Package gov.sandia.cognition.learning.algorithm.clustering.divergence

Provides divergence functions for use in clustering.

See:
          Description

Interface Summary
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.
 

Class Summary
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.
 

Package gov.sandia.cognition.learning.algorithm.clustering.divergence Description

Provides divergence functions for use in clustering. In particular, it contains divergence functions for handling the divergence from different clusters to data plus divergence functions between clusters, which are useful for Agglomerative Clustering.

Since:
2.0
Author:
Justin Basilico