| 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  
NumberVectors only in specified
 dimensions. | 
class  | 
SubspaceLPNormDistanceFunction
Lp-Norm distance function between  
NumberVectors only in
 specified dimensions. | 
class  | 
SubspaceManhattanDistanceFunction
Manhattan distance function between  
NumberVectors only in specified
 dimensions. | 
class  | 
SubspaceMaximumDistanceFunction
Maximum distance function between  
NumberVectors only in specified
 dimensions. | 
Copyright © 2019 ELKI Development Team. License information.