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diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/initialization/PredefinedInitialMeans.java b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/initialization/PredefinedInitialMeans.java
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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) 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.Cluster;
+import de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.data.NumberVector.Factory;
+import de.lmu.ifi.dbs.elki.data.model.MeanModel;
+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.PrimitiveDistanceFunction;
+import de.lmu.ifi.dbs.elki.math.linearalgebra.Vector;
+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.parameterization.Parameterization;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.VectorListParameter;
+
+/**
+ * Run k-means with prespecified initial means.
+ * 
+ * @author Erich Schubert
+ */
+public class PredefinedInitialMeans extends AbstractKMeansInitialization<NumberVector> {
+  /**
+   * Initial means to return.
+   */
+  List<? extends NumberVector> initialMeans;
+
+  /**
+   * Constructor.
+   *
+   * @param initialMeans Initial means
+   */
+  public PredefinedInitialMeans(List<? extends NumberVector> initialMeans) {
+    super(null);
+    this.setInitialMeans(initialMeans);
+  }
+
+  /**
+   * Constructor.
+   *
+   * @param initialMeans Initial means
+   */
+  public PredefinedInitialMeans(double[][] initialMeans) {
+    super(null);
+    this.setInitialMeans(initialMeans);
+  }
+
+  /**
+   * Set the initial means.
+   * 
+   * Important notice: Use with care - the means are <em>not copied</em>!
+   * 
+   * @param initialMeans initial means.
+   */
+  public void setInitialMeans(List<? extends NumberVector> initialMeans) {
+    this.initialMeans = initialMeans;
+  }
+
+  /**
+   * Set the initial means.
+   * 
+   * Important notice: Use with care - the means are <em>not copied</em>!
+   * 
+   * @param initialMeans initial means.
+   */
+  public void setInitialClusters(List<? extends Cluster<? extends MeanModel>> initialMeans) {
+    List<Vector> vecs = new ArrayList<>(initialMeans.size());
+    for(Cluster<? extends MeanModel> cluster : initialMeans) {
+      vecs.add(cluster.getModel().getMean().copy());
+    }
+    this.initialMeans = vecs;
+  }
+
+  /**
+   * Set the initial means.
+   * 
+   * Important notice: Use with care - the means are <em>not copied</em>!
+   * 
+   * @param initialMeans initial means.
+   */
+  public void setInitialMeans(double[][] initialMeans) {
+    List<Vector> vecs = new ArrayList<>(initialMeans.length);
+    for(int i = 0; i < initialMeans.length; ++i) {
+      vecs.add(new Vector(initialMeans[i]));
+    }
+    this.initialMeans = vecs;
+  }
+
+  @Override
+  public <T extends NumberVector, O extends NumberVector> List<O> chooseInitialMeans(Database database, Relation<T> relation, int k, PrimitiveDistanceFunction<? super T> distanceFunction, Factory<O> factory) {
+    if(k != initialMeans.size()) {
+      throw new AbortException("Predefined initial means contained " + initialMeans.size() + " means, algorithm requested " + k + " means instead.");
+    }
+    // Chose first mean
+    List<O> means = new ArrayList<>(k);
+
+    for(NumberVector v : initialMeans) {
+      means.add(factory.newNumberVector(v));
+    }
+    return means;
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   *
+   * @apiviz.exclude
+   */
+  public static class Parameterizer extends AbstractParameterizer {
+    /**
+     * Option to specify the initial means to use.
+     */
+    public static final OptionID INITIAL_MEANS = new OptionID("kmeans.means", "Initial means for k-means.");
+
+    /**
+     * Initial means.
+     */
+    protected List<Vector> initialMeans;
+
+    @Override
+    protected void makeOptions(Parameterization config) {
+      super.makeOptions(config);
+      VectorListParameter meansP = new VectorListParameter(INITIAL_MEANS);
+      if(config.grab(meansP)) {
+        initialMeans = meansP.getValue();
+      }
+    }
+
+    @Override
+    protected PredefinedInitialMeans makeInstance() {
+      return new PredefinedInitialMeans(initialMeans);
+    }
+  }
+}