Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace |
Axis-parallel subspace clustering algorithms
The clustering algorithms in this package are instances of both, projected clustering algorithms or
subspace clustering algorithms according to the classical but somewhat obsolete classification schema
of clustering algorithms for axis-parallel subspaces.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace |
Distance functions based on subspaces.
|
Modifier and Type | Field and Description |
---|---|
protected DimensionSelectingSubspaceDistanceFunction<V> |
SUBCLU.Parameterizer.distance |
private DimensionSelectingSubspaceDistanceFunction<V> |
SUBCLU.distanceFunction
Holds the instance of the distance function specified by
SUBCLU.DISTANCE_FUNCTION_ID . |
Constructor and Description |
---|
SUBCLU(DimensionSelectingSubspaceDistanceFunction<V> distanceFunction,
double epsilon,
int minpts)
Constructor.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDimensionsSelectingDistanceFunction<V extends FeatureVector<?>>
Abstract base class for distances computed only in subspaces.
|
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. |
class |
SubspaceLPNormDistanceFunction
LP-Norm distance function between
NumberVector s only in specified
dimensions. |
class |
SubspaceManhattanDistanceFunction
Manhattan distance function between
NumberVector s only in specified
dimensions. |
class |
SubspaceMaximumDistanceFunction
Maximum distance function between
NumberVector s only in specified
dimensions. |
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.