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diff --git a/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/KMeansElkan.java b/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/KMeansElkan.java
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+++ b/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/KMeansElkan.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 java.util.ArrayList;
+import java.util.Arrays;
+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.KMeansModel;
+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.WritableDataStore;
+import de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore;
+import de.lmu.ifi.dbs.elki.database.datastore.WritableIntegerDataStore;
+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.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.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction;
+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.optionhandling.parameterization.Parameterization;
+
+/**
+ * Elkan's fast k-means by exploiting the triangle inequality.
+ *
+ * This variant needs O(n*k) additional memory to store bounds.
+ *
+ * See {@link KMeansHamerly} for a close variant that only uses O(n*2)
+ * additional memory for bounds.
+ *
+ * <p>
+ * Reference:<br />
+ * C. Elkan<br/>
+ * Using the triangle inequality to accelerate k-means<br/>
+ * Proc. 20th International Conference on Machine Learning, ICML 2003
+ * </p>
+ *
+ * @author Erich Schubert
+ *
+ * @apiviz.has KMeansModel
+ *
+ * @param <V> vector datatype
+ */
+@Reference(authors = "C. Elkan", //
+title = "Using the triangle inequality to accelerate k-means", //
+booktitle = "Proc. 20th International Conference on Machine Learning, ICML 2003", //
+url = "http://www.aaai.org/Library/ICML/2003/icml03-022.php")
+public class KMeansElkan<V extends NumberVector> extends AbstractKMeans<V, KMeansModel> {
+  /**
+   * The logger for this class.
+   */
+  private static final Logging LOG = Logging.getLogger(KMeansElkan.class);
+
+  /**
+   * Key for statistics logging.
+   */
+  private static final String KEY = KMeansElkan.class.getName();
+
+  /**
+   * Constructor.
+   *
+   * @param distanceFunction distance function
+   * @param k k parameter
+   * @param maxiter Maxiter parameter
+   * @param initializer Initialization method
+   */
+  public KMeansElkan(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization<? super V> initializer) {
+    super(distanceFunction, k, maxiter, initializer);
+  }
+
+  @Override
+  public Clustering<KMeansModel> run(Database database, Relation<V> relation) {
+    if(relation.size() <= 0) {
+      return new Clustering<>("k-Means Clustering", "kmeans-clustering");
+    }
+    // Choose initial means
+    if(LOG.isStatistics()) {
+      LOG.statistics(new StringStatistic(KEY + ".initializer", initializer.toString()));
+    }
+    List<Vector> means = 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);
+    // Elkan bounds
+    WritableDoubleDataStore upper = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, Double.POSITIVE_INFINITY);
+    WritableDataStore<double[]> lower = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, double[].class);
+    for(DBIDIter it = relation.iterDBIDs(); it.valid(); it.advance()) {
+      lower.put(it, new double[k]); // Filled with 0.
+    }
+    // Storage for updated means:
+    final int dim = means.get(0).getDimensionality();
+    List<Vector> sums = new ArrayList<>(k);
+    for(int i = 0; i < k; i++) {
+      sums.add(new Vector(dim));
+    }
+
+    // Cluster separation
+    double[] sep = new double[k];
+    // Cluster distances
+    double[][] cdist = new double[k][k];
+
+    IndefiniteProgress prog = LOG.isVerbose() ? new IndefiniteProgress("K-Means iteration", LOG) : null;
+    LongStatistic varstat = LOG.isStatistics() ? new LongStatistic(this.getClass().getName() + ".reassignments") : null;
+    int iteration = 0;
+    for(; maxiter <= 0 || iteration < maxiter; iteration++) {
+      LOG.incrementProcessed(prog);
+      int changed;
+      if(iteration == 0) {
+        changed = initialAssignToNearestCluster(relation, means, sums, clusters, assignment, upper, lower);
+      }
+      else {
+        recomputeSeperation(means, sep, cdist); // #1
+        changed = assignToNearestCluster(relation, means, sums, clusters, assignment, sep, cdist, upper, lower);
+      }
+      if(varstat != null) {
+        varstat.setLong(changed);
+        LOG.statistics(varstat);
+      }
+      // Stop if no cluster assignment changed.
+      if(changed == 0) {
+        break;
+      }
+      // Recompute means.
