@Priority(value=-101) @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 RandomNormalGeneratedInitialMeans extends AbstractKMeansInitialization
This is a different interpretation of the work of Jancey, who wrote little
more details but "introduced into known but arbitrary positions"; but
seemingly worked with standardized scores. In contrast to
RandomUniformGeneratedInitialMeans
(which uses a uniform on the entire
value range), this class uses a normal distribution based on the estimated
parameters. The resulting means should be more central, and thus a bit less
likely to become empty (at least if you assume there is no correlation
amongst attributes... it is still not competitive with better methods).
Warning: this still tends to produce empty clusters in many situations, 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 |
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static class |
RandomNormalGeneratedInitialMeans.Parameterizer
Parameterization class.
|
rnd
Constructor and Description |
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RandomNormalGeneratedInitialMeans(RandomFactory rnd)
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
|
unboxVectors
public RandomNormalGeneratedInitialMeans(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.