jsat.clustering.hierarchical

Class DivisiveLocalClusterer

java.lang.Object

jsat.clustering.ClustererBase

jsat.clustering.KClustererBase

jsat.clustering.hierarchical.DivisiveLocalClusterer

All Implemented Interfaces:

Serializable, Clusterer, KClusterer

public class DivisiveLocalClusterer
extends KClustererBase

DivisiveLocalClusterer is a hierarchical clustering method that works by splitting the data set into sub trees from the top down. Unlike many top-up methods, such as SimpleHAC, top-down methods require another clustering method to perform the splitting at each iteration. If the base method is not deterministic, then the top-down method will not be deterministic.

Specifically, DivisiveLocalClusterer greedily chooses the cluster to split based on an evaluation of only the cluster being split. Because of this local search of the world, DivisiveLocalClusterer has poor performance in determining the number of clusters in the data set. As such, only the methods where the exact number of clusters are recommended.
This greedy strategy can also lead to drilling down clusters into small parts, and works best when only a small number of clusters are needed.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
DivisiveLocalClusterer(DivisiveLocalClusterer toCopy) Copy constructor
DivisiveLocalClusterer(KClusterer baseClusterer, ClusterEvaluation clusterEvaluation)

Method Summary

Modifier and Type	Method and Description
DivisiveLocalClusterer	clone()
int[]	cluster(DataSet dataSet, ExecutorService threadpool, int[] designations) Performs clustering on the given data set.
int[]	cluster(DataSet dataSet, int[] designations) Performs clustering on the given data set.
int[]	cluster(DataSet dataSet, int clusters, ExecutorService threadpool, int[] designations)
int[]	cluster(DataSet dataSet, int clusters, int[] designations)
int[]	cluster(DataSet dataSet, int lowK, int highK, ExecutorService threadpool, int[] designations)
int[]	cluster(DataSet dataSet, int lowK, int highK, int[] designations)

Methods inherited from class jsat.clustering.KClustererBase

cluster, cluster, cluster, cluster

Methods inherited from class jsat.clustering.ClustererBase

cluster, cluster, createClusterListFromAssignmentArray, getDatapointsFromCluster, supportsWeightedData

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface jsat.clustering.Clusterer

cluster, cluster, supportsWeightedData

Constructor Detail

DivisiveLocalClusterer

public DivisiveLocalClusterer(KClusterer baseClusterer,
                              ClusterEvaluation clusterEvaluation)

DivisiveLocalClusterer

public DivisiveLocalClusterer(DivisiveLocalClusterer toCopy)

Copy constructor

Parameters:

toCopy - the object to copy

Method Detail

cluster

public int[] cluster(DataSet dataSet,
                     int[] designations)

Description copied from interface: Clusterer

Performs clustering on the given data set. Parameters may be estimated by the method, or other heuristics performed.

Parameters:

dataSet - the data set to perform clustering on

designations - the array which will contain the designated values. The array will be altered and returned by the function. If null is given, a new array will be created and returned.

Returns:

an array indicating for each value indicating the cluster designation. This is the same array as designations, or a new one if the input array was null

cluster

public int[] cluster(DataSet dataSet,
                     ExecutorService threadpool,
                     int[] designations)

Description copied from interface: Clusterer

Performs clustering on the given data set. Parameters may be estimated by the method, or other heuristics performed.

Parameters:

dataSet - the data set to perform clustering on

threadpool - a source of threads to run tasks

designations - the array which will contain the designated values. The array will be altered and returned by the function. If null is given, a new array will be created and returned.

Returns:

an array indicating for each value indicating the cluster designation. This is the same array as designations, or a new one if the input array was null

cluster

public int[] cluster(DataSet dataSet,
                     int clusters,
                     ExecutorService threadpool,
                     int[] designations)

cluster

public int[] cluster(DataSet dataSet,
                     int clusters,
                     int[] designations)

cluster

public int[] cluster(DataSet dataSet,
                     int lowK,
                     int highK,
                     ExecutorService threadpool,
                     int[] designations)

cluster

public int[] cluster(DataSet dataSet,
                     int lowK,
                     int highK,
                     int[] designations)

clone

public DivisiveLocalClusterer clone()

Specified by:

clone in interface Clusterer

Specified by:

clone in interface KClusterer

Specified by:

clone in class KClustererBase