+      for(int i = 0; i < k; i++) {
+        final int s = clusters.get(i).size();
+        sums.get(i).timesEquals(s > 0 ? 1. / s : 1.);
+      }
+      maxMoved(means, sums, sep); // Overwrites sep
+      updateBounds(relation, assignment, upper, lower, sep);
+      for(int i = 0; i < k; i++) {
+        final int s = clusters.get(i).size();
+        means.get(i).set(sums.get(i));
+        // Restore to sum for next iteration
+        sums.get(i).timesEquals(s > 0 ? s : 1.);
+      }
+    }
+    LOG.setCompleted(prog);
+    if(LOG.isStatistics()) {
+      LOG.statistics(new LongStatistic(KEY + ".iterations", iteration));
+    }
+    upper.destroy();
+    lower.destroy();
+
+    // Wrap result
+    Clustering<KMeansModel> result = new Clustering<>("k-Means Clustering", "kmeans-clustering");
+    for(int i = 0; i < clusters.size(); i++) {
+      DBIDs ids = clusters.get(i);
+      if(ids.size() == 0) {
+        continue;
+      }
+      double varsum = 0;
+      Vector mean = means.get(i);
+      for(DBIDIter it = ids.iter(); it.valid(); it.advance()) {
+        varsum += distanceFunction.distance(mean, relation.get(it));
+      }
+      KMeansModel model = new KMeansModel(mean, varsum);
+      result.addToplevelCluster(new Cluster<>(ids, model));
+    }
+    return result;
+  }
+
+  /**
+   * Recompute the separation of cluster means.
+   *
+   * @param means Means
+   * @param sep Output array of separation
+   * @param cdist Center-to-Center distances
+   */
+  private void recomputeSeperation(List<Vector> means, double[] sep, double[][] cdist) {
+    final int k = means.size();
+    assert (sep.length == k);
+    boolean issquared = (distanceFunction instanceof SquaredEuclideanDistanceFunction);
+    Arrays.fill(sep, Double.POSITIVE_INFINITY);
+    for(int i = 1; i < k; i++) {
+      Vector mi = means.get(i);
+      for(int j = 0; j < i; j++) {
+        double d = distanceFunction.distance(mi, means.get(j));
+        d = issquared ? Math.sqrt(d) : d;
+        d *= .5;
+        cdist[i][j] = d;
+        cdist[j][i] = d;
+        sep[i] = (d < sep[i]) ? d : sep[i];
+        sep[j] = (d < sep[j]) ? d : sep[j];
+      }
+    }
+  }
+
+  /**
+   * Reassign objects, but only if their bounds indicate it is necessary to do
+   * so.
+   *
+   * @param relation Data
+   * @param means Current means
+   * @param sums New means
+   * @param clusters Current clusters
+   * @param assignment Cluster assignment
+   * @param upper Upper bounds
+   * @param lower Lower bounds
+   * @return Number of changes (i.e. relation size)
+   */
+  private int initialAssignToNearestCluster(Relation<V> relation, List<Vector> means, List<Vector> sums, List<ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, WritableDoubleDataStore upper, WritableDataStore<double[]> lower) {
+    assert (k == means.size());
+    final NumberVectorDistanceFunction<? super V> df = getDistanceFunction();
+    final boolean issquared = (df instanceof SquaredEuclideanDistanceFunction);
+    for(DBIDIter it = relation.iterDBIDs(); it.valid(); it.advance()) {
+      V fv = relation.get(it);
+      double[] l = lower.get(it);
+      // Check all (other) means:
+      double best = Double.POSITIVE_INFINITY;
+      int cur = -1;
+      for(int j = 0; j < k; j++) {
+        double dist = df.distance(fv, means.get(j));
+        dist = issquared ? Math.sqrt(dist) : dist;
+        l[j] = dist;
+        if(dist < best) {
+          cur = j;
+          best = dist;
+        }
+      }
+      // Assign to nearest cluster.
+      ModifiableDBIDs newc = clusters.get(cur);
+      newc.add(it);
+      assignment.putInt(it, cur);
+      upper.putDouble(it, best);
+      double[] newmean = sums.get(cur).getArrayRef();
+      for(int d = 0; d < fv.getDimensionality(); d++) {
+        newmean[d] += fv.doubleValue(d);
+      }
+    }
+    return relation.size();
+  }
+
+  /**
+   * Reassign objects, but only if their bounds indicate it is necessary to do
+   * so.
