends MTreeEntry>
Default insertion strategy for the M-tree.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MLBDistSplit<O,N extends AbstractMTreeNode<O,N,E>,E extends MTreeEntry>
Encapsulates the required methods for a split of a node in an M-Tree.
|
class |
MMRadSplit<O,N extends AbstractMTreeNode<O,N,E>,E extends MTreeEntry>
Encapsulates the required methods for a split of a node in an M-Tree.
|
class |
MRadSplit<O,N extends AbstractMTreeNode<O,N,E>,E extends MTreeEntry>
Encapsulates the required methods for a split of a node in an M-Tree.
|
class |
RandomSplit<O,N extends AbstractMTreeNode<O,N,E>,E extends MTreeEntry>
Encapsulates the required methods for a split of a node in an M-Tree.
|
| Modifier and Type | Class and 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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
EuclideanRStarTreeKNNQuery<O extends NumberVector>
Instance of a KNN query for a particular spatial index.
|
class |
EuclideanRStarTreeRangeQuery<O extends NumberVector>
Instance of a range query for a particular spatial index.
|
class |
RStarTreeKNNQuery<O extends SpatialComparable>
Instance of a KNN query for a particular spatial index.
|
class |
RStarTreeRangeQuery<O extends SpatialComparable>
Instance of a range query for a particular spatial index.
|
| Modifier and Type | Class and Description |
|---|---|
class |
RStarTree
RStarTree is a spatial index structure based on the concepts of the R*-Tree.
|
| Modifier and Type | Class and Description |
|---|---|
class |
OneDimSortBulkSplit
Simple bulk loading strategy by sorting the data along the first dimension.
|
class |
SortTileRecursiveBulkSplit
Sort-Tile-Recursive aims at tiling the data space with a grid-like structure
for partitioning the dataset into the required number of buckets.
|
class |
SpatialSortBulkSplit
Bulk loading by spatially sorting the objects, then partitioning the sorted
list appropriately.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ApproximativeLeastOverlapInsertionStrategy
The choose subtree method proposed by the R*-Tree with slightly better
performance for large leaf sizes (linear approximation).
|
class |
CombinedInsertionStrategy
Use two different insertion strategies for directory and leaf nodes.
|
class |
LeastEnlargementInsertionStrategy
The default R-Tree insertion strategy: find rectangle with least volume
enlargement.
|
class |
LeastEnlargementWithAreaInsertionStrategy
A slight modification of the default R-Tree insertion strategy: find
rectangle with least volume enlargement, but choose least area on ties.
|
class |
LeastOverlapInsertionStrategy
The choose subtree method proposed by the R*-Tree for leaf nodes.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LimitedReinsertOverflowTreatment
Limited reinsertions, as proposed by the R*-Tree: For each real insert, allow
reinsertions to happen only once per level.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CloseReinsert
Reinsert objects on page overflow, starting with close objects first (even
when they will likely be inserted into the same page again!)
|
class |
FarReinsert
Reinsert objects on page overflow, starting with farther objects first (even
when they will likely be inserted into the same page again!)
|
| Modifier and Type | Class and Description |
|---|---|
class |
AngTanLinearSplit
Line-time complexity split proposed by Ang and Tan.
|
class |
GreeneSplit
Quadratic-time complexity split as used by Diane Greene for the R-Tree.
|
class |
RTreeLinearSplit
Linear-time complexity greedy split as used by the original R-Tree.
|
class |
RTreeQuadraticSplit
Quadratic-time complexity greedy split as used by the original R-Tree.
|
class |
TopologicalSplitter
Encapsulates the required parameters for a topological split of a R*-Tree.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DAFile
Dimension approximation file, a one-dimensional part of the
PartialVAFile. |
class |
PartialVAFile<V extends NumberVector>
PartialVAFile.
|
class |
VAFile<V extends NumberVector>
Vector-approximation file (VAFile)
Reference:
Weber, R. and Blott, S.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Mean
Compute the mean using a numerically stable online algorithm.
|
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.
|
| Modifier and Type | Method and Description |
|---|---|
void |
MeanVariance.put(double val,
double weight)
Add data with a given weight.
