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diff --git a/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/BestOfMultipleKMeans.java b/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/BestOfMultipleKMeans.java
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+++ b/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/BestOfMultipleKMeans.java
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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 de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm;
+import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.KMeansQualityMeasure;
+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.data.type.TypeInformation;
+import de.lmu.ifi.dbs.elki.database.Database;
+import de.lmu.ifi.dbs.elki.database.relation.Relation;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.NumberVectorDistanceFunction;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction;
+import de.lmu.ifi.dbs.elki.logging.Logging;
+import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
+import de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.CommonConstraints;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.ObjectParameter;
+
+/**
+ * Run K-Means multiple times, and keep the best run.
+ *
+ * @author Stephan Baier
+ * @author Erich Schubert
+ *
+ * @param <V> Vector type
+ * @param <M> Model type
+ */
+public class BestOfMultipleKMeans<V extends NumberVector, M extends MeanModel> extends AbstractAlgorithm<Clustering<M>>implements KMeans<V, M> {
+  /**
+   * The logger for this class.
+   */
+  private static final Logging LOG = Logging.getLogger(BestOfMultipleKMeans.class);
+
+  /**
+   * Number of trials to do.
+   */
+  private int trials;
+
+  /**
+   * Variant of kMeans for the bisecting step.
+   */
+  private KMeans<V, M> innerkMeans;
+
+  /**
+   * Quality measure which should be used.
+   */
+  private KMeansQualityMeasure<? super V> qualityMeasure;
+
+  /**
+   * Constructor.
+   *
+   * @param trials Number of trials to do.
+   * @param innerkMeans K-Means variant to actually use.
+   * @param qualityMeasure Quality measure
+   */
+  public BestOfMultipleKMeans(int trials, KMeans<V, M> innerkMeans, KMeansQualityMeasure<? super V> qualityMeasure) {
+    super();
+    this.trials = trials;
+    this.innerkMeans = innerkMeans;
+    this.qualityMeasure = qualityMeasure;
+  }
+
+  @Override
+  public Clustering<M> run(Database database, Relation<V> relation) {
+    if(!(innerkMeans.getDistanceFunction() instanceof PrimitiveDistanceFunction)) {
+      throw new AbortException("K-Means results can only be evaluated for primitive distance functions, got: " + innerkMeans.getDistanceFunction().getClass());
+    }
+    @SuppressWarnings("unchecked")
+    final NumberVectorDistanceFunction<? super NumberVector> df = (NumberVectorDistanceFunction<? super NumberVector>) innerkMeans.getDistanceFunction();
+
+    Clustering<M> bestResult = null;
+    double bestCost = Double.NaN;
+    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("K-means iterations", trials, LOG) : null;
+    for(int i = 0; i < trials; i++) {
+      Clustering<M> currentCandidate = innerkMeans.run(database, relation);
+      double currentCost = qualityMeasure.quality(currentCandidate, df, relation);
+
+      if(LOG.isVerbose()) {
+        LOG.verbose("Cost of candidate " + i + ": " + currentCost);
+      }
+
+      if(qualityMeasure.isBetter(currentCost, bestCost)) {
+        bestResult = currentCandidate;
+        bestCost = currentCost;
+      }
+      LOG.incrementProcessed(prog);
+    }
+    LOG.ensureCompleted(prog);
+
+    return bestResult;
+  }
+
+  @Override
+  public TypeInformation[] getInputTypeRestriction() {
+    return innerkMeans.getInputTypeRestriction();
+  }
+
+  @Override
+  public DistanceFunction<? super V> getDistanceFunction() {
+    return innerkMeans.getDistanceFunction();
+  }
+
+  @Override
+  public void setK(int k) {
+    innerkMeans.setK(k);
+  }
+
+  @Override
+  public void setDistanceFunction(NumberVectorDistanceFunction<? super V> distanceFunction) {
+    innerkMeans.setDistanceFunction(distanceFunction);
+  }
+
+  @Override
+  protected Logging getLogger() {
+    return LOG;
+  }
+
+  /**
+   * Parameterization class.
+   *
+   * @author Stephan Baier
+   * @author Erich Schubert
+   *
+   * @apiviz.exclude
+   *
+   * @param <V> Vector type
+   * @param <M> Model type
+   */
+  public static class Parameterizer<V extends NumberVector, M extends MeanModel> extends AbstractParameterizer {
+    /**
+     * Parameter to specify the iterations of the bisecting step.
+     */
+    public static final OptionID TRIALS_ID = new OptionID("kmeans.trials", "The number of trials to run.");
+
+    /**
+     * Parameter to specify the kMeans variant.
+     */
+    public static final OptionID KMEANS_ID = new OptionID("kmeans.algorithm", "KMeans variant to run multiple times.");
+
+    /**
+     * Parameter to specify the variant of quality measure.
+     */
+    public static final OptionID QUALITYMEASURE_ID = new OptionID("kmeans.qualitymeasure", "Quality measure variant for deciding which run to keep.");
+
+    /**
+     * Number of trials to perform.
+     */
+    protected int trials;
+
+    /**
+     * Variant of kMeans to use.
+     */
+    protected KMeans<V, M> kMeansVariant;
+
+    /**
+     * Quality measure.
+     */
+    protected KMeansQualityMeasure<? super V> qualityMeasure;
+
+    @Override
+    protected void makeOptions(Parameterization config) {
+      IntParameter trialsP = new IntParameter(TRIALS_ID);
+      trialsP.addConstraint(CommonConstraints.GREATER_EQUAL_ONE_INT);
+      if(config.grab(trialsP)) {
+        trials = trialsP.intValue();
+      }
+
+      ObjectParameter<KMeans<V, M>> kMeansVariantP = new ObjectParameter<>(KMEANS_ID, KMeans.class);
+      if(config.grab(kMeansVariantP)) {
+        kMeansVariant = kMeansVariantP.instantiateClass(config);
+      }
+
+      ObjectParameter<KMeansQualityMeasure<V>> qualityMeasureP = new ObjectParameter<>(QUALITYMEASURE_ID, KMeansQualityMeasure.class);
+      if(config.grab(qualityMeasureP)) {
+        qualityMeasure = qualityMeasureP.instantiateClass(config);
+      }
+    }
+
+    @Override
+    protected BestOfMultipleKMeans<V, M> makeInstance() {
+      return new BestOfMultipleKMeans<>(trials, kMeansVariant, qualityMeasure);
+    }
+  }
+}