jsat.clustering

Class KClustererBase

java.lang.Object

jsat.clustering.ClustererBase

jsat.clustering.KClustererBase

All Implemented Interfaces:

Serializable, Clusterer, KClusterer

Direct Known Subclasses:

DivisiveGlobalClusterer, DivisiveLocalClusterer, EMGaussianMixture, GapStatistic, KernelKMeans, KMeans, MiniBatchKMeans, NNChainHAC, PAM, PriorityHAC, SimpleHAC

public abstract class KClustererBase
extends ClustererBase
implements KClusterer

A base foundation that provides an implementation of the methods that return a list of lists for the clusterings using their int array counterparts.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
KClustererBase()

Method Summary

Modifier and Type	Method and Description
abstract KClusterer	clone()
List<List<DataPoint>>	cluster(DataSet dataSet, int clusters) Performs clustering on the given data set.
List<List<DataPoint>>	cluster(DataSet dataSet, int clusters, ExecutorService threadpool) Performs clustering on the given data set.
List<List<DataPoint>>	cluster(DataSet dataSet, int lowK, int highK) Performs clustering on the given data set.
List<List<DataPoint>>	cluster(DataSet dataSet, int lowK, int highK, ExecutorService threadpool) Performs clustering on the given data set.

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.KClusterer

cluster, cluster, cluster, cluster

Methods inherited from interface jsat.clustering.Clusterer

cluster, cluster, cluster, cluster, supportsWeightedData

Constructor Detail

KClustererBase

public KClustererBase()

Method Detail

cluster

public List<List<DataPoint>> cluster(DataSet dataSet,
                                     int clusters,
                                     ExecutorService threadpool)

Description copied from interface: KClusterer

Performs clustering on the given data set.

Specified by:

cluster in interface KClusterer

Parameters:

dataSet - the data points to perform clustering on

clusters - the number of clusters to assume

threadpool - a source of threads to run tasks

Returns:

A list of DataSets, where each DataSet contains the data points for one cluster in the group

cluster

public List<List<DataPoint>> cluster(DataSet dataSet,
                                     int clusters)

Description copied from interface: KClusterer

Performs clustering on the given data set.

Specified by:

cluster in interface KClusterer

Parameters:

dataSet - the data points to perform clustering on

clusters - the number of clusters to assume

Returns:

A list of DataSets, where each DataSet contains the data points for one cluster in the group

cluster

public List<List<DataPoint>> cluster(DataSet dataSet,
                                     int lowK,
                                     int highK,
                                     ExecutorService threadpool)

Description copied from interface: KClusterer

Performs clustering on the given data set. The implementation will attempt to determine the best number of clusters for the given data.

Specified by:

cluster in interface KClusterer

Parameters:

dataSet - the data points to perform clustering on

lowK - the lower bound, inclusive, of the range to search

highK - the upper bound, inclusive, of the range to search

threadpool - a source of threads to run tasks

Returns:

A list of DataSets, where each DataSet contains the data points for one cluster in the group

cluster

public List<List<DataPoint>> cluster(DataSet dataSet,
                                     int lowK,
                                     int highK)

Description copied from interface: KClusterer

Performs clustering on the given data set. The implementation will attempt to determine the best number of clusters for the given data.

Specified by:

cluster in interface KClusterer

Parameters:

dataSet - the data points to perform clustering on

lowK - the lower bound, inclusive, of the range to search

highK - the upper bound, inclusive, of the range to search

Returns:

A list of DataSets, where each DataSet contains the data points for one cluster in the group

clone

public abstract KClusterer clone()

Specified by:

clone in interface Clusterer

Specified by:

clone in interface KClusterer

Specified by:

clone in class ClustererBase