|
| Modifier and Type | Class and Description |
|---|---|
class |
HiCSDimensionSimilarity
Use the statistical tests as used by HiCS to arrange dimensions.
|
class |
HSMDimensionSimilarity
Compute the similarity of dimensions by using a hough transformation.
|
class |
MCEDimensionSimilarity
Compute dimension similarity by using a nested means discretization.
|
class |
SlopeDimensionSimilarity
Arrange dimensions based on the entropy of the slope spectrum.
|
class |
SlopeInversionDimensionSimilarity
Arrange dimensions based on the entropy of the slope spectrum.
|
class |
SURFINGDimensionSimilarity
Compute the similarity of dimensions using the SURFING score.
|
| Modifier and Type | Method and Description |
|---|---|
void |
SURFINGDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix) |
| Modifier and Type | Class and Description |
|---|---|
class |
SphereUtil
Class with utility functions for distance computations on the sphere.
|
| Modifier and Type | Method and Description |
|---|---|
static double |
SphereUtil.ellipsoidVincentyFormulaDeg(double f,
double lat1,
double lon1,
double lat2,
double lon2)
Compute the approximate great-circle distance of two points.
|
static double |
SphereUtil.ellipsoidVincentyFormulaRad(double f,
double lat1,
double lon1,
double lat2,
double lon2)
Compute the approximate great-circle distance of two points.
|
static double |
SphereUtil.haversineFormulaDeg(double lat1,
double lon1,
double lat2,
double lon2)
Compute the approximate great-circle distance of two points using the
Haversine formula
Complexity: 5 trigonometric functions, 2 sqrt.
|
static double |
SphereUtil.haversineFormulaRad(double lat1,
double lon1,
double lat2,
double lon2)
Compute the approximate great-circle distance of two points using the
Haversine formula
Complexity: 5 trigonometric functions, 2 sqrt.
|
static double |
SphereUtil.latlngMinDistDeg(double plat,
double plng,
double rminlat,
double rminlng,
double rmaxlat,
double rmaxlng)
Point to rectangle minimum distance.
|
static double |
SphereUtil.latlngMinDistRad(double plat,
double plng,
double rminlat,
double rminlng,
double rmaxlat,
double rmaxlng)
Point to rectangle minimum distance.
|
static double |
SphereUtil.latlngMinDistRadFull(double plat,
double plng,
double rminlat,
double rminlng,
double rmaxlat,
double rmaxlng)
Point to rectangle minimum distance.
|
static double |
SphereUtil.sphericalVincentyFormulaDeg(double lat1,
double lon1,
double lat2,
double lon2)
Compute the approximate great-circle distance of two points.
|
static double |
SphereUtil.sphericalVincentyFormulaRad(double lat1,
double lon1,
double lat2,
double lon2)
Compute the approximate great-circle distance of two points.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GrahamScanConvexHull2D
Classes to compute the convex hull of a set of points in 2D, using the
classic Grahams scan.
|
class |
PrimsMinimumSpanningTree
Prim's algorithm for finding the minimum spanning tree.
|
class |
SweepHullDelaunay2D
Compute the Convex Hull and/or Delaunay Triangulation, using the sweep-hull
approach of David Sinclair.
|
| Modifier and Type | Class and Description |
|---|---|
class |
PCAFilteredAutotuningRunner
Performs a self-tuning local PCA based on the covariance matrices of given
objects.
|
class |
RANSACCovarianceMatrixBuilder
RANSAC based approach to a more robust covariance matrix computation.
|
class |
WeightedCovarianceMatrixBuilder
CovarianceMatrixBuilder with weights. |
| Modifier and Type | Method and Description |
|---|---|
Matrix |
RANSACCovarianceMatrixBuilder.processIds(DBIDs ids,
Relation<? extends NumberVector> relation) |
| Modifier and Type | Class and Description |
|---|---|
class |
AchlioptasRandomProjectionFamily
Random projections as suggested by Dimitris Achlioptas.
|
class |
CauchyRandomProjectionFamily
Random projections using Cauchy distributions (1-stable).
|
class |
GaussianRandomProjectionFamily
Random projections using Cauchy distributions (1-stable).
|
class |
RandomSubsetProjectionFamily
Random projection family based on selecting random features.
