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package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans;
/*
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.algorithm.clustering.kmeans.initialization.KMeansInitialization;
import de.lmu.ifi.dbs.elki.data.Cluster;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.model.MeanModel;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.datastore.DataStoreFactory;
import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
import de.lmu.ifi.dbs.elki.database.datastore.WritableIntegerDataStore;
import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
import de.lmu.ifi.dbs.elki.database.ids.ModifiableDBIDs;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.NumberVectorDistanceFunction;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.progress.IndefiniteProgress;
import de.lmu.ifi.dbs.elki.logging.statistics.LongStatistic;
import de.lmu.ifi.dbs.elki.logging.statistics.StringStatistic;
import de.lmu.ifi.dbs.elki.math.linearalgebra.Vector;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
/**
* k-medians clustering algorithm, but using Lloyd-style bulk iterations instead
* of the more complicated approach suggested by Kaufman and Rousseeuw (see
* {@link KMedoidsPAM} instead).
*
* Reference:
* <p>
* Clustering via Concave Minimization<br />
* P. S. Bradley, O. L. Mangasarian, W. N. Street<br />
* in: Advances in Neural Information Processing Systems (NIPS)
* </p>
*
* @author Erich Schubert
*
* @apiviz.has MeanModel
*
* @param <V> vector datatype
*/
@Title("K-Medians")
@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.pdf")
public class KMediansLloyd<V extends NumberVector> extends AbstractKMeans<V, MeanModel> {
/**
* The logger for this class.
*/
private static final Logging LOG = Logging.getLogger(KMediansLloyd.class);
/**
* Key for statistics logging.
*/
private static final String KEY = KMediansLloyd.class.getName();
/**
* Constructor.
*
* @param distanceFunction distance function
* @param k k parameter
* @param maxiter Maxiter parameter
* @param initializer Initialization method
*/
public KMediansLloyd(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization<? super V> initializer) {
super(distanceFunction, k, maxiter, initializer);
}
@Override
public Clustering<MeanModel> run(Database database, Relation<V> relation) {
if(relation.size() <= 0) {
return new Clustering<>("k-Medians Clustering", "kmedians-clustering");
}
// Choose initial medians
if(LOG.isStatistics()) {
LOG.statistics(new StringStatistic(KEY + ".initialization", initializer.toString()));
}
List<Vector> medians = initializer.chooseInitialMeans(database, relation, k, getDistanceFunction(), Vector.FACTORY);
// Setup cluster assignment store
List<ModifiableDBIDs> clusters = new ArrayList<>();
for(int i = 0; i < k; i++) {
clusters.add(DBIDUtil.newHashSet((int) (relation.size() * 2. / k)));
}
WritableIntegerDataStore assignment = DataStoreUtil.makeIntegerStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, -1);
double[] distsum = new double[k];
IndefiniteProgress prog = LOG.isVerbose() ? new IndefiniteProgress("K-Medians iteration", LOG) : null;
int iteration = 0;
for(; maxiter <= 0 || iteration < maxiter; iteration++) {
LOG.incrementProcessed(prog);
boolean changed = assignToNearestCluster(relation, medians, clusters, assignment, distsum);
// Stop if no cluster assignment changed.
if(!changed) {
break;
}
// Recompute medians.
medians = medians(clusters, medians, relation);
}
LOG.setCompleted(prog);
if(LOG.isStatistics()) {
LOG.statistics(new LongStatistic(KEY + ".iterations", iteration));
}
// Wrap result
Clustering<MeanModel> result = new Clustering<>("k-Medians Clustering", "kmedians-clustering");
for(int i = 0; i < clusters.size(); i++) {
MeanModel model = new MeanModel(medians.get(i));
result.addToplevelCluster(new Cluster<>(clusters.get(i), model));
}
return result;
}
@Override
protected Logging getLogger() {
return LOG;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer<V extends NumberVector> extends AbstractKMeans.Parameterizer<V> {
@Override
protected Logging getLogger() {
return LOG;
}
@Override
protected KMediansLloyd<V> makeInstance() {
return new KMediansLloyd<>(distanceFunction, k, maxiter, initializer);
}
}
}
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