jsat.clustering.hierarchical

Class PriorityHAC

java.lang.Object

jsat.clustering.ClustererBase

jsat.clustering.KClustererBase

jsat.clustering.hierarchical.PriorityHAC

All Implemented Interfaces:

java.io.Serializable, Clusterer, KClusterer

public class PriorityHAC
extends KClustererBase

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
PriorityHAC(PriorityHAC toCopy) Copy constructor
PriorityHAC(UpdatableClusterDissimilarity dissMeasure)

Method Summary

Modifier and Type	Method and Description
PriorityHAC	clone()
int[]	cluster(DataSet dataSet, boolean parallel, 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, boolean parallel, int[] designations)
int[]	cluster(DataSet dataSet, int clusters, int[] designations)
int[]	cluster(DataSet dataSet, int lowK, int highK, boolean parallel, int[] designations)
int[]	cluster(DataSet dataSet, int lowK, int highK, int[] designations)
java.util.List<java.util.List<DataPoint>>	getClusterDesignations(int clusters) Returns the assignment array for that would have been computed for the previous data set with the desired number of clusters.
int[]	getClusterDesignations(int[] designations, int clusters) Returns the assignment array for that would have been computed for the previous data set with the desired number of clusters.
boolean	hasStoredClustering() The PriorityHAC stores its merging order, so that multiple clusterings can of different sizes can be obtained without having to recluster the data set.

Methods inherited from class jsat.clustering.ClustererBase

createClusterListFromAssignmentArray, getDatapointsFromCluster

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.clustering.KClusterer

cluster, cluster, cluster, cluster

Methods inherited from interface jsat.clustering.Clusterer

cluster, cluster, supportsWeightedData

Constructor Detail

PriorityHAC

public PriorityHAC(UpdatableClusterDissimilarity dissMeasure)

PriorityHAC

public PriorityHAC(PriorityHAC 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,
                     boolean parallel,
                     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

parallel - true if multiple threads should be used to perform clustering. false if it should be done in a single threaded manner.

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:

the int[]

cluster

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

cluster

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

cluster

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

cluster

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

hasStoredClustering

public boolean hasStoredClustering()

The PriorityHAC stores its merging order, so that multiple clusterings can of different sizes can be obtained without having to recluster the data set. This is possible in part because HAC is deterministic.
This returns true if there is currently a data set and its merge order stored.

Returns:

true if you can call for more clusterings, false if no data set has been clustered.

getClusterDesignations

public int[] getClusterDesignations(int[] designations,
                                    int clusters)

Returns the assignment array for that would have been computed for the previous data set with the desired number of clusters.

Parameters:

designations - the array to store the assignments in

clusters - the number of clusters desired

Returns:

the original array passed in, or null if no data set has been clustered.

See Also:

hasStoredClustering()

getClusterDesignations

public java.util.List<java.util.List<DataPoint>> getClusterDesignations(int clusters)

Returns the assignment array for that would have been computed for the previous data set with the desired number of clusters.

Parameters:

clusters - the number of clusters desired

Returns:

the list of data points in each cluster, or null if no data set has been clustered.

See Also:

hasStoredClustering()

clone

public PriorityHAC clone()

Specified by:

clone in interface Clusterer

Specified by:

clone in interface KClusterer

Specified by:

clone in class KClustererBase