@Priority(value=-101) @Alias(value={"de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.RandomlyGeneratedInitialMeans","de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.RandomlyGeneratedInitialMeans"}) @Reference(authors="R. C. Jancey", title="Multidimensional group analysis", booktitle="Australian Journal of Botany 14(1)", url="https://doi.org/10.1071/BT9660127", bibkey="doi:10.1071/BT9660127") public class RandomUniformGeneratedInitialMeans extends AbstractKMeansInitialization
This is attributed to Jancey, but who wrote little more details but
"introduced into known but arbitrary positions". This class assumes this
refers to uniform positions within the value domain. For a normal distributed
variant, see RandomNormalGeneratedInitialMeans
.
Warning: this tends to produce empty clusters, and is one of the least effective initialization strategies, not recommended for use.
Reference:
R. C. Jancey
Multidimensional group analysis
Australian Journal of Botany 14(1)
Modifier and Type | Class and Description |
---|---|
static class |
RandomUniformGeneratedInitialMeans.Parameterizer
Parameterization class.
|
rnd
Constructor and Description |
---|
RandomUniformGeneratedInitialMeans(RandomFactory rnd)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
double[][] |
chooseInitialMeans(Database database,
Relation<? extends NumberVector> relation,
int k,
NumberVectorDistanceFunction<?> distanceFunction)
Choose initial means
|
unboxVectors
public RandomUniformGeneratedInitialMeans(RandomFactory rnd)
rnd
- Random generator.public double[][] chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
KMeansInitialization
database
- Database contextrelation
- Relationk
- Parameter kdistanceFunction
- Distance functionCopyright © 2019 ELKI Development Team. License information.