Class GeneralisedKMeans<T>

java.lang.Object

org.ojalgo.data.cluster.GeneralisedKMeans<T>

All Implemented Interfaces:

ClusteringAlgorithm<T>

public final class GeneralisedKMeans<T>
extends Object
implements ClusteringAlgorithm<T>

Contains the outline of the k-means algorithm, but designed for customisation.

• Works with any type of data
• Allows for custom distance calculations
• Allows for custom centroid initialisation and updating

Field Summary

Fields

Modifier and Type	Field	Description
private static final NumberContext	ACCURACY
private final Function<Collection<T>, List<T>>	myCentroidInitialiser
private final Function<Collection<T>, T>	myCentroidUpdater
private final ToDoubleBiFunction<T,T>	myDistanceCalculator

Constructor Summary

Constructors

Constructor	Description
GeneralisedKMeans(Function<Collection<T>, List<T>> centroidInitialiser, Function<Collection<T>, T> centroidUpdater, ToDoubleBiFunction<T,T> distanceCalculator)	You have to configure how distances are measured and how centroids are derived.

Method Summary

Modifier and Type	Method	Description
List<Set<T>>	cluster(Collection<T> input)	Partitions the given items into clusters.

Methods inherited from class Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Details

ACCURACY

private static final NumberContext ACCURACY

myCentroidUpdater

private final Function<Collection<T>, T> myCentroidUpdater

myDistanceCalculator

private final ToDoubleBiFunction<T,T> myDistanceCalculator

myCentroidInitialiser

private final Function<Collection<T>, List<T>> myCentroidInitialiser

Constructor Details

GeneralisedKMeans

public GeneralisedKMeans(Function<Collection<T>, List<T>> centroidInitialiser,
 Function<Collection<T>, T> centroidUpdater,
 ToDoubleBiFunction<T,T> distanceCalculator)

You have to configure how distances are measured and how centroids are derived.

Parameters:

centroidInitialiser - The initialisation function should return a list of k centroids. This function determines 'K'.

centroidUpdater - The update function should return a new centroid based on a collection of points (the set of items in a cluster).

distanceCalculator - A function that calculates the distance between two points.

Method Details

cluster

public List<Set<T>> cluster(Collection<T> input)

Description copied from interface: ClusteringAlgorithm

Partitions the given items into clusters.

Specified by:

cluster in interface ClusteringAlgorithm<T>

Parameters:

input - The items to cluster; must not be null. May be empty.

Returns:

A list of clusters; each element of the list is a Set<T> representing one cluster and containing its members. Sets are non-empty and pairwise disjoint; ordering is unspecified.