Uses of Class
gov.sandia.cognition.learning.algorithm.clustering.cluster.CentroidCluster

Packages that use CentroidCluster

Package	Description
gov.sandia.cognition.learning.algorithm.clustering	Provides clustering algorithms.
gov.sandia.cognition.learning.algorithm.clustering.cluster	Provides implementations of different types of clusters.
gov.sandia.cognition.learning.algorithm.clustering.divergence	Provides divergence functions for use in clustering.

Uses of CentroidCluster in gov.sandia.cognition.learning.algorithm.clustering

Fields in gov.sandia.cognition.learning.algorithm.clustering with type parameters of type CentroidCluster

Modifier and Type	Field and Description
protected java.util.HashMap<java.lang.Integer,CentroidCluster<DataType>>	AffinityPropagation.clusters The clusters that have been found so far.

Methods in gov.sandia.cognition.learning.algorithm.clustering that return types with arguments of type CentroidCluster

Modifier and Type	Method and Description
ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>>	KMeansFactory.create()
static PartitionalClusterer<Vector,CentroidCluster<Vector>>	PartitionalClusterer.create(int numRequestedClusters) Create a partitional clusterer, using Euclidean distance and a vector mean centroid cluster creator.
static ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>>	KMeansFactory.create(int numClusters, java.util.Random random) Creates a new parallelized k-means clustering algorithm for vector data with the given number of clusters (k) and random number generator.
static ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>>	KMeansFactory.create(int numClusters, Semimetric<? super Vector> distanceMetric, java.util.Random random) Creates a new parallelized k-means clustering algorithm for vector data with the given number of clusters (k), distance metric, and random number generator.
protected java.util.HashMap<java.lang.Integer,CentroidCluster<DataType>>	AffinityPropagation.getClusters() Gets the current clusters, which is a sparse mapping of exemplar identifier to cluster object.
java.util.ArrayList<CentroidCluster<DataType>>	AffinityPropagation.getResult()

Method parameters in gov.sandia.cognition.learning.algorithm.clustering with type arguments of type CentroidCluster

Modifier and Type	Method and Description
protected void	AffinityPropagation.setClusters(java.util.HashMap<java.lang.Integer,CentroidCluster<DataType>> clusters) Sets the current clusters, which is a sparse mapping of exemplar identifier to cluster object.

Constructor parameters in gov.sandia.cognition.learning.algorithm.clustering with type arguments of type CentroidCluster

Constructor and Description
OptimizedKMeansClusterer(int numClusters, int maxIterations, FixedClusterInitializer<CentroidCluster<DataType>,DataType> initializer, Metric<? super DataType> metric, ClusterCreator<CentroidCluster<DataType>,DataType> creator) Creates a new instance of OptimizedKMeansClusterer.
OptimizedKMeansClusterer(int numClusters, int maxIterations, FixedClusterInitializer<CentroidCluster<DataType>,DataType> initializer, Metric<? super DataType> metric, ClusterCreator<CentroidCluster<DataType>,DataType> creator) Creates a new instance of OptimizedKMeansClusterer.

Uses of CentroidCluster in gov.sandia.cognition.learning.algorithm.clustering.cluster

Subclasses of CentroidCluster in gov.sandia.cognition.learning.algorithm.clustering.cluster

Modifier and Type	Class and Description
class	MiniBatchCentroidCluster
class	NormalizedCentroidCluster<ClusterType> Add the ability to store the centroid of the normalized vectors belonging to a centroid cluster.

Methods in gov.sandia.cognition.learning.algorithm.clustering.cluster that return CentroidCluster

Modifier and Type	Method and Description
CentroidCluster<Vector>	VectorMeanCentroidClusterCreator.createCluster()
CentroidCluster<DataType>	MedoidClusterCreator.createCluster(java.util.Collection<? extends DataType> members) Creates a CentroidCluster at the member that minimizes the sum of divergence between all members
CentroidCluster<Vector>	VectorMeanCentroidClusterCreator.createCluster(java.util.Collection<? extends Vector> members)

Methods in gov.sandia.cognition.learning.algorithm.clustering.cluster with parameters of type CentroidCluster

Modifier and Type	Method and Description
void	VectorMeanCentroidClusterCreator.addClusterMember(CentroidCluster<Vector> cluster, Vector member)
boolean	VectorMeanCentroidClusterCreator.removeClusterMember(CentroidCluster<Vector> cluster, Vector member)

Uses of CentroidCluster in gov.sandia.cognition.learning.algorithm.clustering.divergence

Methods in gov.sandia.cognition.learning.algorithm.clustering.divergence with parameters of type CentroidCluster

Modifier and Type	Method and Description
double	ClusterCentroidDivergenceFunction.evaluate(CentroidCluster<DataType> from, CentroidCluster<DataType> to) This method computes the complete link distance between the two given Clusters.
double	ClusterCentroidDivergenceFunction.evaluate(CentroidCluster<DataType> from, CentroidCluster<DataType> to) This method computes the complete link distance between the two given Clusters.
double	CentroidClusterDivergenceFunction.evaluate(CentroidCluster<DataType> cluster, DataType other) Evaluates the divergence between the cluster centroid and the given object.