Uses of Interface
org.openimaj.ml.clustering.SpatialClusterer

Packages that use SpatialClusterer

Package	Description
org.openimaj.ml.clustering.dbscan
org.openimaj.ml.clustering.kdtree
org.openimaj.ml.clustering.kmeans	K-Means in OpenIMAJ is designed to be both extremely fast and flexible.
org.openimaj.ml.clustering.rac
org.openimaj.ml.clustering.random
org.openimaj.ml.clustering.rforest
org.openimaj.ml.clustering.spectral

Uses of SpatialClusterer in org.openimaj.ml.clustering.dbscan

Classes in org.openimaj.ml.clustering.dbscan that implement SpatialClusterer

Modifier and Type	Class and Description
class	DoubleNNDBSCAN Implementation of DBSCAN (http://en.wikipedia.org/wiki/DBSCAN) using a

Uses of SpatialClusterer in org.openimaj.ml.clustering.kdtree

Classes in org.openimaj.ml.clustering.kdtree that implement SpatialClusterer

Modifier and Type	Class and Description
class	DoubleKDTreeClusterer

Uses of SpatialClusterer in org.openimaj.ml.clustering.kmeans

Classes in org.openimaj.ml.clustering.kmeans that implement SpatialClusterer

Modifier and Type	Class and Description
class	ByteKMeans Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
class	DoubleKMeans Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
class	FeatureVectorKMeans<T extends FeatureVector> Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
class	FloatKMeans Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
class	HierarchicalByteKMeans Hierarchical Byte K-Means clustering (HierarchicalByteKMeans) is a simple hierarchical version of ByteKMeans.
class	HierarchicalDoubleKMeans Hierarchical Double K-Means clustering (HierarchicalDoubleKMeans) is a simple hierarchical version of DoubleKMeans.
class	HierarchicalFloatKMeans Hierarchical Float K-Means clustering (HierarchicalFloatKMeans) is a simple hierarchical version of FloatKMeans.
class	HierarchicalIntKMeans Hierarchical Integer K-Means clustering (HierarchicalIntKMeans) is a simple hierarchical version of IntKMeans.
class	HierarchicalLongKMeans Hierarchical Long K-Means clustering (HierarchicalLongKMeans) is a simple hierarchical version of LongKMeans.
class	HierarchicalShortKMeans Hierarchical Short K-Means clustering (HierarchicalShortKMeans) is a simple hierarchical version of ShortKMeans.
class	IntKMeans Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
class	LongKMeans Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
class	ShortKMeans Fast, parallel implementation of the K-Means algorithm with support for bigger-than-memory data.
class	SphericalKMeans Multithreaded (optionally) damped spherical k-means with support for bigger-than-memory data.

Uses of SpatialClusterer in org.openimaj.ml.clustering.rac

Classes in org.openimaj.ml.clustering.rac that implement SpatialClusterer

Modifier and Type	Class and Description
class	ClusterLimitedIntRAC Similar to IntRAC but explicitly specify the limit the number of clusters.
class	IntRAC An implementation of the RAC algorithm proposed by Ramanan and Niranjan.

Uses of SpatialClusterer in org.openimaj.ml.clustering.random

Classes in org.openimaj.ml.clustering.random that implement SpatialClusterer

Modifier and Type	Class and Description
class	RandomByteClusterer A simple (yet apparently quite effective in high dimensions) clustering technique trained used randomly sampled data points.
class	RandomDoubleClusterer A simple (yet apparently quite effective in high dimensions) clustering technique trained used randomly sampled data points.
class	RandomFloatClusterer A simple (yet apparently quite effective in high dimensions) clustering technique trained used randomly sampled data points.
class	RandomIntClusterer A simple (yet apparently quite effective in high dimensions) clustering technique trained used randomly sampled data points.
class	RandomLongClusterer A simple (yet apparently quite effective in high dimensions) clustering technique trained used randomly sampled data points.
class	RandomSetByteClusterer A similar strategy to RandomSetByteClusterer however it is guaranteed that the same training vector will not be sampled more than once.
class	RandomSetDoubleClusterer A similar strategy to RandomSetDoubleClusterer however it is guaranteed that the same training vector will not be sampled more than once.
class	RandomSetFloatClusterer A similar strategy to RandomSetFloatClusterer however it is guaranteed that the same training vector will not be sampled more than once.
class	RandomSetIntClusterer A similar strategy to RandomSetIntClusterer however it is guaranteed that the same training vector will not be sampled more than once.
class	RandomSetLongClusterer A similar strategy to RandomSetLongClusterer however it is guaranteed that the same training vector will not be sampled more than once.
class	RandomSetShortClusterer A similar strategy to RandomSetShortClusterer however it is guaranteed that the same training vector will not be sampled more than once.
class	RandomShortClusterer A simple (yet apparently quite effective in high dimensions) clustering technique trained used randomly sampled data points.

Uses of SpatialClusterer in org.openimaj.ml.clustering.rforest

Classes in org.openimaj.ml.clustering.rforest that implement SpatialClusterer

Modifier and Type	Class and Description
class	IntRandomForest An implementation of the RandomForest clustering algorithm proposed by Jurie et al.

Uses of SpatialClusterer in org.openimaj.ml.clustering.spectral

Methods in org.openimaj.ml.clustering.spectral that return SpatialClusterer

Modifier and Type	Method and Description
SpatialClusterer<? extends SpatialClusters<DATATYPE>,DATATYPE>	SpectralClusteringConf.DefaultClustererFunction.apply(IndependentPair<double[],double[][]> in)

Constructors in org.openimaj.ml.clustering.spectral with parameters of type SpatialClusterer

Constructor and Description
DefaultClustererFunction(SpatialClusterer<? extends SpatialClusters<DATATYPE>,DATATYPE> internal)
MultiviewSpectralClusteringConf(double lambda, SpatialClusterer<? extends SpatialClusters<DATATYPE>,DATATYPE> internal)
MultiviewSpectralClusteringConf(double lambda, SpatialClusterer<? extends SpatialClusters<DATATYPE>,DATATYPE> internal, GraphLaplacian laplacian)
MultiviewSpectralClusteringConf(double lambda, SpatialClusterer<? extends SpatialClusters<DATATYPE>,DATATYPE> internal, int eigK)
SpectralClusteringConf(SpatialClusterer<? extends SpatialClusters<DATATYPE>,DATATYPE> internal) The underlying EigenChooser is set to an ChangeDetectingEigenChooser which looks for a 100x gap between eigen vectors to select number of clusters.
SpectralClusteringConf(SpatialClusterer<? extends SpatialClusters<DATATYPE>,DATATYPE> internal, GraphLaplacian laplacian) The underlying EigenChooser is set to an ChangeDetectingEigenChooser which looks for a 100x gap between eigen vectors to select number of clusters.
SpectralClusteringConf(SpatialClusterer<? extends SpatialClusters<DATATYPE>,DATATYPE> internal, GraphLaplacian lap, int top)
SpectralClusteringConf(SpatialClusterer<? extends SpatialClusters<DATATYPE>,DATATYPE> internal, int eigK)