Package org.openimaj.ml.clustering.kmeans

K-Means in OpenIMAJ is designed to be both extremely fast and flexible.

See: Description

Class Summary

Class	Description
ByteKMeans	Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
ByteKMeans.Result	Result object for ByteKMeans, extending ByteCentroidsResult and ByteNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
ByteKMeansInit	Initialisation for K-Means clustering.
ByteKMeansInit.RANDOM	Simple kmeans initialized on randomly selected samples.
DoubleKMeans	Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
DoubleKMeans.Result	Result object for DoubleKMeans, extending DoubleCentroidsResult and DoubleNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
DoubleKMeansInit	Initialisation for K-Means clustering.
DoubleKMeansInit.RANDOM	Simple kmeans initialized on randomly selected samples.
FeatureVectorKMeans<T extends FeatureVector>	Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory 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 K-Means algorithm (i.e.
FeatureVectorKMeansInit<T>	Initialisation for K-Means clustering.
FeatureVectorKMeansInit.RANDOM<T>	Simple kmeans initialized on randomly selected samples.
FloatKMeans	Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
FloatKMeans.Result	Result object for FloatKMeans, extending FloatCentroidsResult and FloatNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
FloatKMeansInit	Initialisation for K-Means clustering.
FloatKMeansInit.RANDOM	Simple kmeans initialized on randomly selected samples.
HierarchicalByteKMeans	Hierarchical Byte K-Means 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 K-Means 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 K-Means 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 K-Means 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 K-Means 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 K-Means 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 K-Means algorithm with support for bigger-than-memory data.
IntKMeans.Result	Result object for IntKMeans, extending IntCentroidsResult and IntNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
IntKMeansInit	Initialisation for K-Means clustering.
IntKMeansInit.RANDOM	Simple kmeans initialized on randomly selected samples.
KMeansConfiguration<NN extends NearestNeighbours<DATA,?,?>,DATA>	Configuration for the K-Means algorithm.
LongKMeans	Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
LongKMeans.Result	Result object for LongKMeans, extending LongCentroidsResult and LongNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
LongKMeansInit	Initialisation for K-Means clustering.
LongKMeansInit.RANDOM	Simple kmeans initialized on randomly selected samples.
ShortKMeans	Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
ShortKMeans.Result	Result object for ShortKMeans, extending ShortCentroidsResult and ShortNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
ShortKMeansInit	Initialisation for K-Means clustering.
ShortKMeansInit.RANDOM	Simple kmeans initialized on randomly selected samples.
SphericalKMeans	Multithreaded (optionally) damped spherical k-means with support for bigger-than-memory 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.

Package org.openimaj.ml.clustering.kmeans Description

K-Means in OpenIMAJ is designed to be both extremely fast and flexible. To this end, we've separated out the cluster-assignment part of the algorithm to be performed by a separate object. This means that the core implementation (initial assignment, followed by iterative cluster assignment and centroid averaging) can use any kind of cluster assignment approach. There are a number of assignment techniques that can be used, including exact assignment and approximate assignment based on KD-Tree ensembles.