Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization |
Initialization strategies for k-means.
|
Modifier and Type | Class and Description |
---|---|
class |
FarthestPointsInitialMeans<O>
K-Means initialization by repeatedly choosing the farthest point (by the
minimum distance to earlier points).
|
class |
FarthestSumPointsInitialMeans<O>
K-Means initialization by repeatedly choosing the farthest point (by the
sum of distances to previous objects).
|
class |
KMeansPlusPlusInitialMeans<O>
K-Means++ initialization for k-means.
|
class |
PredefinedInitialMeans
Run k-means with prespecified initial means.
|
class |
RandomlyChosenInitialMeans<O>
Initialize K-means by randomly choosing k existing elements as cluster
centers.
|
class |
RandomlyGeneratedInitialMeans
Initialize k-means by generating random vectors (within the data sets value
range).
|
class |
SampleKMeansInitialization<V extends NumberVector>
Initialize k-means by running k-means on a sample of the data set only.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.