Interface  Description 

SpectralClusteringConf.ClustererProvider<DATATYPE> 
A function which can represent itself as a string

StoppingCondition 
The stopping condition for a multiview spectral clustering algorithm

Class  Description 

AbsoluteValueEigenChooser 
Attempts to automatically choose the number of eigen vectors based on the
comparative value of the eigen value with the first eigen value seen.

CachedDoubleSpectralClustering 
DoubleSpectralClustering extention which knows how to write and read its eigenvectors to disk
and therefore not regenerate them when calling the underlying PreparedSpectralClustering 
ChangeDetectingEigenChooser 
Attempts to automatically choose the number of eigen vectors based on the
relative gap between eigen values.

DoubleFVSimilarityFunction<T> 
Wraps the functionality of a
SimilarityClusterer around a dataset 
DoubleMultiviewSpectralClustering  
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

DummyExtractor  
EigenChooser 
Method which makes a decision on how many eigen vectors to select

FBEigenIterator 
A forward or backward iterator of eigen vector/value pairs

GraphLaplacian 
Functions which turn a graph weight adjacency matrix into the Laplacian
matrix.

GraphLaplacian.Normalised 
The inverted symmetric normalised Laplacian is defined as:
L = D^1/2 A D^1/2

GraphLaplacian.Unnormalised 
The symmetric normalised Laplacian is defined as:
L = D  W

GraphLaplacian.Warped 
The inverted symmetric normalised Laplacian is defined as:
L = D^1 .

HardCodedEigenChooser  
MultiviewSpectralClusteringConf<DATATYPE>  
NormalisedSimilarityDoubleClustererWrapper<T> 
Wraps the functionality of a
SimilarityClusterer around a dataset 
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 
RBFSimilarityDoubleClustererWrapper<T> 
Construct a similarity matrix using a Radial Basis Function

SpectralClusteringConf<DATATYPE>  
SpectralClusteringConf.DefaultClustererFunction<DATATYPE>  
SpectralIndexedClusters 
IndexClusters which also hold the eigenvector/value pairs which created them 
StoppingCondition.HardCoded 
Counts the iterations

WineDatasetExperiment 
Perform spectral clustering experiments using the Wine Dataset
