Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.clustering |
Clustering algorithms.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace |
Subspace outlier detection methods.
|
de.lmu.ifi.dbs.elki.application.greedyensemble |
Greedy ensembles for outlier detection.
|
de.lmu.ifi.dbs.elki.database.query.knn |
Prepared queries for k nearest neighbor (kNN) queries.
|
de.lmu.ifi.dbs.elki.distance.distancefunction |
Distance functions for use within ELKI.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram |
Distance functions using correlations.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.correlation |
Distance functions using correlations.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.geo |
Geographic (earth) distance functions.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace |
Distance functions based on subspaces.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries |
Distance functions designed for time series.
|
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query |
Classes for performing queries (knn, range, ...) on metrical trees.
|
tutorial.clustering |
Classes from the tutorial on implementing a custom k-means variation.
|
tutorial.distancefunction |
Classes from the tutorial on implementing distance functions.
|
Modifier and Type | Method and Description |
---|---|
private void |
SLINK.step2double(DBIDRef id,
DBIDs processedIDs,
Relation<? extends O> relation,
PrimitiveDoubleDistanceFunction<? super O> distFunc,
WritableDoubleDistanceDataStore m)
Second step: Determine the pairwise distances from all objects in the
pointer representation to the new object with the specified id.
|
Modifier and Type | Method and Description |
---|---|
protected double |
KMeansPlusPlusInitialMeans.updateWeights(double[] weights,
ArrayDBIDs ids,
DBID latest,
PrimitiveDoubleDistanceFunction<V> distF,
Relation<V> rel)
Update the weight list.
|
Modifier and Type | Method and Description |
---|---|
private DoubleDistanceDBIDList |
OUTRES.subsetNeighborhoodQuery(DistanceDBIDResult<DoubleDistance> neighc,
DBIDRef dbid,
PrimitiveDoubleDistanceFunction<? super V> df,
double adjustedEps,
OUTRES.KernelDensityEstimator kernel)
Refine neighbors within a subset.
|
Modifier and Type | Method and Description |
---|---|
private PrimitiveDoubleDistanceFunction<NumberVector<?>> |
GreedyEnsembleExperiment.getDistanceFunction(double[] estimated_weights) |
Modifier and Type | Field and Description |
---|---|
(package private) PrimitiveDoubleDistanceFunction<O> |
LinearScanRawDoubleDistanceKNNQuery.rawdist
Raw distance function.
|
Modifier and Type | Interface and Description |
---|---|
interface |
DoubleNorm<O>
Interface for norms in the double domain.
|
interface |
SpatialPrimitiveDoubleDistanceFunction<V extends SpatialComparable>
Interface combining spatial primitive distance functions with primitive
number distance functions.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractVectorDoubleDistanceFunction
Abstract base class for the most common family of distance functions: defined
on number vectors and returning double values.
|
class |
AbstractVectorDoubleDistanceNorm
Abstract base class for double-valued number-vector-based distances based on
norms.
|
class |
ArcCosineDistanceFunction
Cosine distance function for feature vectors.
|
class |
CanberraDistanceFunction
Canberra distance function, a variation of Manhattan distance.
|
class |
CosineDistanceFunction
Cosine distance function for feature vectors.
|
class |
EuclideanDistanceFunction
Provides the Euclidean distance for FeatureVectors.
|
class |
JeffreyDivergenceDistanceFunction
Provides the Jeffrey Divergence Distance for FeatureVectors.
|
class |
LPNormDistanceFunction
Provides a LP-Norm for FeatureVectors.
|
class |
ManhattanDistanceFunction
Manhattan distance function to compute the Manhattan distance for a pair of
FeatureVectors.
|
class |
MaximumDistanceFunction
Maximum distance function to compute the Maximum distance for a pair of
FeatureVectors.
|
class |
MinimumDistanceFunction
Maximum distance function to compute the Minimum distance for a pair of
FeatureVectors.
|
class |
SparseEuclideanDistanceFunction
Euclidean distance function.
|
class |
SparseLPNormDistanceFunction
Provides a LP-Norm for FeatureVectors.
|
class |
SparseManhattanDistanceFunction
Manhattan distance function.
|
class |
SparseMaximumDistanceFunction
Maximum distance function.
|
class |
SquaredEuclideanDistanceFunction
Provides the squared Euclidean distance for FeatureVectors.
