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

Interface KMeansInitialization<V extends NumberVector>

Type Parameters:

V - Vector type

All Known Implementing Classes:

AbstractKMeansInitialization, FarthestPointsInitialMeans, FarthestSumPointsInitialMeans, FirstKInitialMeans, KMeansPlusPlusInitialMeans, PAMInitialMeans, PredefinedInitialMeans, RandomlyChosenInitialMeans, RandomlyGeneratedInitialMeans, SampleKMeansInitialization

public interface KMeansInitialization<V extends NumberVector>

Interface for initializing K-Means

Author:

Erich Schubert

Method Summary

Methods

Modifier and Type	Method and Description
<T extends V,O extends NumberVector> List<O>	chooseInitialMeans(Database database, Relation<T> relation, int k, NumberVectorDistanceFunction<? super T> distanceFunction, NumberVector.Factory<O> factory) Choose initial means

Method Detail

chooseInitialMeans

<T extends V,O extends NumberVector> List<O> chooseInitialMeans(Database database,
                                                              Relation<T> relation,
                                                              int k,
                                                              NumberVectorDistanceFunction<? super T> distanceFunction,
                                                              NumberVector.Factory<O> factory)

Choose initial means

Type Parameters:

T - Input vector type

O - Output vector type

Parameters:

database - Database context

relation - Relation

k - Parameter k

distanceFunction - Distance function

factory - Factory for output vectors.

Returns:

List of chosen means for k-means