SquaredErrors (ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures)

java.lang.Object
- elki.evaluation.clustering.internal.SquaredErrors

All Implemented Interfaces:

Evaluator, ResultProcessor
```
public class SquaredErrors
extends java.lang.Object
implements Evaluator
```
Evaluate a clustering by reporting the squared errors (SSE, SSQ), as used by k-means. This should be used with SquaredEuclideanDistance only (when used with other distances, it will manually square the values; but beware that the result is less meaningful with other distance functions).
For clusterings that provide a cluster prototype object (e.g., k-means), the prototype will be used. For other algorithms, the centroid will be recomputed.
TODO: support non-vector based clusterings, too, if the algorithm provided a prototype object (e.g., PAM).
TODO: when combined with k-means, detect if the distance functions agree (both should be using squared Euclidean), and reuse the SSQ values provided by k-means.

Since:

0.7.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes
Modifier and Type Class Description

static class SquaredErrors.Par
Parameterization class.

Field Summary

Fields
Modifier and Type	Field	Description
`private NumberVectorDistance<?>`	`distance`	Distance function to use.
`private java.lang.String`	`key`	Key for logging statistics.
`private static Logging`	`LOG`	Logger for debug output.
`private NoiseHandling`	`noiseOption`	Handling of Noise clusters

Constructor Summary

Constructors
Constructor Description

SquaredErrors(NumberVectorDistance<?> distance, NoiseHandling noiseOption)
Constructor.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`double`	`evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)`	Evaluate a single clustering.
`void`	`processNewResult(java.lang.Object result)`	Process a result.

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

- Field Detail
  - LOG
```
private static final Logging LOG
```
    Logger for debug output.
  - noiseOption
```
private NoiseHandling noiseOption
```
    Handling of Noise clusters
  - distance
```
private NumberVectorDistance<?> distance
```
    Distance function to use.
  - key
```
private java.lang.String key
```
    Key for logging statistics.
- Constructor Detail
  - SquaredErrors
```
public SquaredErrors(NumberVectorDistance<?> distance,
                     NoiseHandling noiseOption)
```
    Constructor.
    
    Parameters:
    
    distance - Distance function to use.
    
    noiseOption - Control noise handling.
- Method Detail
  - evaluateClustering
```
public double evaluateClustering(Relation<? extends NumberVector> rel,
                                 Clustering<?> c)
```
    Evaluate a single clustering.
    
    Parameters:
    
    rel - Data relation
    
    c - Clustering
    
    Returns:
    
    ssq
  - processNewResult
```
public void processNewResult(java.lang.Object result)
```
    Description copied from interface: ResultProcessor
    
    Process a result.
    
    Specified by:
    
    processNewResult in interface ResultProcessor
    
    Parameters:
    
    result - Newly added result subtree.