de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch

Interface BIRCHDistance

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

Modifier and Type	Method and 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