Package elki.clustering.kmeans.initialization.betula

Class CFRandomlyChosen

java.lang.Object

elki.clustering.kmeans.initialization.betula.AbstractCFKMeansInitialization

elki.clustering.kmeans.initialization.betula.CFRandomlyChosen

@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 CFRandomlyChosen
extends AbstractCFKMeansInitialization

Initialize K-means by randomly choosing k existing elements as initial cluster centers for Clustering Features. For normal k-means use RandomlyChosen.

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	CFRandomlyChosen.Par	Parameterization class.

Field Summary

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

rf

Constructor Summary

Constructors

Constructor	Description
CFRandomlyChosen(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 java.lang.Object

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

Constructor Detail

CFRandomlyChosen

public CFRandomlyChosen(RandomFactory rf)

Constructor.

Parameters:

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.

Specified by:

chooseInitialMeans in class AbstractCFKMeansInitialization

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