Uses of Package
gov.sandia.cognition.learning.algorithm.clustering

Packages that use gov.sandia.cognition.learning.algorithm.clustering

Package	Description
gov.sandia.cognition.learning.algorithm.clustering	Provides clustering algorithms.
gov.sandia.cognition.learning.function.vector	Provides functions that output vectors.
gov.sandia.cognition.statistics.distribution	Provides statistical distributions.

Classes in gov.sandia.cognition.learning.algorithm.clustering used by gov.sandia.cognition.learning.algorithm.clustering

Class and Description
AffinityPropagation 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).
AgglomerativeClusterer The AgglomerativeClusterer implements an agglomerative clustering algorithm, which is a type of hierarchical clustering algorithm.
AgglomerativeClusterer.HierarchyNode Holds the hierarchy information for the agglomerative clusterer.
BatchClusterer The BatchClusterer interface defines the functionality of a batch clustering algorithm.
DBSCANClusterer The DBSCAN algorithm requires three parameters: a distance metric, a value for neighborhood radius, and a value for the minimum number of surrounding neighbors for a point to be considered non-noise.
DirichletProcessClustering Clustering algorithm that wraps Dirichlet Process Mixture Model.
KMeansClusterer The KMeansClusterer class implements the standard k-means (k-centroids) clustering algorithm.
MiniBatchKMeansClusterer Approximates k-means clustering by working on random subsets of the data.
MiniBatchKMeansClusterer.Builder Can be used to create custom MiniBatchKMeansClusterers without using the big constructor.
OptimizedKMeansClusterer 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.
ParallelizedKMeansClusterer This is a parallel implementation of the k-means clustering algorithm.
PartitionalClusterer The PartitionalClusterer implements a partitional clustering algorithm, which is a type of hierarchical clustering algorithm.

Classes in gov.sandia.cognition.learning.algorithm.clustering used by gov.sandia.cognition.learning.function.vector

Class and Description
KMeansClusterer The KMeansClusterer class implements the standard k-means (k-centroids) clustering algorithm.

Classes in gov.sandia.cognition.learning.algorithm.clustering used by gov.sandia.cognition.statistics.distribution

Class and Description
KMeansClusterer The KMeansClusterer class implements the standard k-means (k-centroids) clustering algorithm.