See: Description
| Interface | Description |
|---|---|
| KMeansInitialization |
Interface for initializing K-Means
|
| KMedoidsInitialization<V> |
Interface for initializing K-Medoids.
|
| Class | Description |
|---|---|
| AbstractKMeansInitialization |
Abstract base class for common k-means initializations.
|
| AbstractKMeansInitialization.Parameterizer |
Parameterization class.
|
| FarthestPointsInitialMeans<O> |
K-Means initialization by repeatedly choosing the farthest point (by the
minimum distance to earlier points).
|
| FarthestPointsInitialMeans.Parameterizer<O> |
Parameterization class.
|
| FarthestSumPointsInitialMeans<O> |
K-Means initialization by repeatedly choosing the farthest point (by the
sum of distances to previous objects).
|
| FarthestSumPointsInitialMeans.Parameterizer<V> |
Parameterization class.
|
| FirstKInitialMeans<O> |
Initialize K-means by using the first k objects as initial means.
|
| FirstKInitialMeans.Parameterizer<V extends NumberVector> |
Parameterization class.
|
| KMeansPlusPlusInitialMeans<O> |
K-Means++ initialization for k-means.
|
| KMeansPlusPlusInitialMeans.Parameterizer<V> |
Parameterization class.
|
| LABInitialMeans<O> |
Linear approximative BUILD (LAB) initialization for FastPAM (and k-means).
|
| LABInitialMeans.Parameterizer<V> |
Parameterization class.
|
| OstrovskyInitialMeans<O> |
Ostrovsky initial means, a variant of k-means++ that is expected to give
slightly better results on average, but only works for k-means and not for,
e.g., PAM (k-medoids).
|
| OstrovskyInitialMeans.Parameterizer<V> |
Parameterization class.
|
| PAMInitialMeans<O> |
PAM initialization for k-means (and of course, for PAM).
|
| PAMInitialMeans.Parameterizer<V> |
Parameterization class.
|
| ParkInitialMeans<O> |
Initialization method proposed by Park and Jun.
|
| ParkInitialMeans.Parameterizer<V> |
Parameterization class.
|
| PredefinedInitialMeans |
Run k-means with prespecified initial means.
|
| PredefinedInitialMeans.Parameterizer |
Parameterization class.
|
| RandomlyChosenInitialMeans<O> |
Initialize K-means by randomly choosing k existing elements as initial
cluster centers.
|
| RandomlyChosenInitialMeans.Parameterizer<V> |
Parameterization class.
|
| RandomNormalGeneratedInitialMeans |
Initialize k-means by generating random vectors (normal distributed
with \(N(\mu,\sigma)\) in each dimension).
|
| RandomNormalGeneratedInitialMeans.Parameterizer |
Parameterization class.
|
| RandomUniformGeneratedInitialMeans |
Initialize k-means by generating random vectors (uniform, within the value
range of the data set).
|
| RandomUniformGeneratedInitialMeans.Parameterizer |
Parameterization class.
|
| SampleKMeansInitialization<V extends NumberVector> |
Initialize k-means by running k-means on a sample of the data set only.
|
| SampleKMeansInitialization.Parameterizer<V extends NumberVector> |
Parameterization class.
|
Copyright © 2019 ELKI Development Team. License information.