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diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/COF.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/COF.java
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+package de.lmu.ifi.dbs.elki.algorithm.outlier.lof;
+
+/*
+ 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 de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm;
+import de.lmu.ifi.dbs.elki.algorithm.outlier.OutlierAlgorithm;
+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.datastore.DataStoreFactory;
+import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
+import de.lmu.ifi.dbs.elki.database.datastore.DoubleDataStore;
+import de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore;
+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.DoubleDBIDListIter;
+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.DoubleRelation;
+import de.lmu.ifi.dbs.elki.database.relation.MaterializedDoubleRelation;
+import de.lmu.ifi.dbs.elki.database.relation.Relation;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
+import de.lmu.ifi.dbs.elki.logging.Logging;
+import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
+import de.lmu.ifi.dbs.elki.logging.progress.StepProgress;
+import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
+import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
+import de.lmu.ifi.dbs.elki.result.outlier.OutlierScoreMeta;
+import de.lmu.ifi.dbs.elki.result.outlier.QuotientOutlierScoreMeta;
+import de.lmu.ifi.dbs.elki.utilities.DatabaseUtil;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
+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;
+
+/**
+ * Connectivity-based outlier factor (COF).
+ * 
+ * Reference:
+ * <p>
+ * J. Tang, Z. Chen, A. W. C. Fu, D. W. Cheung<br />
+ * Enhancing effectiveness of outlier detections for low density patterns.<br />
+ * In Advances in Knowledge Discovery and Data Mining.
+ * </p>
+ * 
+ * @author Erich Schubert
+ *
+ * @param <O> Object type
+ */
+@Reference(authors = "J. Tang, Z. Chen, A. W. C. Fu, D. W. Cheung", //
+title = "Enhancing effectiveness of outlier detections for low density patterns", //
+booktitle = "In Advances in Knowledge Discovery and Data Mining", //
+url = "http://dx.doi.org/10.1007/3-540-47887-6_53")
+public class COF<O> extends AbstractDistanceBasedAlgorithm<O, OutlierResult> implements OutlierAlgorithm {
+  /**
+   * The logger for this class.
+   */
+  private static final Logging LOG = Logging.getLogger(COF.class);
+
+  /**
+   * The number of neighbors to query (including the query point!)
+   */
+  protected int k;
+
+  /**
+   * Constructor.
+   * 
+   * @param k the number of neighbors to use for comparison (excluding the query
+   *        point)
+   * @param distanceFunction the neighborhood distance function
+   */
+  public COF(int k, DistanceFunction<? super O> distanceFunction) {
+    super(distanceFunction);
+    this.k = k + 1;
+  }
+
+  /**
+   * Runs the COF algorithm on the given database.
+   * 
+   * @param database Database to query
+   * @param relation Data to process
+   * @return COF outlier result
+   */
+  public OutlierResult run(Database database, Relation<O> relation) {
+    StepProgress stepprog = LOG.isVerbose() ? new StepProgress("COF", 3) : null;
+    DistanceQuery<O> dq = database.getDistanceQuery(relation, getDistanceFunction());
+    LOG.beginStep(stepprog, 1, "Materializing COF neighborhoods.");
+    KNNQuery<O> knnq = DatabaseUtil.precomputedKNNQuery(database, relation, dq, k);
+    DBIDs ids = relation.getDBIDs();
+
+    LOG.beginStep(stepprog, 2, "Computing Average Chaining Distances.");
+    WritableDoubleDataStore acds = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
+    computeAverageChainingDistances(knnq, dq, ids, acds);
+
+    // compute COF_SCORE of each db object
+    LOG.beginStep(stepprog, 3, "Computing Connectivity-based Outlier Factors.");
+    WritableDoubleDataStore cofs = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_DB);
+    // track the maximum value for normalization.
+    DoubleMinMax cofminmax = new DoubleMinMax();
+    computeCOFScores(knnq, ids, acds, cofs, cofminmax);
+
+    LOG.setCompleted(stepprog);
+
+    // Build result representation.
+    DoubleRelation scoreResult = new MaterializedDoubleRelation("Connectivity-Based Outlier Factor", "cof-outlier", cofs, ids);
+    OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(cofminmax.getMin(), cofminmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 1.0);
+    return new OutlierResult(scoreMeta, scoreResult);
+  }
+
+  /**
+   * Computes the average chaining distance, the average length of a path
+   * through the given set of points to each target. The authors of COF decided
+   * to approximate this value using a weighted mean that assumes every object
+   * is reached from the previous point (but actually every point could be best
+   * reachable from the first, in which case this does not make much sense.)
+   * 
+   * TODO: can we accelerate this by using the kNN of the neighbors?
