Uses of Interface

Packages that use BatchClusterer
gov.sandia.cognition.learning.algorithm.clustering Provides clustering algorithms. 

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

Classes in gov.sandia.cognition.learning.algorithm.clustering that implement BatchClusterer
 class AffinityPropagation<DataType>
          The AffinityPropagation algorithm requires three parameters: a divergence function, a value to use for self-divergence, and a damping factor (called lambda in the paper; 0.5 is the default).
 class AgglomerativeClusterer<DataType,ClusterType extends Cluster<DataType>>
          The AgglomerativeClusterer implements an agglomerative clustering algorithm, which is a type of hierarchical clustering algorithm.
 class DirichletProcessClustering
          Clustering algorithm that wraps Dirichlet Process Mixture Model.
 class KMeansClusterer<DataType,ClusterType extends Cluster<DataType>>
          The KMeansClusterer class implements the standard k-means (k-centroids) clustering algorithm.
 class KMeansClustererWithRemoval<DataType,ClusterType extends Cluster<DataType>>
          Creates a k-means clustering algorithm that removes clusters that do not have sufficient membership to pass a simple statistical significance test.
 class OptimizedKMeansClusterer<DataType>
          This class implements an optimized version of the k-means algorithm that makes use of the triangle inequality to compute the same answer as k-means while using less distance calculations.
 class ParallelizedKMeansClusterer<DataType,ClusterType extends Cluster<DataType>>
          This is a parallel implementation of the k-means clustering algorithm.
 class PartitionalClusterer<DataType,ClusterType extends Cluster<DataType>>
          The PartitionClusterer implements a partitional clustering algorithm, which is a type of hierarchical clustering algorithm.