+   *
+   * @param relation Data
+   * @param means Current means
+   * @param sums New means
+   * @param clusters Current clusters
+   * @param assignment Cluster assignment
+   * @param sep Separation of means
+   * @param cdist Center-to-center distances
+   * @param upper Upper bounds
+   * @param lower Lower bounds
+   * @return true when the object was reassigned
+   */
+  private int assignToNearestCluster(Relation<V> relation, List<Vector> means, List<Vector> sums, List<ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, double[] sep, double[][] cdist, WritableDoubleDataStore upper, WritableDataStore<double[]> lower) {
+    assert (k == means.size());
+    final NumberVectorDistanceFunction<? super V> df = getDistanceFunction();
+    final boolean issquared = (df instanceof SquaredEuclideanDistanceFunction);
+    int changed = 0;
+    for(DBIDIter it = relation.iterDBIDs(); it.valid(); it.advance()) {
+      final int orig = assignment.intValue(it);
+      double u = upper.doubleValue(it);
+      // Upper bound check (#2):
+      if(u <= sep[orig]) {
+        continue;
+      }
+      boolean recompute_u = true; // Elkan's r(x)
+      V fv = relation.get(it);
+      double[] l = lower.get(it);
+      // Check all (other) means:
+      int cur = orig;
+      for(int j = 0; j < k; j++) {
+        if(orig == j || u <= l[j] || u <= cdist[cur][j]) {
+          continue; // Condition #3 i-iii not satisfied
+        }
+        if(recompute_u) { // Need to update bound? #3a
+          u = df.distance(fv, means.get(cur));
+          u = issquared ? Math.sqrt(u) : u;
+          upper.putDouble(it, u);
+          recompute_u = false; // Once only
+          if(u <= l[j] || u <= cdist[cur][j]) { // #3b
+            continue;
+          }
+        }
+        double dist = df.distance(fv, means.get(j));
+        dist = issquared ? Math.sqrt(dist) : dist;
+        l[j] = dist;
+        if(dist < u) {
+          cur = j;
+          u = dist;
+        }
+      }
+      // Object is to be reassigned.
+      if(cur != orig) {
+        upper.putDouble(it, u); // Remember bound.
+        ModifiableDBIDs newc = clusters.get(cur);
+        newc.add(it);
+        assignment.putInt(it, cur);
+        double[] newmean = sums.get(cur).getArrayRef();
+        ModifiableDBIDs oldc = clusters.get(orig);
+        oldc.remove(it);
+        double[] oldmean = sums.get(orig).getArrayRef();
+        for(int d = 0; d < fv.getDimensionality(); d++) {
+          final double v = fv.doubleValue(d);
+          newmean[d] += v;
+          oldmean[d] -= v;
+        }
+        ++changed;
+      }
+    }
+    return changed;
+  }
+
+  /**
+   * Maximum distance moved.
+   *
+   * @param means Old means
+   * @param newmeans New means
+   * @param dists Distances moved
+   * @return Maximum distance moved
+   */
+  private double maxMoved(List<Vector> means, List<Vector> newmeans, double[] dists) {
+    assert (means.size() == k);
+    assert (newmeans.size() == k);
+    assert (dists.length == k);
+    boolean issquared = (distanceFunction instanceof SquaredEuclideanDistanceFunction);
+    double max = 0.;
+    for(int i = 0; i < k; i++) {
+      double d = distanceFunction.distance(means.get(i), newmeans.get(i));
+      d = issquared ? Math.sqrt(d) : d;
+      dists[i] = d;
+      max = (d > max) ? d : max;
+    }
+    return max;
+  }
+
+  /**
+   * Update the bounds for k-means.
+   *
+   * @param relation Relation
+   * @param assignment Cluster assignment
+   * @param upper Upper bounds
+   * @param lower Lower bounds
+   * @param move Movement of centers
+   */
+  private void updateBounds(Relation<V> relation, WritableIntegerDataStore assignment, WritableDoubleDataStore upper, WritableDataStore<double[]> lower, double[] move) {
+    for(DBIDIter it = relation.iterDBIDs(); it.valid(); it.advance()) {
+      upper.increment(it, move[assignment.intValue(it)]);
+      double[] l = lower.get(it);
+      for(int i = 0; i < k; i++) {
+        l[i] -= move[i];
+      }
+    }
+  }
+
+  @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 void getParameterDistanceFunction(Parameterization config) {
+      super.getParameterDistanceFunction(config);
+      if(distanceFunction instanceof SquaredEuclideanDistanceFunction) {
+        return; // Proper choice.
+      }
+      if(distanceFunction != null && !distanceFunction.isMetric()) {
+        LOG.warning("Elkan k-means requires a metric distance, and k-means should only be used with squared Euclidean distance!");
+      }
+    }
+
+    @Override
+    protected KMeansElkan<V> makeInstance() {
+      return new KMeansElkan<>(distanceFunction, k, maxiter, initializer);
+    }
+  }
+}