Interface BIRCHDistance
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- 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
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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.
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Method Detail
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squaredDistance
double squaredDistance(NumberVector v, ClusteringFeature cf)
Distance of a vector to a clustering feature.- Parameters:
v
- Vectorcf
- Clustering Feature- Returns:
- Distance
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squaredDistance
double squaredDistance(ClusteringFeature c1, ClusteringFeature c2)
Distance between two clustering features.- Parameters:
c1
- First clustering featurec2
- Second clustering feature- Returns:
- Distance
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