Package elki.clustering.kmeans.initialization.betula

Class CFKPlusPlusTrunk

java.lang.Object

elki.clustering.kmeans.initialization.betula.AbstractCFKMeansInitialization

elki.clustering.kmeans.initialization.betula.CFKPlusPlusLeaves

elki.clustering.kmeans.initialization.betula.CFKPlusPlusTrunk

@Alias("trunk")
@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 CFKPlusPlusTrunk
extends CFKPlusPlusLeaves

Trunk strategy for initializing k-means with BETULA: only the nodes up to a particular level are considered for k-means++ style 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

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	CFKPlusPlusTrunk.Par	Parameterization class.

Field Summary

Fields inherited from class elki.clustering.kmeans.initialization.betula.CFKPlusPlusLeaves

distance, firstUniform

Fields inherited from class elki.clustering.kmeans.initialization.betula.AbstractCFKMeansInitialization

rf

Constructor Summary

Constructors

Constructor	Description
CFKPlusPlusTrunk(CFInitWeight dist, boolean firstUniform, RandomFactory rf)	Constructor.

Method Summary

Modifier and Type	Method	Description
double[][]	chooseInitialMeans(CFTree<?> tree, java.util.List<? extends ClusterFeature> cfs, int k)	Build the initial models.

Methods inherited from class elki.clustering.kmeans.initialization.betula.CFKPlusPlusLeaves

run

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

CFKPlusPlusTrunk

public CFKPlusPlusTrunk(CFInitWeight dist,
                        boolean firstUniform,
                        RandomFactory rf)

Constructor.

Parameters:

dist - distance function

firstUniform - choose the first center uniformly from leaves

rf - random generator

Method Detail

chooseInitialMeans

public double[][] chooseInitialMeans(CFTree<?> tree,
                                     java.util.List<? extends ClusterFeature> cfs,
                                     int k)

Description copied from class: AbstractCFKMeansInitialization

Build the initial models.

Overrides:

chooseInitialMeans in class CFKPlusPlusLeaves

Parameters:

tree - CF tree

cfs - Cluster features of the tree (may be ignored for tree-based initializations, should be an array list for efficiency)

k - Number of clusters.

Returns:

initial cluster means