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diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/initialization/FarthestSumPointsInitialMeans.java b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/initialization/FarthestSumPointsInitialMeans.java
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+++ b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/initialization/FarthestSumPointsInitialMeans.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.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.DBIDIter;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDRef;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDVar;
+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.random.RandomFactory;
+
+/**
+ * K-Means initialization by repeatedly choosing the farthest point (by the
+ * <em>sum</em> of distances to previous objects).
+ * 
+ * Note: this is less random than other initializations, so running multiple
+ * times will be more likely to return the same local minima.
+ * 
+ * @author Erich Schubert
+ * 
+ * @param <O> Object type for kmedoids and kmedians
+ */
+public class FarthestSumPointsInitialMeans<O> extends FarthestPointsInitialMeans<O> {
+  /**
+   * Constructor.
+   * 
+   * @param rnd Random generator.
+   * @param dropfirst Flag to discard the first vector.
+   */
+  public FarthestSumPointsInitialMeans(RandomFactory rnd, boolean dropfirst) {
+    super(rnd, dropfirst);
+  }
+
+  @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) {
+    // Get a distance query
+    DistanceQuery<T> distQ = database.getDistanceQuery(relation, distanceFunction);
+
+    DBIDs ids = relation.getDBIDs();
+    WritableDoubleDataStore store = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, 0.);
+
+    // Chose first mean
+    List<V> means = new ArrayList<>(k);
+
+    DBIDRef first = DBIDUtil.randomSample(ids, 1, rnd).iter();
+    T prevmean = relation.get(first);
+    means.add(factory.newNumberVector(prevmean));
+
+    // Find farthest object each.
+    DBIDVar best = DBIDUtil.newVar(first);
+    for(int i = (dropfirst ? 0 : 1); i < k; i++) {
+      double maxdist = Double.NEGATIVE_INFINITY;
+      for(DBIDIter it = ids.iter(); it.valid(); it.advance()) {
+        final double prev = store.doubleValue(it);
+        if(prev != prev) {
+          continue; // NaN: already chosen!
+        }
+        double dsum = prev + distQ.distance(prevmean, it);
+        // Don't store distance to first mean, when it will be dropped below.
+        if(i > 0) {
+          store.putDouble(it, dsum);
+        }
+        if(dsum > maxdist) {
+          maxdist = dsum;
+          best.set(it);
+        }
+      }
+      // Add new mean (and drop the initial mean when desired)
+      if(i == 0) {
+        means.clear(); // Remove temporary first element.
+      }
+      store.putDouble(best, Double.NaN); // So it won't be chosen twice.
+      prevmean = relation.get(best);
+      means.add(factory.newNumberVector(prevmean));
+    }
+
+    // Explicitly destroy temporary data.
+    store.destroy();
+    return means;
+  }
+
+  @Override
+  public DBIDs chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distQ) {
+    @SuppressWarnings("unchecked")
+    final Relation<O> relation = (Relation<O>) distQ.getRelation();
+
+    WritableDoubleDataStore store = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, 0.);
+
+    ArrayModifiableDBIDs means = DBIDUtil.newArray(k);
+
+    DBIDRef first = DBIDUtil.randomSample(ids, 1, rnd).iter();
+    means.add(first);
+    O prevmean = relation.get(first);
+
+    DBIDVar best = DBIDUtil.newVar(first);
+    for(int i = (dropfirst ? 0 : 1); i < k; i++) {
+      // Find farthest object:
+      double maxdist = Double.NEGATIVE_INFINITY;
+      for(DBIDIter it = relation.iterDBIDs(); it.valid(); it.advance()) {
+        final double prev = store.doubleValue(it);
+        if(prev != prev) {
+          continue; // NaN: already chosen!
+        }
+        double dsum = prev + distQ.distance(prevmean, it);
+        // Don't store distance to first mean, when it will be dropped below.
+        if(i > 0) {
+          store.putDouble(it, dsum);
+        }
+        if(dsum > maxdist) {
+          maxdist = dsum;
+          best.set(it);
+        }
+      }
+      // Add new mean:
+      if(k == 0) {
+        means.clear(); // Remove temporary first element.
+      }
+      store.putDouble(best, Double.NaN); // So it won't be chosen twice.
+      prevmean = relation.get(best);
+      means.add(best);
+    }
+
+    return means;
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer<V> extends FarthestPointsInitialMeans.Parameterizer<V> {
+    /**
+     * Flag for discarding the first object chosen.
+     */
+    protected boolean keepfirst = false;
+
+    @Override
+    protected FarthestSumPointsInitialMeans<V> makeInstance() {
+      return new FarthestSumPointsInitialMeans<>(rnd, !keepfirst);
+    }
+  }
+}