OnePassExclusiveClustering (thomasjungblut-common 2.0.0 API)

java.lang.Object
- de.jungblut.clustering.OnePassExclusiveClustering

```
public final class OnePassExclusiveClustering
extends java.lang.Object
```
A one pass exclusive clustering algorithm. As the name suggests, the clustering algorithm will iterate once over a constructed kd-tree and find nearest neighbours inside a distance threshold. The found neighbours are going to be put into a bitset and will be omitted from search in the following kd-tree searches. Found clusters are checked against a minimum size and maybe discarded when not reaching the configured threshold. This is considered a very fast algorithm, it can be used instead of CanopyClustering.

Author:

thomas.jungblut

Constructor Summary

Constructors
Constructor	Description
`OnePassExclusiveClustering(double t1)`	Constructs a one pass clustering algorithm.
`OnePassExclusiveClustering(double t1, int k, int minSize, boolean mergeOverlaps)`	Constructs a one pass clustering algorithm.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`java.util.List<de.jungblut.math.DoubleVector>`	`cluster(java.util.List<de.jungblut.math.DoubleVector> values, boolean verbose)`	Cluster the given items.

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

- Constructor Detail
  - OnePassExclusiveClustering
```
public OnePassExclusiveClustering(double t1)
```
    Constructs a one pass clustering algorithm. With unlimited maximum number of neighbours retrieved and a minimum cluster size of 2.
    
    Parameters:
    
    t1 - the maximum distance of neighbourhood.
  - OnePassExclusiveClustering
```
public OnePassExclusiveClustering(double t1,
                                  int k,
                                  int minSize,
                                  boolean mergeOverlaps)
```
    Constructs a one pass clustering algorithm.
    
    Parameters:
    
    t1 - the maximum distance of neighbourhood.
    
    k - the maximum number of neighbours to retrieve inside the t1 threshold.
    
    minSize - the minimum size of a cluster.
    
    mergeOverlaps - if true, overlapping found centers by t1 distance will be merged.
- Method Detail
  - cluster
```
public java.util.List<de.jungblut.math.DoubleVector> cluster(java.util.List<de.jungblut.math.DoubleVector> values,
                                                             boolean verbose)
```
    Cluster the given items.
    
    Parameters:
    
    values - the vectors to cluster.
    
    verbose - if true, outputs progress to STDOUT.
    
    Returns:
    
    a list of centers that describe the given vectors.