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diff --git a/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/statistics/EvaluateRankingQuality.java b/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/statistics/EvaluateRankingQuality.java
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+package de.lmu.ifi.dbs.elki.algorithm.statistics;
+
+/*
+ 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.Collection;
+import java.util.Collections;
+import java.util.HashMap;
+
+import de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm;
+import de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.ByLabelOrAllInOneClustering;
+import de.lmu.ifi.dbs.elki.data.Cluster;
+import de.lmu.ifi.dbs.elki.data.DoubleVector;
+import de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.data.model.Model;
+import de.lmu.ifi.dbs.elki.data.type.CombinedTypeInformation;
+import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
+import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
+import de.lmu.ifi.dbs.elki.database.Database;
+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.DoubleDBIDPair;
+import de.lmu.ifi.dbs.elki.database.ids.KNNList;
+import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
+import de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery;
+import de.lmu.ifi.dbs.elki.database.relation.Relation;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
+import de.lmu.ifi.dbs.elki.evaluation.scores.ROCEvaluation;
+import de.lmu.ifi.dbs.elki.logging.Logging;
+import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
+import de.lmu.ifi.dbs.elki.math.MathUtil;
+import de.lmu.ifi.dbs.elki.math.MeanVariance;
+import de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix;
+import de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix;
+import de.lmu.ifi.dbs.elki.math.linearalgebra.Vector;
+import de.lmu.ifi.dbs.elki.result.CollectionResult;
+import de.lmu.ifi.dbs.elki.result.HistogramResult;
+import de.lmu.ifi.dbs.elki.utilities.datastructures.histogram.MeanVarianceStaticHistogram;
+import de.lmu.ifi.dbs.elki.utilities.datastructures.histogram.ObjHistogram;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
+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;
+
+/**
+ * Evaluate a distance function with respect to kNN queries. For each point, the
+ * neighbors are sorted by distance, then the ROC AUC is computed. A score of 1
+ * means that the distance function provides a perfect ordering of relevant
+ * neighbors first, then irrelevant neighbors. A value of 0.5 can be obtained by
+ * random sorting. A value of 0 means the distance function is inverted, i.e. a
+ * similarity.
+ *
+ * In contrast to {@link RankingQualityHistogram}, this method uses a binning
+ * based on the centrality of objects. This allows analyzing whether or not a
+ * particular distance degrades for the outer parts of a cluster.
+ *
+ * TODO: Allow fixed binning range, configurable
+ *
+ * TODO: Add sampling
+ *
+ * @author Erich Schubert
+ * @param <V> Vector type
+ */
+@Title("Evaluate Ranking Quality")
+@Description("Evaluates the effectiveness of a distance function via the obtained rankings.")
+public class EvaluateRankingQuality<V extends NumberVector> extends AbstractDistanceBasedAlgorithm<V, CollectionResult<DoubleVector>> {
+  /**
+   * The logger for this class.
+   */
+  private static final Logging LOG = Logging.getLogger(EvaluateRankingQuality.class);
+
+  /**
+   * Number of bins to use.
+   */
+  int numbins = 20;
+
+  /**
+   * Constructor.
