KMeansPlusPlusClusterer (Commons Math 2.2 API)

java.lang.Object
- org.apache.commons.math.stat.clustering.KMeansPlusPlusClusterer<T>

Type Parameters:

T - type of the points to cluster
```
public class KMeansPlusPlusClusterer<T extends Clusterable<T>>
extends java.lang.Object
```
Clustering algorithm based on David Arthur and Sergei Vassilvitski k-means++ algorithm.

Since:

2.0

Version:

$Revision: 1054333 $ $Date: 2011-01-02 01:34:58 +0100 (dim. 02 janv. 2011) $

See Also:

K-means++ (wikipedia)

Nested Class Summary

Nested Classes
Modifier and Type Class Description

static class KMeansPlusPlusClusterer.EmptyClusterStrategy
Strategies to use for replacing an empty cluster.

Constructor Summary

Constructors
Constructor	Description
`KMeansPlusPlusClusterer(java.util.Random random)`	Build a clusterer.
`KMeansPlusPlusClusterer(java.util.Random random, KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy)`	Build a clusterer.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`java.util.List<Cluster<T>>`	`cluster(java.util.Collection<T> points, int k, int maxIterations)`	Runs the K-means++ clustering algorithm.

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

- Constructor Detail
  - KMeansPlusPlusClusterer
```
public KMeansPlusPlusClusterer(java.util.Random random)
```
    Build a clusterer.
    The default strategy for handling empty clusters that may appear during algorithm iterations is to split the cluster with largest distance variance.
    
    Parameters:
    
    random - random generator to use for choosing initial centers
  - KMeansPlusPlusClusterer
```
public KMeansPlusPlusClusterer(java.util.Random random,
                               KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy)
```
    Build a clusterer.
    
    Parameters:
    
    random - random generator to use for choosing initial centers
    
    emptyStrategy - strategy to use for handling empty clusters that may appear during algorithm iterations
    
    Since:
    
    2.2
- Method Detail
  - cluster
```
public java.util.List<Cluster<T>> cluster(java.util.Collection<T> points,
                                          int k,
                                          int maxIterations)
```
    Runs the K-means++ clustering algorithm.
    
    Parameters:
    
    points - the points to cluster
    
    k - the number of clusters to split the data into
    
    maxIterations - the maximum number of iterations to run the algorithm for. If negative, no maximum will be used
    
    Returns:
    
    a list of clusters containing the points