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

Packages that use CentroidCluster
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
protected  HashMap<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
 ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>> KMeansFactory.create()
           
static ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>> KMeansFactory.create(int numClusters, 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, 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  HashMap<Integer,CentroidCluster<DataType>> AffinityPropagation.getClusters()
          Gets the current clusters, which is a sparse mapping of exemplar identifier to cluster object.
 ArrayList<CentroidCluster<DataType>> AffinityPropagation.getResult()
           
 

Method parameters in gov.sandia.cognition.learning.algorithm.clustering with type arguments of type CentroidCluster
protected  void AffinityPropagation.setClusters(HashMap<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
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
 

Methods in gov.sandia.cognition.learning.algorithm.clustering.cluster that return CentroidCluster
 CentroidCluster<Vector> VectorMeanCentroidClusterCreator.createCluster()
           
 CentroidCluster<DataType> MedoidClusterCreator.createCluster(Collection<DataType> members)
          Creates a CentroidCluster at the member that minimizes the sum of divergence between all members
 CentroidCluster<Vector> VectorMeanCentroidClusterCreator.createCluster(Collection<Vector> members)
           
 

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