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

java.lang.Object
- elki.clustering.kmeans.AbstractKMeans<V,KMeansModel>
- - elki.clustering.kmeans.spherical.SphericalKMeans<V>
  - - elki.clustering.kmeans.spherical.SphericalHamerlyKMeans<V>

Type Parameters:

V - vector datatype

All Implemented Interfaces:

elki.Algorithm, ClusteringAlgorithm<Clustering<KMeansModel>>, KMeans<V,KMeansModel>
```
@Reference(authors="Erich Schubert, Andreas Lang, Gloria Feher",title="Accelerating Spherical k-Means",booktitle="Int. Conf. on Similarity Search and Applications, SISAP 2021",url="https://doi.org/10.1007/978-3-030-89657-7_17",bibkey="DBLP:conf/sisap/SchubertLF21") @Reference(authors="Erich Schubert",title="A Triangle Inequality for Cosine Similarity",booktitle="Int. Conf. on Similarity Search and Applications, SISAP 2021",url="https://doi.org/10.1007/978-3-030-89657-7_3",bibkey="DBLP:conf/sisap/Schubert21")
public class SphericalHamerlyKMeans<V extends elki.data.NumberVector>
extends SphericalKMeans<V>
```
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.
FIXME: currently requires the vectors to be L2 normalized beforehand
Reference:
Erich Schubert, Andreas Lang, Gloria Feher
Accelerating Spherical k-Means
Int. Conf. on Similarity Search and Applications, SISAP 2021
The underlying triangle inequality used for pruning is introduced in:
Erich Schubert
A Triangle Inequality for Cosine Similarity
Int. Conf. on Similarity Search and Applications, SISAP 2021

Since:

0.8.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes
Modifier and Type	Class	Description
`protected static class`	`SphericalHamerlyKMeans.Instance`	Inner instance, storing state for a single data set.
`static class`	`SphericalHamerlyKMeans.Par<V extends elki.data.NumberVector>`	Parameterization class.

Nested classes/interfaces inherited from interface elki.Algorithm
elki.Algorithm.Utils

Field Summary

Fields
Modifier and Type	Field	Description
`private static elki.logging.Logging`	`LOG`	The logger for this class.
`protected boolean`	`varstat`	Flag whether to compute the final variance statistic.

Fields inherited from class elki.clustering.kmeans.AbstractKMeans
distance, initializer, k, maxiter

Fields inherited from interface elki.clustering.kmeans.KMeans
DISTANCE_FUNCTION_ID, INIT_ID, K_ID, MAXITER_ID, SEED_ID, VARSTAT_ID

Constructor Summary

Constructors
Constructor	Description
`SphericalHamerlyKMeans(int k, int maxiter, KMeansInitialization initializer, boolean varstat)`	Constructor.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`protected elki.logging.Logging`	`getLogger()`	Get the (STATIC) logger for this class.
`Clustering<KMeansModel>`	`run(elki.database.relation.Relation<V> relation)`	Run the clustering algorithm.

Methods inherited from class elki.clustering.kmeans.spherical.SphericalKMeans
getInputTypeRestriction

Methods inherited from class elki.clustering.kmeans.AbstractKMeans
getDistance, incrementalUpdateMean, initialMeans, means, minusEquals, nearestMeans, plusEquals, plusMinusEquals, setDistance, setInitializer, setK

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

Methods inherited from interface elki.clustering.ClusteringAlgorithm
autorun

- Field Detail
  - LOG
```
private static final elki.logging.Logging LOG
```
    The logger for this class.
  - varstat
```
protected boolean varstat
```
    Flag whether to compute the final variance statistic.
- Constructor Detail
  - SphericalHamerlyKMeans
```
public SphericalHamerlyKMeans(int k,
                              int maxiter,
                              KMeansInitialization initializer,
                              boolean varstat)
```
    Constructor.
    
    Parameters:
    
    k - k parameter
    
    maxiter - Maxiter parameter
    
    initializer - Initialization method
    
    varstat - Compute the variance statistic
- Method Detail
  - run
```
public Clustering<KMeansModel> run(elki.database.relation.Relation<V> relation)
```
    Description copied from interface: KMeans
    
    Run the clustering algorithm.
    
    Specified by:
    
    run in interface KMeans<V extends elki.data.NumberVector,KMeansModel>
    
    Overrides:
    
    run in class SphericalKMeans<V extends elki.data.NumberVector>
    
    Parameters:
    
    relation - Relation to process.
    
    Returns:
    
    Clustering result
  - getLogger
```
protected elki.logging.Logging getLogger()
```
    Description copied from class: AbstractKMeans
    
    Get the (STATIC) logger for this class.
    
    Overrides:
    
    getLogger in class SphericalKMeans<V extends elki.data.NumberVector>
    
    Returns:
    
    the static logger