jsat.clustering

Interface KClusterer

All Superinterfaces:

Clusterer, java.io.Serializable

All Known Implementing Classes:

CLARA, DivisiveGlobalClusterer, DivisiveLocalClusterer, ElkanKernelKMeans, ElkanKMeans, EMGaussianMixture, GapStatistic, GMeans, HamerlyKMeans, KClustererBase, KernelKMeans, KMeans, KMeansPDN, LloydKernelKMeans, MEDDIT, MiniBatchKMeans, NaiveKMeans, NNChainHAC, PAM, PriorityHAC, SimpleHAC, TRIKMEDS, XMeans

public interface KClusterer
extends Clusterer

Defines a clustering method that requires the number of clusters in the data set to be known before hand.

Method Summary

Modifier and Type	Method and Description
KClusterer	clone()
default java.util.List<java.util.List<DataPoint>>	cluster(DataSet dataSet, int clusters) Performs clustering on the given data set.
default java.util.List<java.util.List<DataPoint>>	cluster(DataSet dataSet, int clusters, boolean parallel) Performs clustering on the given data set.
int[]	cluster(DataSet dataSet, int clusters, boolean parallel, int[] designations)
default java.util.List<java.util.List<DataPoint>>	cluster(DataSet dataSet, int lowK, int highK) Performs clustering on the given data set.
default int[]	cluster(DataSet dataSet, int clusters, int[] designations)
default java.util.List<java.util.List<DataPoint>>	cluster(DataSet dataSet, int lowK, int highK, boolean parallel) Performs clustering on the given data set.
int[]	cluster(DataSet dataSet, int lowK, int highK, boolean parallel, int[] designations)
default int[]	cluster(DataSet dataSet, int lowK, int highK, int[] designations)

Methods inherited from interface jsat.clustering.Clusterer

cluster, cluster, cluster, cluster, supportsWeightedData

Method Detail

cluster

default java.util.List<java.util.List<DataPoint>> cluster(DataSet dataSet,
                                                          int clusters,
                                                          boolean parallel)

Performs clustering on the given data set.

Parameters:

dataSet - the data points to perform clustering on

clusters - the number of clusters to assume

parallel - a source of threads to run tasks

Returns:

the java.util.List>

cluster

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

cluster

default java.util.List<java.util.List<DataPoint>> cluster(DataSet dataSet,
                                                          int clusters)

Performs clustering on the given data set.

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

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

cluster

default java.util.List<java.util.List<DataPoint>> cluster(DataSet dataSet,
                                                          int lowK,
                                                          int highK,
                                                          boolean parallel)

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

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

parallel - a source of threads to run tasks

Returns:

the java.util.List>

cluster

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

cluster

default java.util.List<java.util.List<DataPoint>> cluster(DataSet dataSet,
                                                          int lowK,
                                                          int highK)

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

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

cluster

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

clone

KClusterer clone()

Specified by:

clone in interface Clusterer