O
- Object type for KMedoids@Alias(value="de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.FirstKInitialMeans") @Reference(authors="J. MacQueen", title="Some Methods for Classification and Analysis of Multivariate Observations", booktitle="5th Berkeley Symp. Math. Statist. Prob.", url="http://projecteuclid.org/euclid.bsmsp/1200512992", bibkey="conf/bsmsp/MacQueen67") public class FirstKInitialMeans<O> extends java.lang.Object implements KMeansInitialization, KMedoidsInitialization<O>
Reference:
J. MacQueen
Some Methods for Classification and Analysis of Multivariate Observations
5th Berkeley Symp. Math. Statist. Prob.
Modifier and Type | Class and Description |
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static class |
FirstKInitialMeans.Parameterizer<V extends NumberVector>
Parameterization class.
|
Constructor and Description |
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FirstKInitialMeans()
Constructor.
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Modifier and Type | Method and Description |
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double[][] |
chooseInitialMeans(Database database,
Relation<? extends NumberVector> relation,
int k,
NumberVectorDistanceFunction<?> distanceFunction)
Choose initial means
|
DBIDs |
chooseInitialMedoids(int k,
DBIDs ids,
DistanceQuery<? super O> distanceFunction)
Choose initial means
|
public double[][] chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
KMeansInitialization
chooseInitialMeans
in interface KMeansInitialization
database
- Database contextrelation
- Relationk
- Parameter kdistanceFunction
- Distance functionpublic DBIDs chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distanceFunction)
KMedoidsInitialization
chooseInitialMedoids
in interface KMedoidsInitialization<O>
k
- Parameter kids
- Candidate IDs.distanceFunction
- Distance functionCopyright © 2019 ELKI Development Team. License information.