BIRCHDistance (ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures)

All Known Implementing Classes:

AverageInterclusterDistance, AverageIntraclusterDistance, CentroidEuclideanDistance, CentroidManhattanDistance, VarianceIncreaseDistance
```
public interface BIRCHDistance
```
Distance function for BIRCH clustering. For performance we (usually) use squared distances. The exception to this rule is Manhattan.

Since:

0.7.5

Author:

Erich Schubert

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method	Description
`double`	`squaredDistance(ClusteringFeature c1, ClusteringFeature c2)`	Distance between two clustering features.
`double`	`squaredDistance(NumberVector v, ClusteringFeature cf)`	Distance of a vector to a clustering feature.

- Method Detail
  - squaredDistance
```
double squaredDistance(NumberVector v,
                       ClusteringFeature cf)
```
    Distance of a vector to a clustering feature.
    
    Parameters:
    
    v - Vector
    
    cf - Clustering Feature
    
    Returns:
    
    Distance
  - squaredDistance
```
double squaredDistance(ClusteringFeature c1,
                       ClusteringFeature c2)
```
    Distance between two clustering features.
    
    Parameters:
    
    c1 - First clustering feature
    
    c2 - Second clustering feature
    
    Returns:
    
    Distance