+   *
+   * @param distanceFunction Distance function
+   * @param numbins Number of bins
+   */
+  public EvaluateRankingQuality(DistanceFunction<? super V> distanceFunction, int numbins) {
+    super(distanceFunction);
+    this.numbins = numbins;
+  }
+
+  @Override
+  public HistogramResult<DoubleVector> run(Database database) {
+    final Relation<V> relation = database.getRelation(getInputTypeRestriction()[0]);
+    final DistanceQuery<V> distQuery = database.getDistanceQuery(relation, getDistanceFunction());
+    final KNNQuery<V> knnQuery = database.getKNNQuery(distQuery, relation.size());
+
+    if(LOG.isVerbose()) {
+      LOG.verbose("Preprocessing clusters...");
+    }
+    // Cluster by labels
+    Collection<Cluster<Model>> split = (new ByLabelOrAllInOneClustering()).run(database).getAllClusters();
+
+    // Compute cluster averages and covariance matrix
+    HashMap<Cluster<?>, Vector> averages = new HashMap<>(split.size());
+    HashMap<Cluster<?>, Matrix> covmats = new HashMap<>(split.size());
+    for(Cluster<?> clus : split) {
+      CovarianceMatrix covmat = CovarianceMatrix.make(relation, clus.getIDs());
+      averages.put(clus, covmat.getMeanVector());
+      covmats.put(clus, covmat.destroyToNaiveMatrix());
+    }
+
+    MeanVarianceStaticHistogram hist = new MeanVarianceStaticHistogram(numbins, 0.0, 1.0);
+
+    if(LOG.isVerbose()) {
+      LOG.verbose("Processing points...");
+    }
+    FiniteProgress rocloop = LOG.isVerbose() ? new FiniteProgress("Computing ROC AUC values", relation.size(), LOG) : null;
+
+    // sort neighbors
+    for(Cluster<?> clus : split) {
+      ArrayList<DoubleDBIDPair> cmem = new ArrayList<>(clus.size());
+      Vector av = averages.get(clus);
+      Matrix covm = covmats.get(clus);
+
+      for(DBIDIter iter = clus.getIDs().iter(); iter.valid(); iter.advance()) {
+        double d = MathUtil.mahalanobisDistance(covm, relation.get(iter).getColumnVector().minusEquals(av));
+        cmem.add(DBIDUtil.newPair(d, iter));
+      }
+      Collections.sort(cmem);
+
+      for(int ind = 0; ind < cmem.size(); ind++) {
+        KNNList knn = knnQuery.getKNNForDBID(cmem.get(ind), relation.size());
+        double result = new ROCEvaluation().evaluate(clus, knn);
+
+        hist.put(((double) ind) / clus.size(), result);
+
+        LOG.incrementProcessed(rocloop);
+      }
+    }
+    LOG.ensureCompleted(rocloop);
+    // Collections.sort(results);
+
+    // Transform Histogram into a Double Vector array.
+    Collection<DoubleVector> res = new ArrayList<>(relation.size());
+    for(ObjHistogram.Iter<MeanVariance> iter = hist.iter(); iter.valid(); iter.advance()) {
+      DoubleVector row = new DoubleVector(new double[] { iter.getCenter(), iter.getValue().getCount(), iter.getValue().getMean(), iter.getValue().getSampleVariance() });
+      res.add(row);
+    }
+    return new HistogramResult<>("Ranking Quality Histogram", "ranking-histogram", res);
+  }
+
+  @Override
+  public TypeInformation[] getInputTypeRestriction() {
+    return TypeUtil.array(new CombinedTypeInformation(getDistanceFunction().getInputTypeRestriction(), TypeUtil.NUMBER_VECTOR_FIELD));
+  }
+
+  @Override
+  protected Logging getLogger() {
+    return LOG;
+  }
+
+  /**
+   * Parameterization class.
+   *
+   * @author Erich Schubert
+   *
+   * @apiviz.exclude
+   *
+   * @param <V> Vector type
+   */
+  public static class Parameterizer<V extends NumberVector> extends AbstractDistanceBasedAlgorithm.Parameterizer<V> {
+    /**
+     * Option to configure the number of bins to use.
+     */
+    public static final OptionID HISTOGRAM_BINS_ID = new OptionID("rankqual.bins", "Number of bins to use in the histogram");
+    /**
+     * Number of bins to use.
+     */
+    protected int numbins = 20;
+
+    @Override
+    protected void makeOptions(Parameterization config) {
+      super.makeOptions(config);
+      final IntParameter param = new IntParameter(HISTOGRAM_BINS_ID, 20) //
+      .addConstraint(CommonConstraints.GREATER_THAN_ONE_INT);
+      if(config.grab(param)) {
+        numbins = param.getValue();
+      }
+    }
+
+    @Override
+    protected EvaluateRankingQuality<V> makeInstance() {
+      return new EvaluateRankingQuality<>(distanceFunction, numbins);
+    }
+  }
+}