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

java.lang.Object
- elki.clustering.kmeans.AbstractKMeans<V,KMeansModel>
- - elki.clustering.kmeans.HamerlyKMeans<V>
  - - elki.clustering.kmeans.AnnulusKMeans<V>

Type Parameters:

V - vector datatype

All Implemented Interfaces:

elki.Algorithm, ClusteringAlgorithm<Clustering<KMeansModel>>, KMeans<V,KMeansModel>
```
@Reference(authors="J. Drake",title="Faster k-means clustering",booktitle="Faster k-means clustering",url="http://hdl.handle.net/2104/8826",bibkey="mathesis/Drake13") @Reference(authors="G. Hamerly and J. Drake",title="Accelerating Lloyd\u2019s Algorithm for k-Means Clustering",booktitle="Partitional Clustering Algorithms",url="https://doi.org/10.1007/978-3-319-09259-1_2",bibkey="doi:10.1007/978-3-319-09259-1_2")
public class AnnulusKMeans<V extends elki.data.NumberVector>
extends HamerlyKMeans<V>
```
Annulus k-means algorithm. A variant of Hamerly with an additional bound, based on comparing the norm of the mean and the norm of the points.
This implementation could be further improved by precomputing and storing the norms of all points (at the cost of O(n) memory additionally).
Reference:
J. Drake
Faster k-means clustering
Masters Thesis
G. Hamerly and J. Drake
Accelerating Lloyd’s Algorithm for k-Means Clustering
Partitional Clustering Algorithms

Since:

0.7.5

Author:

Erich Schubert

Nested Class Summary

Nested Classes
Modifier and Type	Class	Description
`protected static class`	`AnnulusKMeans.Instance`	Inner instance, storing state for a single data set.
`static class`	`AnnulusKMeans.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.
- Fields inherited from class elki.clustering.kmeans.HamerlyKMeans
  varstat
- 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
`AnnulusKMeans(elki.distance.NumberVectorDistance<? super V> distance, 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.AbstractKMeans
getDistance, getInputTypeRestriction, 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.
- Constructor Detail
  - AnnulusKMeans
```
public AnnulusKMeans(elki.distance.NumberVectorDistance<? super V> distance,
                     int k,
                     int maxiter,
                     KMeansInitialization initializer,
                     boolean varstat)
```
    Constructor.
    
    Parameters:
    
    distance - distance function
    
    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 HamerlyKMeans<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 HamerlyKMeans<V extends elki.data.NumberVector>
    
    Returns:
    
    the static logger