|
class |
WeightedDistanceFunction
Provides the Weighted distance for feature vectors.
|
class |
WeightedLPNormDistanceFunction
Weighted version of the Euclidean distance function.
|
class |
WeightedSquaredEuclideanDistanceFunction
Provides the squared Euclidean distance for FeatureVectors.
|
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 | Class and Description |
---|---|
class |
PearsonCorrelationDistanceFunction
Pearson correlation distance function for feature vectors.
|
class |
SquaredPearsonCorrelationDistanceFunction
Squared Pearson correlation distance function for feature vectors.
|
class |
WeightedPearsonCorrelationDistanceFunction
Pearson correlation distance function for feature vectors.
|
class |
WeightedSquaredPearsonCorrelationDistanceFunction
Squared Pearson correlation distance function for feature vectors.
|
Modifier and Type | Class and Description |
---|---|
class |
DimensionSelectingLatLngDistanceFunction
Distance function for 2D vectors in Latitude, Longitude form.
|
class |
LatLngDistanceFunction
Distance function for 2D vectors in Latitude, Longitude form.
|
class |
LngLatDistanceFunction
Distance function for 2D vectors in Longitude, Latitude form.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDimensionsSelectingDoubleDistanceFunction<V extends FeatureVector<?>>
Provides a distance function that computes the distance (which is a double
distance) between feature vectors only in specified dimensions.
|
class |
DimensionSelectingDistanceFunction
Provides a distance function that computes the distance between feature
vectors as the absolute difference of their values in a specified dimension.
|
class |
SubspaceEuclideanDistanceFunction
Provides a distance function that computes the Euclidean distance between
feature vectors only in specified dimensions.
|
class |
SubspaceLPNormDistanceFunction
Provides a distance function that computes the Euclidean distance between
feature vectors only in specified dimensions.
|
class |
SubspaceManhattanDistanceFunction
Provides a distance function that computes the Euclidean distance between
feature vectors only in specified dimensions.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractEditDistanceFunction
Provides the Edit Distance for FeatureVectors.
|
class |
DTWDistanceFunction
Provides the Dynamic Time Warping distance for FeatureVectors.
|
class |
EDRDistanceFunction
Provides the Edit Distance on Real Sequence distance for FeatureVectors.
|
class |
ERPDistanceFunction
Provides the Edit Distance With Real Penalty distance for FeatureVectors.
|
class |
LCSSDistanceFunction
Provides the Longest Common Subsequence distance for FeatureVectors.
|
Modifier and Type | Field and Description |
---|---|
protected PrimitiveDoubleDistanceFunction<? super O> |
DoubleDistanceMetricalIndexRangeQuery.distf
Distance function
|
protected PrimitiveDoubleDistanceFunction<? super O> |
DoubleDistanceMetricalIndexKNNQuery.distf
Distance function
|
Constructor and Description |
---|
DoubleDistanceMetricalIndexKNNQuery(AbstractMTree<O,DoubleDistance,?,?> index,
DistanceQuery<O,DoubleDistance> distanceQuery,
PrimitiveDoubleDistanceFunction<? super O> distf)
Constructor.
|
DoubleDistanceMetricalIndexRangeQuery(AbstractMTree<O,DoubleDistance,?,?> index,
DistanceQuery<O,DoubleDistance> distanceQuery,
PrimitiveDoubleDistanceFunction<? super O> distf)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
protected PrimitiveDoubleDistanceFunction<? super NumberVector<?>> |
SameSizeKMeansAlgorithm.Parameterizer.distanceFunction
Distance function
|
Modifier and Type | Method and Description |
---|---|
protected void |
SameSizeKMeansAlgorithm.updateDistances(Relation<V> relation,
List<? extends NumberVector<?>> means,
WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas,
PrimitiveDoubleDistanceFunction<NumberVector<?>> df)
Compute the distances of each object to all means.
|
Constructor and Description |
---|
SameSizeKMeansAlgorithm(PrimitiveDoubleDistanceFunction<? super NumberVector<?>> distanceFunction,
int k,
int maxiter,
KMeansInitialization<V> initializer)
Constructor.
|
Modifier and Type | Class and Description |
---|---|
class |
MultiLPNorm
Tutorial example for ELKI.
|
class |
TutorialDistanceFunction
Tutorial example for ELKI.
|