Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.clustering.em |
Expectation-Maximization clustering algorithm.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization |
Initialization strategies for k-means.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel |
Parallelized implementations of k-means.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain |
Clustering algorithms for uncertain data.
|
Modifier and Type | Field and Description |
---|---|
protected KMeansInitialization<V> |
AbstractEMModelFactory.initializer
Class to choose the initial means
|
protected KMeansInitialization<V> |
AbstractEMModelFactory.Parameterizer.initializer
Initialization method
|
Constructor and Description |
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AbstractEMModelFactory(KMeansInitialization<V> initializer)
Constructor.
|
DiagonalGaussianModelFactory(KMeansInitialization<V> initializer)
Constructor.
|
MultivariateGaussianModelFactory(KMeansInitialization<V> initializer)
Constructor.
|
SphericalGaussianModelFactory(KMeansInitialization<V> initializer)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
protected KMeansInitialization<? super V> |
AbstractKMeans.initializer
Method to choose initial means.
|
protected KMeansInitialization<V> |
AbstractKMeans.Parameterizer.initializer
Initialization method.
|
Constructor and Description |
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AbstractKMeans(NumberVectorDistanceFunction<? super V> distanceFunction,
int k,
int maxiter,
KMeansInitialization<? super V> initializer)
Constructor.
|
KMeansBatchedLloyd(NumberVectorDistanceFunction<? super V> distanceFunction,
int k,
int maxiter,
KMeansInitialization<? super V> initializer,
int blocks,
RandomFactory random)
Constructor.
|
KMeansCompare(NumberVectorDistanceFunction<? super V> distanceFunction,
int k,
int maxiter,
KMeansInitialization<? super V> initializer)
Constructor.
|
KMeansElkan(NumberVectorDistanceFunction<? super V> distanceFunction,
int k,
int maxiter,
KMeansInitialization<? super V> initializer,
boolean varstat)
Constructor.
|
KMeansHamerly(NumberVectorDistanceFunction<? super V> distanceFunction,
int k,
int maxiter,
KMeansInitialization<? super V> initializer,
boolean varstat)
Constructor.
|
KMeansHybridLloydMacQueen(NumberVectorDistanceFunction<? super V> distanceFunction,
int k,
int maxiter,
KMeansInitialization<? super V> initializer)
Constructor.
|
KMeansLloyd(NumberVectorDistanceFunction<? super V> distanceFunction,
int k,
int maxiter,
KMeansInitialization<? super V> initializer)
Constructor.
|
KMeansMacQueen(NumberVectorDistanceFunction<? super V> distanceFunction,
int k,
int maxiter,
KMeansInitialization<? super V> initializer)
Constructor.
|
KMeansSort(NumberVectorDistanceFunction<? super V> distanceFunction,
int k,
int maxiter,
KMeansInitialization<? super V> initializer)
Constructor.
|
KMediansLloyd(NumberVectorDistanceFunction<? super V> distanceFunction,
int k,
int maxiter,
KMeansInitialization<? super V> initializer)
Constructor.
|
SingleAssignmentKMeans(NumberVectorDistanceFunction<? super V> distanceFunction,
int k,
KMeansInitialization<? super V> initializer)
Constructor.
|
XMeans(NumberVectorDistanceFunction<? super V> distanceFunction,
int k_min,
int k_max,
int maxiter,
KMeans<V,M> innerKMeans,
KMeansInitialization<? super V> initializer,
PredefinedInitialMeans splitInitializer,
KMeansQualityMeasure<V> informationCriterion,
RandomFactory random)
Constructor.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractKMeansInitialization<V extends NumberVector>
Abstract base class for common k-means initializations.
|
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 |
FirstKInitialMeans<O>
Initialize K-means by using the first k objects as initial means.
|
class |
KMeansPlusPlusInitialMeans<O>
K-Means++ initialization for k-means.
|
class |
PAMInitialMeans<O>
PAM initialization for k-means (and of course, PAM).
|
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.
|
Constructor and Description |
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ParallelLloydKMeans(NumberVectorDistanceFunction<? super V> distanceFunction,
int k,
int maxiter,
KMeansInitialization<? super V> initializer)
Constructor.
|
Constructor and Description |
---|
CKMeans(NumberVectorDistanceFunction<? super NumberVector> distanceFunction,
int k,
int maxiter,
KMeansInitialization<? super NumberVector> initializer)
Constructor that uses Lloyd's k-means algorithm.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.