|
class |
SimplifiedRandomHyperplaneProjectionFamily
Random hyperplane projection family.
|
| Modifier and Type | Class and Description |
|---|---|
class |
XorShift1024NonThreadsafeRandom
Replacement for Java's
Random class, using a different
random number generation strategy. |
class |
XorShift64NonThreadsafeRandom
Replacement for Java's
Random class, using a different
random number generation strategy. |
| Modifier and Type | Class and Description |
|---|---|
class |
BinarySplitSpatialSorter
Spatially sort the data set by repetitive binary splitting, circulating
through the dimensions.
|
class |
HilbertSpatialSorter
Sort object along the Hilbert Space Filling curve by mapping them to their
Hilbert numbers and sorting them.
|
class |
PeanoSpatialSorter
Bulk-load an R-tree index by presorting the objects with their position on
the Peano curve.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ProbabilityWeightedMoments
Estimate the L-Moments of a sample.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DistanceCorrelationDependenceMeasure
Distance correlation.
|
class |
HiCSDependenceMeasure
Use the statistical tests as used by HiCS to measure dependence of variables.
|
class |
HoeffdingsDDependenceMeasure
Calculate Hoeffding's D as a measure of dependence.
|
class |
HSMDependenceMeasure
Compute the similarity of dimensions by using a hough transformation.
|
class |
MCEDependenceMeasure
Compute a mutual information based dependence measure using a nested means
discretization, originally proposed for ordering axes in parallel coordinate
plots.
|
class |
SlopeDependenceMeasure
Arrange dimensions based on the entropy of the slope spectrum.
|
class |
SlopeInversionDependenceMeasure
Arrange dimensions based on the entropy of the slope spectrum.
|
class |
SURFINGDependenceMeasure
Compute the similarity of dimensions using the SURFING score.
|
| Modifier and Type | Method and Description |
|---|---|
<A,B> double |
SURFINGDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2) |
| Modifier and Type | Class and Description |
|---|---|
class |
HaltonUniformDistribution
Halton sequences are a pseudo-uniform distribution.
|
| Modifier and Type | Method and Description |
|---|---|
protected static double |
GammaDistribution.chisquaredProbitApproximation(double p,
double nu,
double g)
Approximate probit for chi squared distribution
Based on first half of algorithm AS 91
Reference:
Algorithm AS 91: The percentage points of the $\chi^2$ distribution
D.J. |
private static double |
PoissonDistribution.devianceTerm(double x,
double np)
Evaluate the deviance term of the saddle point approximation.
|
static double |
GammaDistribution.digamma(double x)
Compute the Psi / Digamma function
Reference:
J.
|
static double |
PoissonDistribution.pmf(double x,
int n,
double p)
Poisson probability mass function (PMF) for integer values.
|
static double |
ChiSquaredDistribution.quantile(double x,
double dof)
Return the quantile function for this distribution
Reference:
Algorithm AS 91: The percentage points of the $\chi$^2 distribution
D.J. |
static double |
GammaDistribution.quantile(double p,
double k,
double theta)
Compute probit (inverse cdf) for Gamma distributions.
|
private static double |
PoissonDistribution.stirlingError(double n)
Calculates the Striling Error
stirlerr(n) = ln(n!)
|
private static double |
PoissonDistribution.stirlingError(int n)
Calculates the Striling Error
stirlerr(n) = ln(n!)
|
| Modifier and Type | Class and Description |
|---|---|
class |
CauchyMADEstimator
Estimate Cauchy distribution parameters using Median and MAD.
|
class |
EMGOlivierNorbergEstimator
Naive distribution estimation using mean and sample variance.
|
class |
ExponentialLMMEstimator
Estimate the parameters of a Gamma Distribution, using the methods of
L-Moments (LMM).
|
class |
ExponentialMADEstimator
Estimate Exponential distribution parameters using Median and MAD.
|
class |
ExponentialMedianEstimator
Estimate Exponential distribution parameters using Median and MAD.
|
class |
GammaChoiWetteEstimator
Estimate distribution parameters using the method by Choi and Wette.
|
class |
GammaLMMEstimator
Estimate the parameters of a Gamma Distribution, using the methods of
L-Moments (LMM).
