jsat.clustering

Interface Clusterer

All Superinterfaces:

java.io.Serializable

All Known Subinterfaces:

KClusterer

All Known Implementing Classes:

CLARA, ClustererBase, DBSCAN, DivisiveGlobalClusterer, DivisiveLocalClusterer, ElkanKernelKMeans, ElkanKMeans, EMGaussianMixture, FLAME, GapStatistic, GMeans, HamerlyKMeans, HDBSCAN, KClustererBase, KernelKMeans, KMeans, KMeansPDN, LloydKernelKMeans, LSDBC, MeanShift, MEDDIT, MiniBatchKMeans, NaiveKMeans, NNChainHAC, OPTICS, PAM, PriorityHAC, SimpleHAC, TRIKMEDS, XMeans

public interface Clusterer
extends java.io.Serializable

Defines the interface for a generic clustering algorithm.

Method Summary

Modifier and Type	Method and Description
Clusterer	clone()
default java.util.List<java.util.List<DataPoint>>	cluster(DataSet dataSet) Performs clustering on the given data set.
default java.util.List<java.util.List<DataPoint>>	cluster(DataSet dataSet, boolean parallel) Performs clustering on the given data set.
int[]	cluster(DataSet dataSet, boolean parallel, int[] designations) Performs clustering on the given data set.
default int[]	cluster(DataSet dataSet, int[] designations) Performs clustering on the given data set.
default boolean	supportsWeightedData() Indicates whether the model knows how to cluster using weighted data points.

Method Detail

cluster

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

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

Returns:

A list of clusters found by this method.

cluster

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

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

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

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.

Returns:

the java.util.List>

cluster

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

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[]

supportsWeightedData

default boolean supportsWeightedData()

Indicates whether the model knows how to cluster using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.

Returns:

true if the model supports weighted data, false otherwise

clone

Clusterer clone()