KMeansClusterer

java.lang.Object
- org.ojalgo.data.cluster.FeatureBasedClusterer
- - org.ojalgo.data.cluster.KMeansClusterer

All Implemented Interfaces:: ClusteringAlgorithm<Point>

final class KMeansClusterer
extends FeatureBasedClusterer

Field Summary

Fields
Modifier and Type Field Description

private GeneralisedKMeans<Point> myClusterer

private RandomClustering<Point> myInitialiser

Constructor Summary

Constructors
Constructor Description

KMeansClusterer(int k, DistanceMeasure measure)

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`java.util.List<java.util.Set<Point>>`	`cluster(java.util.Collection<Point> input)`	Partitions the given items into clusters.

Methods inherited from class org.ojalgo.data.cluster.FeatureBasedClusterer
centroid, cluster, distance, distance, getThreshold, initialiser, isSquared, newAutomatic, newAutomatic, newGreedy, newGreedy, newKMeans, newKMeans, newSpectral, newSpectral, setup

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - myClusterer
```
private final GeneralisedKMeans<Point> myClusterer
```
  - myInitialiser
```
private final RandomClustering<Point> myInitialiser
```
- Constructor Detail
  - KMeansClusterer
```
KMeansClusterer(int k,
                DistanceMeasure measure)
```
- Method Detail
  - cluster
```
public java.util.List<java.util.Set<Point>> cluster(java.util.Collection<Point> input)
```
    Description copied from interface: ClusteringAlgorithm
    
    Partitions the given items into clusters.
    
    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.