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

Provides clustering algorithms.


Interface Summary
BatchClusterer<DataType,ClusterType extends Cluster<DataType>> The BatchClusterer interface defines the functionality of a batch clustering algorithm.

Class Summary
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).
AgglomerativeClusterer<DataType,ClusterType extends Cluster<DataType>> The AgglomerativeClusterer implements an agglomerative clustering algorithm, which is a type of hierarchical clustering algorithm.
AgglomerativeClusterer.HierarchyNode<DataType,ClusterType extends Cluster<DataType>> Holds the hierarchy information for the agglomerative clusterer.
DirichletProcessClustering Clustering algorithm that wraps Dirichlet Process Mixture Model.
KMeansClusterer<DataType,ClusterType extends Cluster<DataType>> The KMeansClusterer class implements the standard k-means (k-centroids) clustering algorithm.
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.
KMeansFactory Creates a parallelized version of the k-means clustering algorithm for the typical use: clustering vector data with a Euclidean distance metric.
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.
ParallelizedKMeansClusterer<DataType,ClusterType extends Cluster<DataType>> This is a parallel implementation of the k-means clustering algorithm.
PartitionalClusterer<DataType,ClusterType extends Cluster<DataType>> The PartitionClusterer implements a partitional clustering algorithm, which is a type of hierarchical clustering algorithm.

Package gov.sandia.cognition.learning.algorithm.clustering Description

Provides clustering algorithms. Clustering algorithms are a type of unsupervised learning that attempts to find appropriate groupings from given data.

Justin Basilico