Uses of Interface
gov.sandia.cognition.learning.algorithm.clustering.initializer.FixedClusterInitializer

Packages that use FixedClusterInitializer
gov.sandia.cognition.learning.algorithm.clustering Provides clustering algorithms. 
gov.sandia.cognition.learning.algorithm.clustering.initializer Provides implementations of methods for selecting initial clusters. 
 

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

Fields in gov.sandia.cognition.learning.algorithm.clustering declared as FixedClusterInitializer
protected  FixedClusterInitializer<ClusterType,DataType> KMeansClusterer.initializer
          The initializer for the algorithm.
 

Methods in gov.sandia.cognition.learning.algorithm.clustering that return FixedClusterInitializer
 FixedClusterInitializer<ClusterType,DataType> KMeansClusterer.getInitializer()
          Gets the cluster initializer.
 

Methods in gov.sandia.cognition.learning.algorithm.clustering with parameters of type FixedClusterInitializer
 void KMeansClusterer.setInitializer(FixedClusterInitializer<ClusterType,DataType> initializer)
          Sets the cluster initializer.
 

Constructors in gov.sandia.cognition.learning.algorithm.clustering with parameters of type FixedClusterInitializer
KMeansClusterer(int numRequestedClusters, int maxIterations, FixedClusterInitializer<ClusterType,DataType> initializer, ClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator)
          Creates a new instance of KMeansClusterer using the given parameters.
KMeansClustererWithRemoval(int numRequestedClusters, int maxIterations, FixedClusterInitializer<ClusterType,DataType> initializer, ClusterDivergenceFunction<ClusterType,DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator, double removalThreshold)
          Creates a new instance of KMeansClusterer using the given parameters.
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.
ParallelizedKMeansClusterer(int numRequestedClusters, int maxIterations, ThreadPoolExecutor threadPool, FixedClusterInitializer<ClusterType,DataType> initializer, ClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator)
          Creates a new instance of ParallelizedKMeansClusterer2
 

Uses of FixedClusterInitializer in gov.sandia.cognition.learning.algorithm.clustering.initializer
 

Classes in gov.sandia.cognition.learning.algorithm.clustering.initializer that implement FixedClusterInitializer
 class AbstractMinDistanceFixedClusterInitializer<ClusterType extends Cluster<DataType>,DataType>
          Implements an abstract FixedClusterInitializer that works by using the minimum distance from a point to the cluster.
 class DistanceSamplingClusterInitializer<ClusterType extends Cluster<DataType>,DataType>
          Implements FixedClusterInitializer that initializes clusters by first selecting a random point for the first cluster and then randomly sampling each successive cluster based on the squared minimum distance from the point to the existing selected clusters.
 class GreedyClusterInitializer<ClusterType extends Cluster<DataType>,DataType>
          Implements a FixedClusterInitializer that greedily attempts to create the initial clusters.
 class NeighborhoodGaussianClusterInitializer
          Creates GaussianClusters near existing, but not on top of, data points.