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Diffstat (limited to 'src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/initialization/PAMInitialMeans.java')
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diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/initialization/PAMInitialMeans.java b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/initialization/PAMInitialMeans.java new file mode 100644 index 00000000..812ea4ab --- /dev/null +++ b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/initialization/PAMInitialMeans.java @@ -0,0 +1,180 @@ +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) 2014 + 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.datastore.DataStoreFactory; +import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil; +import de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore; +import de.lmu.ifi.dbs.elki.database.ids.ArrayModifiableDBIDs; +import de.lmu.ifi.dbs.elki.database.ids.DBID; +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.PrimitiveDistanceFunction; +import de.lmu.ifi.dbs.elki.math.Mean; +import de.lmu.ifi.dbs.elki.utilities.documentation.Reference; +import de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException; +import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer; + +/** + * PAM initialization for k-means (and of course, PAM). + * + * Reference: + * <p> + * Clustering my means of Medoids<br /> + * Kaufman, L. and Rousseeuw, P.J.<br /> + * in: Statistical Data Analysis Based on the L_1–Norm and Related Methods + * </p> + * + * TODO: enforce using a distance matrix? + * + * @author Erich Schubert + * + * @param <O> Object type for KMedoids initialization + */ +@Reference(title = "Clustering my means of Medoids", // +authors = "Kaufman, L. and Rousseeuw, P.J.", // +booktitle = "Statistical Data Analysis Based on the L_1–Norm and Related Methods") +public class PAMInitialMeans<O> implements KMeansInitialization<NumberVector>, KMedoidsInitialization<O> { + /** + * Constructor. + */ + public PAMInitialMeans() { + super(); + } + + @Override + public <T extends NumberVector, V extends NumberVector> List<V> chooseInitialMeans(Database database, Relation<T> relation, int k, PrimitiveDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory) { + // Ugly cast; but better than code duplication. + @SuppressWarnings("unchecked") + Relation<O> rel = (Relation<O>) relation; + // Get a distance query + @SuppressWarnings("unchecked") + final PrimitiveDistanceFunction<? super O> distF = (PrimitiveDistanceFunction<? super O>) distanceFunction; + final DistanceQuery<O> distQ = database.getDistanceQuery(rel, distF); + DBIDs medids = chooseInitialMedoids(k, rel.getDBIDs(), distQ); + List<V> medoids = new ArrayList<>(k); + for(DBIDIter iter = medids.iter(); iter.valid(); iter.advance()) { + medoids.add(factory.newNumberVector(relation.get(iter))); + } + return medoids; + } + + @Override + public DBIDs chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distQ) { + ArrayModifiableDBIDs medids = DBIDUtil.newArray(k); + double best = Double.POSITIVE_INFINITY; + Mean mean = new Mean(); // Mean is numerically more stable than sum. + WritableDoubleDataStore mindist = null; + + // First mean is chosen by having the smallest distance sum to all others. + { + DBID bestid = null; + WritableDoubleDataStore bestd = null; + for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) { + WritableDoubleDataStore newd = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP); + mean.reset(); + for(DBIDIter iter2 = ids.iter(); iter2.valid(); iter2.advance()) { + double d = distQ.distance(iter, iter2); + mean.put(d); + newd.putDouble(iter2, d); + } + if(mean.getMean() < best) { + best = mean.getMean(); + bestid = DBIDUtil.deref(iter); + if(bestd != null) { + bestd.destroy(); + } + bestd = newd; + } + else { + newd.destroy(); + } + } + medids.add(bestid); + mindist = bestd; + } + assert (mindist != null); + + // Subsequent means optimize the full criterion. + for(int i = 1; i < k; i++) { + DBID bestid = null; + WritableDoubleDataStore bestd = null; + for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) { + if(medids.contains(iter)) { + continue; + } + WritableDoubleDataStore newd = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP); + mean.reset(); + for(DBIDIter iter2 = ids.iter(); iter2.valid(); iter2.advance()) { + double dn = distQ.distance(iter, iter2); + double v = Math.min(dn, mindist.doubleValue(iter2)); + mean.put(v); + newd.put(iter2, v); + } + assert (mean.getCount() == ids.size()); + if(mean.getMean() < best) { + best = mean.getMean(); + bestid = DBIDUtil.deref(iter); + if(bestd != null) { + bestd.destroy(); + } + bestd = newd; + } + else { + newd.destroy(); + } + } + if(bestid == null) { + throw new AbortException("No median found that improves the criterion function?!?"); + } + medids.add(bestid); + mindist.destroy(); + mindist = bestd; + } + + mindist.destroy(); + return medids; + } + + /** + * Parameterization class. + * + * @author Erich Schubert + * + * @apiviz.exclude + */ + public static class Parameterizer<V> extends AbstractParameterizer { + @Override + protected PAMInitialMeans<V> makeInstance() { + return new PAMInitialMeans<>(); + } + } +}
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