Uses of Interface
org.openimaj.experiment.evaluation.cluster.processor.Clusterer

Packages that use Clusterer

Package	Description
org.openimaj.experiment.evaluation.cluster
org.openimaj.ml.clustering
org.openimaj.ml.clustering.dbscan
org.openimaj.ml.clustering.incremental
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 Clusterer in org.openimaj.experiment.evaluation.cluster

Constructors in org.openimaj.experiment.evaluation.cluster with parameters of type Clusterer

Constructor and Description
ClusterEvaluator(Clusterer<D> gen, D data, Function<B,Integer> indexFunc, Map<A,? extends List<B>> dataset, ClusterAnalyser<T> analyser)
ClusterEvaluator(Clusterer<D> gen, D data, int[][] clusters, ClusterAnalyser<T> analyser)
ClusterEvaluator(Clusterer<D> gen, D data, Map<A,? extends List<B>> dataset, ClusterAnalyser<T> analyser)
ClusterEvaluator(Clusterer<D> gen, Map<A,? extends List<B>> dataset, Function<List<B>,D> transform, ClusterAnalyser<T> analyser)

Uses of Clusterer in org.openimaj.ml.clustering

Subinterfaces of Clusterer in org.openimaj.ml.clustering

Modifier and Type	Interface and Description
interface	DataClusterer<DATA,CLUSTER extends IndexClusters> Clusterers can extract clusters from data types and return the data in a clustered form
interface	DistanceClusterer<CLUSTERS extends IndexClusters> A DistanceClusterer clusters data that can be represented as a distance matrix.
interface	MultiviewSimilarityClusterer<CLUSTERS extends IndexClusters> A MultiviewSimilarityClusterer clusters data that can be represented as multiple similarity matricies.
interface	SimilarityClusterer<CLUSTERS extends IndexClusters> A SimilarityClusterer clusters data that can be represented as a similarity matrix.
interface	SparseMatrixClusterer<CLUSTERS extends IndexClusters> A matrix clusterer can cluster a matrix of data in some way
interface	SpatialClusterer<CLUSTERTYPE extends SpatialClusters<DATATYPE>,DATATYPE> A SpatialClusterer clusters data that can be represented as points in a space.

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

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

Modifier and Type	Class and Description
class	ContectedComponentSimilarityClusterer Cluster based on connected components.
class	DistanceDBSCAN DBSCAN using a SparseMatrix of distances
class	DoubleNNDBSCAN Implementation of DBSCAN (http://en.wikipedia.org/wiki/DBSCAN) using a
class	SimilarityDBSCAN DBSCAN using a SparseMatrix of similarities
class	SparseMatrixDBSCAN Implementation of DBSCAN (http://en.wikipedia.org/wiki/DBSCAN) using a

Uses of Clusterer in org.openimaj.ml.clustering.incremental

Classes in org.openimaj.ml.clustering.incremental that implement Clusterer

Modifier and Type	Class and Description
class	IncrementalLifetimeSparseClusterer An IncrementalSparseClusterer which also has a notion of a lifetime.
class	IncrementalSparseClusterer An incremental clusterer which holds old SparseMatrix instances internally, only forgetting rows once they have been clustered and are relatively stable.

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

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

Modifier and Type	Class and Description
class	DoubleKDTreeClusterer

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

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

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	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.

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

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

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 Clusterer in org.openimaj.ml.clustering.random

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

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	RandomClusterer Given a similarity or distance matrix, this clusterer randomly selects a number of clusters and randomly assigned each row to each cluster.
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 Clusterer in org.openimaj.ml.clustering.rforest

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

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

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

Classes in org.openimaj.ml.clustering.spectral that implement Clusterer

Modifier and Type	Class and Description
class	CachedDoubleSpectralClustering DoubleSpectralClustering extention which knows how to write and read its eigenvectors to disk and therefore not regenerate them when calling the underlying PreparedSpectralClustering
class	DoubleMultiviewSpectralClustering
class	DoubleSpectralClustering Built from a mixture of this tutorial: - http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/attachments/Luxburg07_tutorial_4488%5B0%5D.pdf And this implementation: - https://github.com/peterklipfel/AutoponicsVision/blob/master/SpectralClustering.java
class	PreparedSpectralClustering For a given set of Eigenvalues perform the stages of spectral clustering which involve the selection of the best eigen values and the calling of an internal clustering algorithm