Environment for
DeveLoping
KDD-Applications
Supported by Index-Structures

Package de.lmu.ifi.dbs.elki.distance.distancefunction.subspace

Distance functions based on subspaces.

See:
          Description

Class Summary
AbstractDimensionsSelectingDoubleDistanceFunction<V extends FeatureVector<V,?>> Provides a distance function that computes the distance (which is a double distance) between feature vectors only in specified dimensions.
AbstractPreferenceVectorBasedCorrelationDistanceFunction<V extends NumberVector<V,?>,P extends PreferenceVectorPreprocessor<V>> Abstract super class for all preference vector based correlation distance functions.
DimensionSelectingDistanceFunction<V extends NumberVector<V,?>> Provides a distance function that computes the distance between feature vectors as the absolute difference of their values in a specified dimension.
DimensionsSelectingEuclideanDistanceFunction<V extends NumberVector<V,?>> Provides a distance function that computes the Euclidean distance between feature vectors only in specified dimensions.
DiSHDistanceFunction<V extends NumberVector<V,?>,P extends PreferenceVectorPreprocessor<V>> Distance function used in the DiSH algorithm.
HiSCDistanceFunction<V extends NumberVector<V,?>,P extends PreferenceVectorPreprocessor<V>> Distance function used in the HiSC algorithm.
SubspaceDistanceFunction<V extends NumberVector<V,?>,P extends LocalPCAPreprocessor<V>> Provides 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.
 

Package de.lmu.ifi.dbs.elki.distance.distancefunction.subspace Description

Distance functions based on subspaces.


Release 0.3 (2010-03-31_1612)