|
class |
GammaMADEstimator
Robust parameter estimation for the Gamma distribution.
|
class |
GammaMOMEstimator
Simple parameter estimation for the Gamma distribution.
|
class |
GeneralizedExtremeValueLMMEstimator
Estimate the parameters of a Generalized Extreme Value Distribution, using
the methods of L-Moments (LMM).
|
class |
GeneralizedLogisticAlternateLMMEstimator
Estimate the parameters of a Generalized Logistic Distribution, using the
methods of L-Moments (LMM).
|
class |
GeneralizedParetoLMMEstimator
Estimate the parameters of a Generalized Pareto Distribution (GPD), using the
methods of L-Moments (LMM).
|
class |
GumbelLMMEstimator
Estimate the parameters of a Gumbel Distribution, using the methods of
L-Moments (LMM).
|
class |
GumbelMADEstimator
Parameter estimation via median and median absolute deviation from median
(MAD).
|
class |
LaplaceMADEstimator
Estimate Laplace distribution parameters using Median and MAD.
|
class |
LaplaceMLEEstimator
Estimate Laplace distribution parameters using Median and mean deviation from
median.
|
class |
LogGammaChoiWetteEstimator
Estimate distribution parameters using the method by Choi and Wette.
|
class |
LogisticLMMEstimator
Estimate the parameters of a Logistic Distribution, using the methods of
L-Moments (LMM).
|
class |
LogisticMADEstimator
Estimate Logistic distribution parameters using Median and MAD.
|
class |
LogLogisticMADEstimator
Estimate Logistic distribution parameters using Median and MAD.
|
class |
LogNormalBilkovaLMMEstimator
Alternate estimate the parameters of a log Gamma Distribution, using the
methods of L-Moments (LMM) for the Generalized Normal Distribution.
|
class |
LogNormalLMMEstimator
Estimate the parameters of a log Normal Distribution, using the methods of
L-Moments (LMM) for the Generalized Normal Distribution.
|
class |
LogNormalLogMADEstimator
Estimator using Medians.
|
class |
NormalLMMEstimator
Estimate the parameters of a normal distribution using the method of
L-Moments (LMM).
|
class |
NormalMADEstimator
Estimator using Medians.
|
class |
RayleighMADEstimator
Estimate the parameters of a RayleighDistribution using the MAD.
|
class |
SkewGNormalLMMEstimator
Estimate the parameters of a skew Normal Distribution (Hoskin's Generalized
Normal Distribution), using the methods of L-Moments (LMM).
|
class |
UniformMADEstimator
Estimate Uniform distribution parameters using Median and MAD.
|
class |
WeibullLogMADEstimator
Parameter estimation via median and median absolute deviation from median
(MAD).
|
| Modifier and Type | Class and Description |
|---|---|
class |
WinsorisingEstimator<D extends Distribution>
Winsorising or Georgization estimator.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AggregatedHillEstimator
Estimator using the weighted average of multiple hill estimators.
|
class |
GEDEstimator
Generalized Expansion Dimension for estimating the intrinsic dimensionality.
|
class |
HillEstimator
Hill estimator of the intrinsic dimensionality (maximum likelihood estimator
for ID).
|
class |
MOMEstimator
Methods of moments estimator, using the first moment (i.e. average).
|
class |
RVEstimator
Regularly Varying Functions estimator of the intrinsic dimensionality
Reference:
L.
|
class |
ZipfEstimator
Zipf estimator (qq-estimator) of the intrinsic dimensionality.
|
| Modifier and Type | Field and Description |
|---|---|
static double |
UniformKernelDensityFunction.CANONICAL_BANDWIDTH
Canonical bandwidth: (9/2)^(1/5)
|
static double |
TriweightKernelDensityFunction.CANONICAL_BANDWIDTH
Canonical bandwidth: (9450/143)^(1/5)
|
static double |
GaussianKernelDensityFunction.CANONICAL_BANDWIDTH
Canonical bandwidth: (1./(4*pi))^(1/10)
|
static double |
EpanechnikovKernelDensityFunction.CANONICAL_BANDWIDTH
Canonical bandwidth: 15^(1/5)
|
static double |
BiweightKernelDensityFunction.CANONICAL_BANDWIDTH
Canonical bandwidth: 35^(1/5)
|
| Modifier and Type | Method and Description |
|---|---|
double |
KernelDensityFunction.canonicalBandwidth()
Get the canonical bandwidth for this kernel.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AndersonDarlingTest
Perform Anderson-Darling test for a Gaussian distribution.
