| Package | Description | 
|---|---|
| de.lmu.ifi.dbs.elki.distance.distancefunction | Distance functions for use within ELKI. | 
| de.lmu.ifi.dbs.elki.distance.distancefunction.correlation | Distance functions using correlations. | 
| de.lmu.ifi.dbs.elki.distance.distancefunction.subspace | Distance functions based on subspaces. | 
| Modifier and Type | Interface and Description | 
|---|---|
| interface  | FilteredLocalPCABasedDistanceFunction<O extends NumberVector<?>,P extends FilteredLocalPCAIndex<? super O>,D extends Distance<D>>Interface for local PCA based preprocessors. | 
| Modifier and Type | Class and Description | 
|---|---|
| class  | AbstractIndexBasedDistanceFunction<O,I extends Index,D extends Distance<D>>Abstract super class for distance functions needing a database index. | 
| class  | LocallyWeightedDistanceFunction<V extends NumberVector<?>>Provides a locally weighted distance function. | 
| class  | SharedNearestNeighborJaccardDistanceFunction<O>SharedNearestNeighborJaccardDistanceFunction computes the Jaccard
 coefficient, which is a proper distance metric. | 
| Modifier and Type | Class and Description | 
|---|---|
| class  | ERiCDistanceFunctionProvides a distance function for building the hierarchy in the ERiC
 algorithm. | 
| class  | PCABasedCorrelationDistanceFunctionProvides the correlation distance for real valued vectors. | 
| Modifier and Type | Class and Description | 
|---|---|
| class  | AbstractPreferenceVectorBasedCorrelationDistanceFunction<V extends NumberVector<?>,P extends PreferenceVectorIndex<V>>Abstract super class for all preference vector based correlation distance
 functions. | 
| class  | DiSHDistanceFunctionDistance function used in the DiSH algorithm. | 
| class  | HiSCDistanceFunction<V extends NumberVector<?>>Distance function used in the HiSC algorithm. | 
| class  | LocalSubspaceDistanceFunctionProvides a distance function to determine a kind of correlation distance
 between two points, which is a pair consisting of the distance between the
 two subspaces spanned by the strong eigenvectors of the two points and the
 affine distance between the two subspaces. |