See: Description
Class  Description 

ByteKMeans 
Fast, parallel implementation of the KMeans algorithm with support for
biggerthanmemory data.

ByteKMeans.Result 
Result object for ByteKMeans, extending ByteCentroidsResult and ByteNearestNeighboursProvider,
as well as giving access to state information from the operation of the KMeans algorithm
(i.e.

ByteKMeansInit 
Initialisation for KMeans clustering.

ByteKMeansInit.RANDOM 
Simple kmeans initialized on randomly selected samples.

DoubleKMeans 
Fast, parallel implementation of the KMeans algorithm with support for
biggerthanmemory data.

DoubleKMeans.Result 
Result object for DoubleKMeans, extending DoubleCentroidsResult and DoubleNearestNeighboursProvider,
as well as giving access to state information from the operation of the KMeans algorithm
(i.e.

DoubleKMeansInit 
Initialisation for KMeans clustering.

DoubleKMeansInit.RANDOM 
Simple kmeans initialized on randomly selected samples.

FeatureVectorKMeans<T extends FeatureVector> 
Fast, parallel implementation of the KMeans algorithm with support for
biggerthanmemory data.

FeatureVectorKMeans.Result<T extends FeatureVector> 
Result object for FeatureVectorKMeans, extending
FeatureVectorCentroidsResult and ObjectNearestNeighboursProvider, as well
as giving access to state information from the operation of the KMeans
algorithm (i.e.

FeatureVectorKMeansInit<T> 
Initialisation for KMeans clustering.

FeatureVectorKMeansInit.RANDOM<T> 
Simple kmeans initialized on randomly selected samples.

FloatKMeans 
Fast, parallel implementation of the KMeans algorithm with support for
biggerthanmemory data.

FloatKMeans.Result 
Result object for FloatKMeans, extending FloatCentroidsResult and FloatNearestNeighboursProvider,
as well as giving access to state information from the operation of the KMeans algorithm
(i.e.

FloatKMeansInit 
Initialisation for KMeans clustering.

FloatKMeansInit.RANDOM 
Simple kmeans initialized on randomly selected samples.

HierarchicalByteKMeans 
Hierarchical Byte KMeans clustering (
HierarchicalByteKMeans ) is a simple
hierarchical version of ByteKMeans. 
HierarchicalByteKMeansResult 
The result of a
HierarchicalByteKMeans clustering operation. 
HierarchicalByteKMeansResult.Node 
HierarchicalByteKMeans tree node
The number of children is not bigger than the HierarchicalByteKMeans K parameter

HierarchicalDoubleKMeans 
Hierarchical Double KMeans clustering (
HierarchicalDoubleKMeans ) is a simple
hierarchical version of DoubleKMeans. 
HierarchicalDoubleKMeansResult 
The result of a
HierarchicalDoubleKMeans clustering operation. 
HierarchicalDoubleKMeansResult.Node 
HierarchicalDoubleKMeans tree node
The number of children is not bigger than the HierarchicalDoubleKMeans K parameter

HierarchicalFloatKMeans 
Hierarchical Float KMeans clustering (
HierarchicalFloatKMeans ) is a simple
hierarchical version of FloatKMeans. 
HierarchicalFloatKMeansResult 
The result of a
HierarchicalFloatKMeans clustering operation. 
HierarchicalFloatKMeansResult.Node 
HierarchicalFloatKMeans tree node
The number of children is not bigger than the HierarchicalFloatKMeans K parameter

HierarchicalIntKMeans 
Hierarchical Integer KMeans clustering (
HierarchicalIntKMeans ) is a simple
hierarchical version of IntKMeans. 
HierarchicalIntKMeansResult 
The result of a
HierarchicalIntKMeans clustering operation. 
HierarchicalIntKMeansResult.Node 
HierarchicalIntKMeans tree node
The number of children is not bigger than the HierarchicalIntKMeans K parameter

HierarchicalLongKMeans 
Hierarchical Long KMeans clustering (
HierarchicalLongKMeans ) is a simple
hierarchical version of LongKMeans. 
HierarchicalLongKMeansResult 
The result of a
HierarchicalLongKMeans clustering operation. 
HierarchicalLongKMeansResult.Node 
HierarchicalLongKMeans tree node
The number of children is not bigger than the HierarchicalLongKMeans K parameter

HierarchicalShortKMeans 
Hierarchical Short KMeans clustering (
HierarchicalShortKMeans ) is a simple
hierarchical version of ShortKMeans. 
HierarchicalShortKMeansResult 
The result of a
HierarchicalShortKMeans clustering operation. 
HierarchicalShortKMeansResult.Node 
HierarchicalShortKMeans tree node
The number of children is not bigger than the HierarchicalShortKMeans K parameter

IntKMeans 
Fast, parallel implementation of the KMeans algorithm with support for
biggerthanmemory data.

IntKMeans.Result 
Result object for IntKMeans, extending IntCentroidsResult and IntNearestNeighboursProvider,
as well as giving access to state information from the operation of the KMeans algorithm
(i.e.

IntKMeansInit 
Initialisation for KMeans clustering.

IntKMeansInit.RANDOM 
Simple kmeans initialized on randomly selected samples.

KMeansConfiguration<NN extends NearestNeighbours<DATA,?,?>,DATA> 
Configuration for the KMeans algorithm.

LongKMeans 
Fast, parallel implementation of the KMeans algorithm with support for
biggerthanmemory data.

LongKMeans.Result 
Result object for LongKMeans, extending LongCentroidsResult and LongNearestNeighboursProvider,
as well as giving access to state information from the operation of the KMeans algorithm
(i.e.

LongKMeansInit 
Initialisation for KMeans clustering.

LongKMeansInit.RANDOM 
Simple kmeans initialized on randomly selected samples.

ShortKMeans 
Fast, parallel implementation of the KMeans algorithm with support for
biggerthanmemory data.

ShortKMeans.Result 
Result object for ShortKMeans, extending ShortCentroidsResult and ShortNearestNeighboursProvider,
as well as giving access to state information from the operation of the KMeans algorithm
(i.e.

ShortKMeansInit 
Initialisation for KMeans clustering.

ShortKMeansInit.RANDOM 
Simple kmeans initialized on randomly selected samples.

SphericalKMeans 
Multithreaded (optionally) damped spherical kmeans with support for
biggerthanmemory data.

SphericalKMeans.IterationResult 
Object storing the result of the previous iteration of spherical kmeans.

SphericalKMeansResult 
The result of a
SpatialClusterer that just produces a flat set of
centroids. 