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package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2015
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import java.util.ArrayList;
import java.util.List;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.NumberVectorDistanceFunction;
import de.lmu.ifi.dbs.elki.math.random.RandomFactory;
import de.lmu.ifi.dbs.elki.utilities.Alias;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
/**
* Initialize K-means by randomly choosing k existing elements as cluster
* centers.
*
* This initialization is attributed to:
* <p>
* E. W. Forgy<br />
* Cluster analysis of multivariate data : efficiency versus interpretability of
* classifications<br />
* Abstract published in Biometrics 21(3)
* </p>
* but we were unable to verify this so far (apparently, only an abstract is
* available in Biometrics).
*
* @author Erich Schubert
* @since 0.4.0
*
* @param <O> Vector type
*/
@Reference(authors = "E. W. Forgy", //
title = "Cluster analysis of multivariate data: efficiency versus interpretability of classifications", //
booktitle = "Biometrics 21(3)")
@Alias("de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.RandomlyChosenInitialMeans")
public class RandomlyChosenInitialMeans<O> extends AbstractKMeansInitialization<NumberVector> implements KMedoidsInitialization<O> {
/**
* Constructor.
*
* @param rnd Random generator.
*/
public RandomlyChosenInitialMeans(RandomFactory rnd) {
super(rnd);
}
@Override
public <T extends NumberVector, V extends NumberVector> List<V> chooseInitialMeans(Database database, Relation<T> relation, int k, NumberVectorDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory) {
DBIDs ids = DBIDUtil.randomSample(relation.getDBIDs(), k, rnd);
List<V> means = new ArrayList<>(k);
for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
means.add(factory.newNumberVector(relation.get(iter)));
}
return means;
}
@Override
public DBIDs chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distanceFunction) {
return DBIDUtil.randomSample(ids, k, rnd);
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer<V> extends AbstractKMeansInitialization.Parameterizer {
@Override
protected RandomlyChosenInitialMeans<V> makeInstance() {
return new RandomlyChosenInitialMeans<>(rnd);
}
}
}
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