See: Description
Interface  Description 

ClusterDivergenceFunction<ClusterType extends Cluster<DataType>,DataType> 
The ClusterDivergenceFunction interface defines a function that computes
the divergence between a cluster and some other object.

ClusterToClusterDivergenceFunction<ClusterType extends Cluster<DataType>,DataType> 
The ClusterToClusterDivergenceFunction defines a DivergenceFunction between
two clusters of the same data type.

WithinClusterDivergence<ClusterType extends Cluster<DataType>,DataType> 
Defines a function that computes the divergence of the elements in a cluster.

Class  Description 

AbstractClusterToClusterDivergenceFunction<ClusterType extends Cluster<DataType>,DataType> 
The AbstractClusterToClusterDivergenceFunction class is an abstract class
that helps out implementations of ClusterToClusterDivergenceFunction
implementations by holding a DivergenceFunction between elements of a
cluster.

CentroidClusterDivergenceFunction<DataType> 
The CentroidClusterDivergenceFunction class implements a divergence function
between a cluster and an object by computing the divergence between the
center of the cluster and the object.

ClusterCentroidDivergenceFunction<DataType> 
The ClusterCentroidDivergenceFunction class implements the distance
between two clusters by computing the distance between the cluster's
centroid.

ClusterCompleteLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType> 
The ClusterCompleteLinkDivergenceFunction class implements the complete
linkage distance metric between two clusters.

ClusterMeanLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType> 
The ClusterMeanLinkDivergenceFunction class implements the mean linkage
distance metric between two clusters.

ClusterSingleLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType> 
The ClusterSingleLinkDivergenceFunction class implements the complete
linkage distance metric between two clusters.

GaussianClusterDivergenceFunction 
The GaussianClusterDivergenceFunction class implements a divergence
function between a Gaussian cluster and a vector, which is calculated
by finding the likelihood that the vector was generated from that Gaussian
and then returning the negative of the likelihood since it is a divergence
measure, not a similarity measure.

WithinClusterDivergenceWrapper<ClusterType extends Cluster<DataType>,DataType> 
Accumulates the results of a
ClusterDivergenceFunction by summing the
divergence of each point to its cluster. 
WithinNormalizedCentroidClusterCosineDivergence<V extends Vectorizable> 
This class calculates the total cosine divergence between all members of a
cluster and the cluster's centroid
