| 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 |
AGNES<O>
Hierarchical Agglomerative Clustering (HAC) or Agglomerative Nesting (AGNES)
is a classic hierarchical clustering algorithm.
|
class |
CentroidLinkageMethod
Centroid linkage clustering method, aka UPGMC: Unweighted Pair-Group Method
using Centroids.
|
class |
CLINK<O>
CLINK algorithm for complete linkage.
|
class |
CompleteLinkageMethod
Complete-linkage clustering method.
|
class |
GroupAverageLinkageMethod
Group-average linkage clustering method.
|
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 |
WardLinkageMethod
Ward's method clustering method.
|
class |
WeightedAverageLinkageMethod
Weighted average linkage clustering method.
|
| 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 |
OPTICSHeap<O>
The OPTICS algorithm for density-based hierarchical clustering.
|
class |
OPTICSXi
Class to handle OPTICS Xi extraction.
|
| 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 |
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 |
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 |
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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KDDCLIApplication
Basic command line application for Knowledge Discovery in Databases use
cases.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DropNaNFilter
A filter to drop all records that contain NaN values.
|
class |
NoMissingValuesFilter
A filter to remove entries that have missing values.
|
class |
ReplaceNaNWithRandomFilter
A filter to replace all NaN values.
|
class |
VectorDimensionalityFilter<V extends NumberVector>
Filter to remove all vectors that do not have the desired dimensionality.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AttributeWiseBetaNormalization<V extends NumberVector>
Project the data using a Beta distribution.
|
class |
AttributeWiseCDFNormalization<V extends NumberVector>
Class to perform and undo a normalization on real vectors by estimating the
distribution of values along each dimension independently, then rescaling
objects to the cumulative density function (CDF) value at the original
coordinate.
|
class |
AttributeWiseErfNormalization<V extends NumberVector>
Attribute-wise Normalization using the error function.
|
class |
AttributeWiseMADNormalization<V extends NumberVector>
Median Absolute Deviation is used for scaling the data set as follows:
First, the median, and median absolute deviation are computed in each axis.
|
class |
AttributeWiseMeanNormalization<V extends NumberVector>
Normalization designed for data with a meaningful zero: Each
attribute is scaled to have the same mean (but 0 is not changed).
|
class |
AttributeWiseMinMaxNormalization<V extends NumberVector>
Class to perform and undo a normalization on real vectors with respect to
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.
|
class |
IntegerRankTieNormalization
Normalize vectors according to their rank in the attributes.
|
class |
InverseDocumentFrequencyNormalization<V extends SparseNumberVector>
Normalization for text frequency (TF) vectors, using the inverse document
frequency (IDF).
|
| Modifier and Type | Class and Description |
|---|---|
class |
HellingerHistogramNormalization<V extends NumberVector>
Normalize histograms by scaling them to L1 norm 1, then taking the square
root in each attribute.
|
class |
LengthNormalization<V extends NumberVector>
Class to perform a normalization on vectors to norm 1.
|
class |
Log1PlusNormalization<V extends NumberVector>
Normalize the data set by applying log(1+|x|*b)/log(b+1) to any value.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ByLabelFilter
A filter to select data set by their label.
|
class |
RandomSamplingStreamFilter
Subsampling stream filter.
|
class |
ShuffleObjectsFilter
A filter to shuffle the dataset.
|
class |
SortByLabelFilter
A filter to sort the data set by some label.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ClassicMultidimensionalScalingTransform<O>
Rescale the data set using multidimensional scaling, MDS.
|
class |
FastMultidimensionalScalingTransform<O>
Rescale the data set using multidimensional scaling, MDS.
|
class |
GlobalPrincipalComponentAnalysisTransform<O extends NumberVector>
Apply principal component analysis to the data set.
|
class |
LinearDiscriminantAnalysisFilter<V extends NumberVector>
Linear Discriminant Analysis (LDA) / Fisher's linear discriminant.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ClassLabelFilter
Class that turns a label column into a class label column.
|
class |
ClassLabelFromPatternFilter
Streaming filter to derive an outlier class label.
|
class |
ExternalIDFilter
Class that turns a label column into an external ID column.
