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

Packages that use FixedClusterInitializer

Package	Description
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

Modifier and Type	Field and Description
protected FixedClusterInitializer<ClusterType,DataType>	KMeansClusterer.initializer The initializer for the algorithm.

Methods in gov.sandia.cognition.learning.algorithm.clustering that return FixedClusterInitializer

Modifier and Type	Method and Description
FixedClusterInitializer<ClusterType,DataType>	KMeansClusterer.getInitializer() Gets the cluster initializer.

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

Modifier and Type	Method and Description
void	KMeansClusterer.setInitializer(FixedClusterInitializer<ClusterType,DataType> initializer) Sets the cluster initializer.
MiniBatchKMeansClusterer.Builder<DataType>	MiniBatchKMeansClusterer.Builder.withInitializer(FixedClusterInitializer<MiniBatchCentroidCluster,Vector> initializer)

Constructors in gov.sandia.cognition.learning.algorithm.clustering with parameters of type FixedClusterInitializer

Constructor and Description
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.
MiniBatchKMeansClusterer(int numClusters, int maxIterations, FixedClusterInitializer<MiniBatchCentroidCluster,Vector> initializer, Semimetric<? super Vector> metric, ClusterCreator<MiniBatchCentroidCluster,Vector> creator, java.util.Random random) Creates a new MiniBatchKMeansClusterer.
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, java.util.concurrent.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

Modifier and Type	Class and Description
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.
class	RandomClusterInitializer<ClusterType extends Cluster<DataType>,DataType> Creates initial clusters by selecting random data points as singleton clusters.