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

Provides implementations of methods for selecting initial clusters.

See: Description

Interface Summary

Interface	Description
FixedClusterInitializer<ClusterType extends Cluster<DataType>,DataType>	The FixedClusterInitializer interface defines the functionality of a class that can initialize a given number of clusters from a set of elements.

Class Summary

Class	Description
AbstractMinDistanceFixedClusterInitializer<ClusterType extends Cluster<DataType>,DataType>	Implements an abstract FixedClusterInitializer that works by using the minimum distance from a point to the cluster.
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.
GreedyClusterInitializer<ClusterType extends Cluster<DataType>,DataType>	Implements a FixedClusterInitializer that greedily attempts to create the initial clusters.
NeighborhoodGaussianClusterInitializer	Creates GaussianClusters near existing, but not on top of, data points.
RandomClusterInitializer<ClusterType extends Cluster<DataType>,DataType>	Creates initial clusters by selecting random data points as singleton clusters.

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

Provides implementations of methods for selecting initial clusters. These provide some clustering algorithms, such as k-means with a starting set of clusters based on the data.

Since:

2.0

Author:

Justin Basilico