@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.
R. C. Jancey
Multidimensional group analysis
Australian Journal of Botany 14(1)
|Modifier and Type||Class and Description|
|Constructor and Description|
|Modifier and Type||Method and Description|
Choose initial means
public RandomNormalGeneratedInitialMeans(RandomFactory rnd)
rnd- Random generator.
public double chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
database- Database context
k- Parameter k
distanceFunction- Distance function
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