+   * 
+   * @param knnq KNN query
+   * @param dq Distance query
+   * @param ids IDs to process
+   * @param acds Storage for average chaining distances
+   */
+  protected void computeAverageChainingDistances(KNNQuery<O> knnq, DistanceQuery<O> dq, DBIDs ids, WritableDoubleDataStore acds) {
+    FiniteProgress lrdsProgress = LOG.isVerbose() ? new FiniteProgress("Computing average chaining distances", ids.size(), LOG) : null;
+
+    // Compute the chaining distances.
+    // We do <i>not</i> bother to materialize the chaining order.
+    for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
+      final KNNList neighbors = knnq.getKNNForDBID(iter, k);
+      final int r = neighbors.size();
+      DoubleDBIDListIter it1 = neighbors.iter(), it2 = neighbors.iter();
+      // Store the current lowest reachability.
+      final double[] mindists = new double[r];
+      for(int i = 0; it1.valid(); it1.advance(), ++i) {
+        mindists[i] = DBIDUtil.equal(it1, iter) ? Double.NaN : it1.doubleValue();
+      }
+
+      double acsum = 0.;
+      for(int j = ((r < k) ? r : k) - 1; j > 0; --j) {
+        // Find the minimum:
+        int minpos = -1;
+        double mindist = Double.NaN;
+        for(int i = 0; i < mindists.length; ++i) {
+          double curdist = mindists[i];
+          // Both values could be NaN, deliberately.
+          if(curdist == curdist && !(curdist > mindist)) {
+            minpos = i;
+            mindist = curdist;
+          }
+        }
+        acsum += mindist * j; // Weighted sum, decreasing weights
+        mindists[minpos] = Double.NaN;
+        it1.seek(minpos);
+        // Update distances
+        it2.seek(0);
+        for(int i = 0; it2.valid(); it2.advance(), ++i) {
+          final double curdist = mindists[i];
+          if(curdist != curdist) {
+            continue; // NaN = processed!
+          }
+          double newdist = dq.distance(it1, it2);
+          if(newdist < curdist) {
+            mindists[i] = newdist;
+          }
+        }
+      }
+      acds.putDouble(iter, acsum / (r * 0.5 * (r - 1.)));
+      LOG.incrementProcessed(lrdsProgress);
+    }
+    LOG.ensureCompleted(lrdsProgress);
+  }
+
+  /**
+   * Compute Connectivity outlier factors.
+   * 
+   * @param knnq KNN query
+   * @param ids IDs to process
+   * @param acds Average chaining distances
+   * @param cofs Connectivity outlier factor storage
+   * @param cofminmax Score minimum/maximum tracker
+   */
+  private void computeCOFScores(KNNQuery<O> knnq, DBIDs ids, DoubleDataStore acds, WritableDoubleDataStore cofs, DoubleMinMax cofminmax) {
+    FiniteProgress progressCOFs = LOG.isVerbose() ? new FiniteProgress("COF for objects", ids.size(), LOG) : null;
+    for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
+      final KNNList neighbors = knnq.getKNNForDBID(iter, k);
+      // Aggregate the average chaining distances of all neighbors:
+      double sum = 0.;
+      for(DBIDIter neighbor = neighbors.iter(); neighbor.valid(); neighbor.advance()) {
+        // skip the point itself
+        if(DBIDUtil.equal(neighbor, iter)) {
+          continue;
+        }
+        sum += acds.doubleValue(neighbor);
+      }
+      final double cof = (sum > 0.) ? (acds.doubleValue(iter) * k / sum) : (acds.doubleValue(iter) > 0. ? Double.POSITIVE_INFINITY : 1.);
+      cofs.putDouble(iter, cof);
+      // update minimum and maximum
+      cofminmax.put(cof);
+
+      LOG.incrementProcessed(progressCOFs);
+    }
+    LOG.ensureCompleted(progressCOFs);
+  }
+
+  @Override
+  public TypeInformation[] getInputTypeRestriction() {
+    return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
+  }
+
+  @Override
+  protected Logging getLogger() {
+    return LOG;
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   * 
+   * @param <O> Object type
+   */
+  public static class Parameterizer<O> extends AbstractDistanceBasedAlgorithm.Parameterizer<O> {
+    /**
+     * Parameter to specify the neighborhood size for COF. This does not include
+     * the query object.
+     */
+    public static final OptionID K_ID = new OptionID("cof.k", "The number of neighbors (not including the query object) to use for computing the COF score.");
+
+    /**
+     * The neighborhood size to use.
+     */
+    protected int k = 2;
+
+    @Override
+    protected void makeOptions(Parameterization config) {
+      super.makeOptions(config);
+
+      final IntParameter pK = new IntParameter(K_ID) //
+      .addConstraint(CommonConstraints.GREATER_EQUAL_ONE_INT);
+      if(config.grab(pK)) {
+        k = pK.intValue();
+      }
+    }
+
+    @Override
+    protected COF<O> makeInstance() {
+      return new COF<>(k, distanceFunction);
+    }
+  }
+}