de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Class FirstKInitialMeans<V>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.FirstKInitialMeans<V>

Type Parameters:

V - Vector type

All Implemented Interfaces:

KMeansInitialization<V>, KMedoidsInitialization<V>

public class FirstKInitialMeans<V>
extends Object
implements KMeansInitialization<V>, KMedoidsInitialization<V>

Initialize K-means by using the first k objects as initial means.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	FirstKInitialMeans.Parameterizer<V extends NumberVector<?>> Parameterization class.

Constructor Summary

Constructors

Constructor and Description
FirstKInitialMeans() Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
List<V>	chooseInitialMeans(Relation<V> relation, int k, PrimitiveDistanceFunction<? super V,?> distanceFunction) Choose initial means
DBIDs	chooseInitialMedoids(int k, DistanceQuery<? super V,?> distanceFunction) Choose initial means

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

FirstKInitialMeans

public FirstKInitialMeans()

Constructor.

Method Detail

chooseInitialMeans

public List<V> chooseInitialMeans(Relation<V> relation,
                         int k,
                         PrimitiveDistanceFunction<? super V,?> distanceFunction)

Description copied from interface: KMeansInitialization

Choose initial means

Specified by:

chooseInitialMeans in interface KMeansInitialization<V>

Parameters:

relation - Relation

k - Parameter k

distanceFunction - Distance function

Returns:

List of chosen means for k-means

chooseInitialMedoids

public DBIDs chooseInitialMedoids(int k,
                         DistanceQuery<? super V,?> distanceFunction)

Description copied from interface: KMedoidsInitialization

Choose initial means

Specified by:

chooseInitialMedoids in interface KMedoidsInitialization<V>

Parameters:

k - Parameter k

distanceFunction - Distance function

Returns:

List of chosen means for k-means