|
class |
SparseVectorFieldFilter<V extends SparseNumberVector>
Class that turns sparse float vectors into a proper vector field, by setting
the maximum dimensionality for each vector.
|
class |
SplitNumberVectorFilter<V extends NumberVector>
Split an existing column into two types.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ArcCosineDistanceFunction
Cosine distance function for feature vectors.
|
class |
BrayCurtisDistanceFunction
Bray-Curtis distance function / Sørensen–Dice coefficient for continuous
spaces.
|
class |
CanberraDistanceFunction
Canberra distance function, a variation of Manhattan distance.
|
class |
CosineDistanceFunction
Cosine distance function for feature vectors.
|
| Modifier and Type | Class and Description |
|---|---|
class |
EuclideanDistanceFunction
Euclidean distance for
NumberVectors. |
class |
LPNormDistanceFunction
LP-Norm for
NumberVectors. |
class |
ManhattanDistanceFunction
Manhattan distance for
NumberVectors. |
class |
MaximumDistanceFunction
Maximum distance for
NumberVectors. |
class |
MinimumDistanceFunction
Maximum distance for
NumberVectors. |
class |
SquaredEuclideanDistanceFunction
Squared Euclidean distance, optimized for
SparseNumberVectors. |
| 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 |
KullbackLeiblerDivergenceAsymmetricDistanceFunction
Kullback-Leibler (asymmetric!)
|
class |
KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction
Kullback-Leibler (asymmetric!)
|
| Modifier and Type | Class and Description |
|---|---|
class |
RadialBasisFunctionKernelFunction
Gaussian radial basis function kernel (RBF Kernel).
|
| Modifier and Type | Class and Description |
|---|---|
class |
OutlierROCCurve
Compute a ROC curve to evaluate a ranking algorithm and compute the
corresponding ROCAUC value.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MiniGUI
Minimal GUI built around a table-based parameter editor.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MultiStepGUI
Experimenter-style multi step GUI.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MTreeFactory<O>
Factory for a M-Tree
|
| Modifier and Type | Class and Description |
|---|---|
static class |
MinimalisticMemoryKDTree.Factory<O extends NumberVector>
Factory class
|
static class |
SmallMemoryKDTree.Factory<O extends NumberVector>
Factory class
|
| Modifier and Type | Class and Description |
|---|---|
class |
RStarTreeFactory<O extends NumberVector>
Factory for regular R*-Trees.
|
| Modifier and Type | Class and Description |
|---|---|
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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Clarke1858SpheroidEarthModel
The Clarke 1858 spheroid earth model.
|
class |
Clarke1880SpheroidEarthModel
The Clarke 1880 spheroid earth model.
|
class |
GRS67SpheroidEarthModel
The GRS 67 spheroid earth model.
|
class |
GRS80SpheroidEarthModel
The GRS 80 spheroid earth model, without height model (so not a geoid, just a
spheroid!)
|
class |
WGS72SpheroidEarthModel
The WGS72 spheroid earth model, without height model.
|
class |
WGS84SpheroidEarthModel
The WGS84 spheroid earth model, without height model (so not a geoid, just a
spheroid!)
|
| Modifier and Type | Class and Description |
|---|---|
class |
ExponentiallyModifiedGaussianDistribution
Exponentially modified Gaussian (EMG) distribution (ExGaussian distribution)
is a combination of a normal distribution and an exponential distribution.
|
class |
LaplaceDistribution
Laplace distribution also known as double exponential distribution
|
class |
LogisticDistribution
Logistic distribution.
|
class |
LogLogisticDistribution
Log-Logistic distribution also known as Fisk distribution.
|
class |
LogNormalDistribution
Log-Normal distribution.
|
class |
NormalDistribution
Gaussian distribution aka normal distribution
|
class |
WaldDistribution
Inverse Gaussian distribution aka Wald distribution
|
| Modifier and Type | Class and Description |
|---|---|
class |
BiweightKernelDensityFunction
Biweight (Quartic) kernel density estimator.
|
class |
EpanechnikovKernelDensityFunction
Epanechnikov kernel density estimator.
|
class |
TriweightKernelDensityFunction
Triweight kernel density estimator.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ResultVisualizer
Handler to process and visualize a Result.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.