jsat.clustering

Interface Clusterer

All Superinterfaces:

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, MiniBatchKMeans, NaiveKMeans, NNChainHAC, OPTICS, PAM, PriorityHAC, SimpleHAC, XMeans

public interface Clusterer
extends Serializable

Defines the interface for a generic clustering algorithm.

Author:

Edward Raff

Method Summary

Modifier and Type	Method and Description
Clusterer	clone()
List<List<DataPoint>>	cluster(DataSet dataSet) Performs clustering on the given data set.
List<List<DataPoint>>	cluster(DataSet dataSet, ExecutorService threadpool) Performs clustering on the given data set.
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.
boolean	supportsWeightedData() Indicates whether the model knows how to cluster using weighted data points.

Method Detail

cluster

List<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

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

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

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

Returns:

list of clusters found by this method.

cluster

int[] cluster(DataSet dataSet,
              ExecutorService threadpool,
              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

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

supportsWeightedData

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()