Class InterclusterWeight
- java.lang.Object
-
- elki.clustering.kmeans.initialization.betula.InterclusterWeight
-
- All Implemented Interfaces:
CFInitWeight
@Reference(authors="Andreas Lang and Erich Schubert", title="BETULA: Fast Clustering of Large Data with Improved BIRCH CF-Trees", booktitle="Information Systems", url="https://doi.org/10.1016/j.is.2021.101918", bibkey="DBLP:journals/is/LangS22") public class InterclusterWeight extends java.lang.Object implements CFInitWeight
Initialization via n2 * D2²(cf1, cf2), which supposedly is closes to the idea of k-means++ initialization.References:
Andreas Lang and Erich Schubert
BETULA: Fast Clustering of Large Data with Improved BIRCH CF-Trees
Information Systems- Since:
- 0.8.0
- Author:
- Andreas Lang
-
-
Constructor Summary
Constructors Constructor Description InterclusterWeight()
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description double
squaredWeight(ClusterFeature existing, ClusterFeature candidate)
Distance between two clustering features.
-
-
-
Method Detail
-
squaredWeight
public double squaredWeight(ClusterFeature existing, ClusterFeature candidate)
Description copied from interface:CFInitWeight
Distance between two clustering features.- Specified by:
squaredWeight
in interfaceCFInitWeight
- Parameters:
existing
- Previously chosen clustering featurecandidate
- Candidate clustering feature- Returns:
- Weight
-
-