SpectralClusterer

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

All Implemented Interfaces:

ClusteringAlgorithm<Point>
```
final class SpectralClusterer
extends FeatureBasedClusterer
```
Spectral clustering for Points using a fully connected RBF similarity graph and the symmetric normalised Laplacian. Pairwise distances are computed with the configured DistanceMeasure; the kernel scale is set from the median distance. The algorithm computes the k eigenvectors corresponding to the smallest eigenvalues of L = I - D^{-1/2} W D^{-1/2}, row-normalises the embedding, runs k-means, and maps clusters back to the original points. For n == 0 it returns empty; for n <= k it returns singletons. Requires point ids contiguous in [0,n). Complexity dominated by O(n^2) pairwise work and an eigen-decomposition of the n×n Laplacian.

Field Summary

Fields
Modifier and Type Field Description

private FeatureBasedClusterer myClusterer

private int myK

Constructor Summary

Constructors
Constructor Description

SpectralClusterer(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
  - myK
```
private final int myK
```
  - myClusterer
```
private final FeatureBasedClusterer myClusterer
```
- Constructor Detail
  - SpectralClusterer
```
SpectralClusterer(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.