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

All Known Implementing Classes:

AverageInterclusterDistance, AverageIntraclusterDistance, BIRCHAverageInterclusterDistance, BIRCHAverageIntraclusterDistance, BIRCHRadiusDistance, BIRCHVarianceIncreaseDistance, CentroidEuclideanDistance, CentroidManhattanDistance, RadiusDistance, VarianceIncreaseDistance
```
public interface CFDistance
```
Distance function for BIRCH clustering.
For performance we (usually, except Manhattan) use squared distances.

Since:

0.8.0

Author:

Erich Schubert

Method Summary

All Methods Instance Methods Abstract Methods Default Methods
Modifier and Type	Method	Description
`default double`	`matSelfInit(ClusterFeature cf)`	Initialization for self measure for new Combinatorial clustering Methods (Podani 1989)
`double`	`squaredDistance(NumberVector v, ClusterFeature cf)`	Distance of a vector to a clustering feature.
`double`	`squaredDistance(ClusterFeature c1, ClusterFeature c2)`	Distance between two clustering features.

- Method Detail
  - squaredDistance
```
double squaredDistance(NumberVector v,
                       ClusterFeature cf)
```
    Distance of a vector to a clustering feature.
    
    Parameters:
    
    v - Vector
    
    cf - Clustering Feature
    
    Returns:
    
    Distance
  - squaredDistance
```
double squaredDistance(ClusterFeature c1,
                       ClusterFeature c2)
```
    Distance between two clustering features.
    
    Parameters:
    
    c1 - First clustering feature
    
    c2 - Second clustering feature
    
    Returns:
    
    Distance
  - matSelfInit
```
default double matSelfInit(ClusterFeature cf)
```
    Initialization for self measure for new Combinatorial clustering Methods (Podani 1989)
    
    Parameters:
    
    cf - Clustering Feature
    
    Returns:
    
    internal measure