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.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.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.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.anglebased |
Angle-based outlier detection algorithms.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering |
Clustering based outlier detection.
|
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.subspace |
Subspace outlier detection methods.
|
de.lmu.ifi.dbs.elki.application |
Base classes for stand alone applications.
|
de.lmu.ifi.dbs.elki.datasource |
Data normalization (and reconstitution) of data sets.
|
de.lmu.ifi.dbs.elki.datasource.filter.cleaning |
Filters for data cleaning.
|
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.normalization.instancewise |
Instancewise normalization, where each instance is normalized independently.
|
de.lmu.ifi.dbs.elki.datasource.filter.selection |
Filters for selecting and sorting data to process.
|
de.lmu.ifi.dbs.elki.datasource.filter.transform |
Data space transformations.
|
de.lmu.ifi.dbs.elki.datasource.filter.typeconversions |
Filters to perform data type conversions.
|
de.lmu.ifi.dbs.elki.datasource.parser |
Parsers for different file formats and data types.
|
de.lmu.ifi.dbs.elki.distance.distancefunction |
Distance functions for use within ELKI.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.adapter |
Distance functions deriving distances from e.g. similarity measures
|
de.lmu.ifi.dbs.elki.distance.distancefunction.external |
Distance functions using external data sources.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski |
Minkowski space L_p 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.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.set |
Distance functions for binary and set type data.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace |
Distance functions based on subspaces.
|
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel |
Kernel functions.
|
de.lmu.ifi.dbs.elki.evaluation.clustering |
Evaluation of clustering results.
|
de.lmu.ifi.dbs.elki.evaluation.outlier |
Evaluate an outlier score using a misclassification based cost model.
|
de.lmu.ifi.dbs.elki.gui.minigui |
A very simple UI to build ELKI command lines.
|
de.lmu.ifi.dbs.elki.gui.multistep |
Multi-step GUI for ELKI
|
de.lmu.ifi.dbs.elki.index.preprocessed.knn |
Indexes providing KNN and rKNN data.
|
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree | |
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.index.tree.spatial.kd |
K-d-tree and variants.
|
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar | |
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk |
Packages for bulk-loading R*-Trees.
|
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split |
Splitting strategies for R-Trees
|
de.lmu.ifi.dbs.elki.math.geodesy | |
de.lmu.ifi.dbs.elki.math.statistics.distribution |
Standard distributions, with random generation functionalities.
|
de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions |
Kernel functions from statistics.
|
de.lmu.ifi.dbs.elki.result |
Result types, representation and handling
|
Modifier and Type | Class and Description |
---|---|
class |
KNNDistancesSampler<O>
Provides an order of the kNN-distances for all objects within the database.
|
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 |
ExtractFlatClusteringFromHierarchy
Extract a flat clustering from a full hierarchy, represented in pointer form.
|
Modifier and Type | Class and Description |
---|---|
class |
KMeansLloyd<V extends NumberVector>
The standard k-means algorithm, using Lloyd-style bulk iterations.
|
Modifier and Type | Class and Description |
---|---|
class |
FarthestPointsInitialMeans<O>
K-Means initialization by repeatedly choosing the farthest point (by the
minimum distance to earlier points).
|
class |
FirstKInitialMeans<O>
Initialize K-means by using the first k objects as initial means.
|
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.
|
class |
RandomlyGeneratedInitialMeans
Initialize k-means by generating random vectors (within the data sets value
range).
|
class |
SampleKMeansInitialization<V extends NumberVector>
Initialize k-means by running k-means on a sample of the data set only.
|
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 |
ByLabelClustering
Pseudo clustering using labels.
|
class |
ByLabelHierarchicalClustering
Pseudo clustering using labels.
|
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.
|
Modifier and Type | Class and Description |
---|---|
class |
APRIORI
The APRIORI algorithm for Mining Association Rules.
|
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 |
EMOutlier<V extends NumberVector>
outlier detection algorithm using EM Clustering.
|
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 |
ALOCI<O extends NumberVector>
Fast Outlier Detection Using the "approximate Local Correlation Integral".
|
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 |
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 |
OnlineLOF<O>
Incremental version of the
LOF Algorithm, supports insertions and
removals. |
class |
SimpleKernelDensityLOF<O extends NumberVector>
A simple variant of the LOF algorithm, which uses a simple kernel density
estimation instead of the local reachability density.
|
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 |
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 |
SOD<V extends NumberVector>
Subspace Outlier Degree.
|
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 |
EmptyDatabaseConnection
Pseudo database that is empty.
|
class |
FileBasedDatabaseConnection
File based database connection based on the parser to be set.
|
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.
|
Modifier and Type | Class and Description |
---|---|
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 |
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 |
LengthNormalization<V extends NumberVector>
Class to perform a normalization on vectors to norm 1.
|
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 |
HistogramJitterFilter<V extends NumberVector>
Add Jitter, preserving the histogram properties (same sum, nonnegative).
|
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 |
BitVectorLabelParser
Parser for parsing one BitVector per line, bits separated by whitespace.
|
class |
NumberVectorLabelParser<V extends NumberVector>
Parser for a simple CSV type of format, with columns separated by the given
pattern (default: whitespace).
|
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 |
ArccosSimilarityAdapter<O>
Adapter from a normalized similarity function to a distance function using
arccos(sim) . |
class |
LinearAdapterLinear<O>
Adapter from a normalized similarity function to a distance function using
1 - sim . |
class |
LnSimilarityAdapter<O>
Adapter from a normalized similarity function to a distance function using
-log(sim) . |
Modifier and Type | Class and Description |
---|---|
class |
AsciiDistanceParser
Parser for parsing one distance value per line.
|
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 |
JaccardSimilarityDistanceFunction<O extends FeatureVector<?>>
A flexible extension of Jaccard similarity to non-binary vectors.
|
Modifier and Type | Class and Description |
---|---|
class |
OnedimensionalDistanceFunction
Distance function that computes the distance between feature vectors as the
absolute difference of their values in a specified dimension only.
|
class |
SubspaceEuclideanDistanceFunction
Euclidean distance function between
NumberVector s only in specified
dimensions. |
Modifier and Type | Class and Description |
---|---|
class |
RadialBasisFunctionKernelFunction
Gaussian radial basis function kernel (RBF Kernel).
|
class |
SigmoidKernelFunction
Sigmoid kernel function (aka: hyperbolic tangent kernel, multilayer
perceptron MLP kernel).
|
Modifier and Type | Class and Description |
---|---|
class |
EvaluateClustering
Evaluate a clustering result by comparing it to an existing cluster label.
|
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 |
MaterializeKNNPreprocessor<O>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
class |
SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
Modifier and Type | Class and Description |
---|---|
class |
MTreeFactory<O>
Factory for a 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 |
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.
|
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 |
MaxExtensionBulkSplit
Split strategy for bulk-loading a spatial tree where the split axes are the
dimensions with maximum extension.
|
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 |
TopologicalSplitter
Encapsulates the required parameters for a topological split of a R*-Tree.
|
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 |
GammaDistribution
Gamma Distribution, with random generation and density functions.
|
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 |
UniformDistribution
Uniform 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 |
AutomaticVisualization
Handler to process and visualize a Result.
|
class |
ExportVisualizations
Class that automatically generates all visualizations and exports them into
SVG files.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.