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

java.lang.Object
- elki.clustering.kmeans.AbstractKMeans<V,MeanModel>
- - elki.clustering.kmeans.KMediansLloyd<V>

Type Parameters:

V - vector datatype

All Implemented Interfaces:

elki.Algorithm, ClusteringAlgorithm<Clustering<MeanModel>>, KMeans<V,MeanModel>
```
@Reference(title="Clustering via Concave Minimization",
           authors="P. S. Bradley, O. L. Mangasarian, W. N. Street",
           booktitle="Advances in Neural Information Processing Systems",
           url="https://papers.nips.cc/paper/1260-clustering-via-concave-minimization",
           bibkey="DBLP:conf/nips/BradleyMS96")
public class KMediansLloyd<V extends elki.data.NumberVector>
extends AbstractKMeans<V,MeanModel>
```
k-medians clustering algorithm, but using Lloyd-style bulk iterations instead of the more complicated approach suggested by Kaufman and Rousseeuw (see PAM instead).
Reference:
Clustering via Concave Minimization
P. S. Bradley, O. L. Mangasarian, W. N. Street
Advances in Neural Information Processing Systems (NIPS'96)

Since:

0.5.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes
Modifier and Type	Class	Description
`protected static class`	`KMediansLloyd.Instance`	Inner instance, storing state for a single data set.
`static class`	`KMediansLloyd.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.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
`KMediansLloyd(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)`	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<MeanModel>`	`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
  - KMediansLloyd
```
public KMediansLloyd(elki.distance.NumberVectorDistance<? super V> distance,
                     int k,
                     int maxiter,
                     KMeansInitialization initializer)
```
    Constructor.
    
    Parameters:
    
    distance - distance function
    
    k - k parameter
    
    maxiter - Maxiter parameter
    
    initializer - Initialization method
- Method Detail
  - run
```
public Clustering<MeanModel> run(elki.database.relation.Relation<V> relation)
```
    Description copied from interface: KMeans
    
    Run the clustering algorithm.
    
    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.
    
    Specified by:
    
    getLogger in class AbstractKMeans<V extends elki.data.NumberVector,MeanModel>
    
    Returns:
    
    the static logger