|
class |
StandardizedTwoSampleAndersonDarlingTest
Perform a two-sample Anderson-Darling rank test, and standardize the
statistic according to Scholz and Stephens.
|
| Modifier and Type | Field and Description |
|---|---|
static Void |
StandardizedTwoSampleAndersonDarlingTest.ADDITIONAL_REFERENCE_1
Additional reference -- Darling's note on this equation
|
static Void |
StandardizedTwoSampleAndersonDarlingTest.ADDITIONAL_REFERENCE_2
More detailed discussion by Pettitt
|
| Modifier and Type | Method and Description |
|---|---|
static double |
AndersonDarlingTest.removeBiasNormalDistribution(double A2,
int n)
Remove bias from the Anderson-Darling statistic if the mean and standard
deviation were estimated from the data, and a normal distribution was
assumed.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KMLOutputHandler
Class to handle KML output.
|
| Modifier and Type | Class and Description |
|---|---|
class |
IntegerArrayQuickSort
Class to sort an int array, using a modified quicksort.
|
| Modifier and Type | Class and Description |
|---|---|
class |
WeightedQuickUnionRangeDBIDs
Union-find algorithm for
DBIDRange only, with optimizations. |
class |
WeightedQuickUnionStaticDBIDs
Union-find algorithm for
StaticDBIDs, with optimizations. |
| Modifier and Type | Method and Description |
|---|---|
static Reference |
DocumentationUtil.getReference(Class<?> c)
Get the reference annotation of a class, or
null. |
| Modifier and Type | Class and Description |
|---|---|
class |
COPOutlierScaling
CDF based outlier score scaling.
|
class |
HeDESNormalizationOutlierScaling
Normalization used by HeDES
|
class |
MinusLogGammaScaling
Scaling that can map arbitrary values to a probability in the range of [0:1],
by assuming a Gamma distribution on the data and evaluating the Gamma CDF.
|
class |
MinusLogStandardDeviationScaling
Scaling that can map arbitrary values to a probability in the range of [0:1].
|
class |
MixtureModelOutlierScalingFunction
Tries to fit a mixture model (exponential for inliers and gaussian for
outliers) to the outlier score distribution.
|
class |
MultiplicativeInverseScaling
Scaling function to invert values basically by computing 1/x, but in a variation
that maps the values to the [0:1] interval and avoiding division by 0.
|
class |
OutlierGammaScaling
Scaling that can map arbitrary values to a probability in the range of [0:1]
by assuming a Gamma distribution on the values.
|
class |
OutlierMinusLogScaling
Scaling function to invert values by computing -1 * Math.log(x)
Useful for example for scaling
ABOD, but see
MinusLogStandardDeviationScaling and MinusLogGammaScaling for
more advanced scalings for this algorithm. |
class |
SigmoidOutlierScalingFunction
Tries to fit a sigmoid to the outlier scores and use it to convert the values
to probability estimates in the range of 0.0 to 1.0
Reference:
J.
|
class |
SqrtStandardDeviationScaling
Scaling that can map arbitrary values to a probability in the range of [0:1].
|
class |
StandardDeviationScaling
Scaling that can map arbitrary values to a probability in the range of [0:1].
|
| Modifier and Type | Method and Description |
|---|---|
static void |
COPOutlierScaling.secondReference()
Secondary reference.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CircleSegmentsVisualizer
Visualizer to draw circle segments of clusterings and enable interactive
selection of segments.
|
| Modifier and Type | Method and Description |
|---|---|
private double[] |
DensityEstimationOverlay.Instance.initializeBandwidth(double[][] data) |
| Modifier and Type | Class and Description |
|---|---|
class |
BubbleVisualization
Generates a SVG-Element containing bubbles.
|
class |
COPVectorVisualization
Visualize error vectors as produced by COP.
|
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