Uses of Interface
edu.ucla.sspace.clustering.criterion.CriterionFunction

Packages that use CriterionFunction

Package	Description
edu.ucla.sspace.clustering
edu.ucla.sspace.clustering.criterion

Uses of CriterionFunction in edu.ucla.sspace.clustering

Methods in edu.ucla.sspace.clustering with parameters of type CriterionFunction

Modifier and Type	Method and Description
static Assignments	DirectClustering.cluster(Matrix matrix, int numClusters, int numRepetitions, CriterionFunction criterion) Clusters matrix using the RandomSeed seeding algorithm and the specified CriterionFunction.
static Assignments	DirectClustering.cluster(Matrix matrix, int numClusters, int numRepetitions, KMeansSeed seedType, CriterionFunction criterion) Clusters matrix using the specified SeedAlgorithm and the specified CriterionFunction.

Uses of CriterionFunction in edu.ucla.sspace.clustering.criterion

Classes in edu.ucla.sspace.clustering.criterion that implement CriterionFunction

Modifier and Type	Class and Description
class	BaseFunction This CriterionFunction implements the basic functionality needed for a majority of the functions available.
class	E1Function This CriterionFunction measures the external differences between clusters.
class	G1Function This CriterionFunction interprets the dataset as a set of vertices in a graph and measures the best cuts of these vertices.
class	H1Function This HybridBaseFunction uses the E1Function and the I1Function.
class	H2Function This HybridBaseFunction uses the E1Function and the I1Function.
class	HybridBaseFunction This CriterionFunction implements the basic functionality needed for a majority of the hybrid functions available.
class	I1Function This CriterionFunction measures the amount of internal similarity for each computed centroid.
class	I2Function This CriterionFunction measures the amount of internal similarity for each computed centroid.