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
The distance function to determine the distance between objects.
|
protected DimensionSelectingSubspaceDistanceFunction<V> |
SUBCLU.distanceFunction
The distance function to determine the distance between objects.
|
Constructor and Description |
---|
SUBCLU(DimensionSelectingSubspaceDistanceFunction<V> distanceFunction,
double epsilon,
int minpts,
int mindim)
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 © 2019 ELKI Development Team. License information.