jsat.clustering.hierarchical

Class NNChainHAC

java.lang.Object

jsat.clustering.ClustererBase

jsat.clustering.KClustererBase

jsat.clustering.hierarchical.NNChainHAC

All Implemented Interfaces:

Serializable, Clusterer, KClusterer

public class NNChainHAC
extends KClustererBase

This class implements Hierarchical Agglomerative Clustering via the Nearest Neighbor Chain approach. This runs in O(n²) time for any Lance Williams dissimilarity and uses O(n) memory.
This implementation also supports multi-threaded execution. see:

• Müllner, D. (2011). Modern hierarchical, agglomerative clustering algorithms. arXiv Preprint arXiv:1109.2378. Retrieved from here
• Murtagh, F., & Contreras, P. (2011). Methods of Hierarchical Clustering. In Data Mining and Knowledge Discovery. Wiley-Interscience.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Modifier	Constructor and Description
	NNChainHAC() Creates a new NNChainHAC using the Ward method.
	NNChainHAC(LanceWilliamsDissimilarity distMeasure) Creates a new NNChainHAC
	NNChainHAC(LanceWilliamsDissimilarity distMeasure, DistanceMetric distance) Creates a new NNChain using the given dissimilarity measure and distance metric.
protected	NNChainHAC(NNChainHAC toCopy) Copy constructor

Method Summary

Modifier and Type	Method and Description
NNChainHAC	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)
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.
List<List<DataPoint>>	getClusterDesignations(int clusters, DataSet data) Returns the assignment array for that would have been computed for the previous data set with the desired number of clusters.

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

NNChainHAC

public NNChainHAC()

Creates a new NNChainHAC using the Ward method.

NNChainHAC

public NNChainHAC(LanceWilliamsDissimilarity distMeasure)

Creates a new NNChainHAC

Parameters:

distMeasure - the dissimilarity measure to use

NNChainHAC

public NNChainHAC(LanceWilliamsDissimilarity distMeasure,
                  DistanceMetric distance)

Creates a new NNChain using the given dissimilarity measure and distance metric. The correctness guarantees may not hold for distances other than the Euclidean distance, which is the norm for Hierarchical Cluster.

Parameters:

distMeasure - the dissimilarity measure to use

distance - the distance metric to use

NNChainHAC

protected NNChainHAC(NNChainHAC toCopy)

Copy constructor

Parameters:

toCopy - the object to copy

Method Detail

clone

public NNChainHAC clone()

Specified by:

clone in interface Clusterer

Specified by:

clone in interface KClusterer

Specified by:

clone in class KClustererBase

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

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 List<List<DataPoint>> getClusterDesignations(int clusters,
                                                    DataSet data)

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

data -

Returns:

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

See Also:

#hasStoredClustering()

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)