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diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/distance/DBOutlierDetection.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/distance/DBOutlierDetection.java
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+package de.lmu.ifi.dbs.elki.algorithm.outlier.distance;
+
+/*
+ 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.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.KNNList;
+import de.lmu.ifi.dbs.elki.database.query.DatabaseQuery;
+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.logging.Logging;
+import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
+import de.lmu.ifi.dbs.elki.utilities.Alias;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
+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.parameterization.Parameterization;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;
+
+/**
+ * Simple distanced based outlier detection algorithm. User has to specify two
+ * parameters An object is flagged as an outlier if at least a fraction p of all
+ * data objects has a distance above d from c
+ * 
+ * Reference:
+ * <p>
+ * E.M. Knorr, R. T. Ng:<br />
+ * Algorithms for Mining Distance-Based Outliers in Large Datasets,<br />
+ * In: Procs Int. Conf. on Very Large Databases (VLDB'98), New York, USA, 1998.
+ * </p>
+ * 
+ * This paper presents several Distance Based Outlier Detection algorithms.
+ * Implemented here is a simple index based algorithm as presented in section
+ * 3.1.
+ * 
+ * @author Lisa Reichert
+ * 
+ * @apiviz.has KNNQuery
+ * 
+ * @param <O> the type of DatabaseObjects handled by this Algorithm
+ */
+@Title("DBOD: Distance Based Outlier Detection")
+@Description("If the D-neighborhood of an object contains only very few objects (less than (1-p) percent of the data) this object is flagged as an outlier")
+@Reference(authors = "E.M. Knorr, R. T. Ng", //
+title = "Algorithms for Mining Distance-Based Outliers in Large Datasets", //
+booktitle = "Procs Int. Conf. on Very Large Databases (VLDB'98), New York, USA, 1998")
+@Alias({ "de.lmu.ifi.dbs.elki.algorithm.outlier.DBOutlierDetection" })
+public class DBOutlierDetection<O> extends AbstractDBOutlier<O> {
+  /**
+   * The logger for this class.
+   */
+  private static final Logging LOG = Logging.getLogger(DBOutlierDetection.class);
+
+  /**
+   * Density threshold percentage p.
+   */
+  private double p;
+
+  /**
+   * Constructor with actual parameters.
+   * 
+   * @param distanceFunction distance function parameter
+   * @param d distance query radius
+   * @param p percentage parameter
+   */
+  public DBOutlierDetection(DistanceFunction<O> distanceFunction, double d, double p) {
+    super(distanceFunction, d);
+    this.p = p;
+  }
+
+  @Override
+  protected DoubleDataStore computeOutlierScores(Database database, Relation<O> relation, double neighborhoodSize) {
+    DistanceQuery<O> distFunc = database.getDistanceQuery(relation, getDistanceFunction());
+    KNNQuery<O> knnQuery = database.getKNNQuery(distFunc, DatabaseQuery.HINT_OPTIMIZED_ONLY);
+
+    // maximum number of objects in the D-neighborhood of an outlier
+    int m = (int) ((distFunc.getRelation().size()) * (1 - p));
+
+    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(distFunc.getRelation().getDBIDs(), DataStoreFactory.HINT_STATIC);
+    if(LOG.isVerbose()) {
+      LOG.verbose("computing outlier flag");
+    }
+
+    FiniteProgress progressOFlags = LOG.isVerbose() ? new FiniteProgress("DBOutlier for objects", distFunc.getRelation().size(), LOG) : null;
+    int counter = 0;
+    // if index exists, kNN query. if the distance to the mth nearest neighbor
+    // is more than d -> object is outlier
+    if(knnQuery != null) {
+      for(DBIDIter iditer = distFunc.getRelation().iterDBIDs(); iditer.valid(); iditer.advance()) {
+        counter++;
+        final KNNList knns = knnQuery.getKNNForDBID(iditer, m);
+        if(LOG.isDebugging()) {
+          LOG.debugFine("distance to mth nearest neighbour" + knns.toString());
+        }
+        if(knns.get(Math.min(m, knns.size()) - 1).doubleValue() <= neighborhoodSize) {
+          // flag as outlier
+          scores.putDouble(iditer, 1.0);
+        }
+        else {
+          // flag as no outlier
+          scores.putDouble(iditer, 0.0);
+        }
+      }
+      if(progressOFlags != null) {
+        progressOFlags.setProcessed(counter, LOG);
+      }
+    }
+    else {
+      // range query for each object. stop if m objects are found
+      for(DBIDIter iditer = distFunc.getRelation().iterDBIDs(); iditer.valid(); iditer.advance()) {
+        counter++;
+        int count = 0;
+        for(DBIDIter iterator = distFunc.getRelation().iterDBIDs(); iterator.valid() && count < m; iterator.advance()) {
+          double currentDistance = distFunc.distance(iditer, iterator);
+          if(currentDistance <= neighborhoodSize) {
+            count++;
+          }
+        }
+        scores.putDouble(iditer, (count < m) ? 1.0 : 0);
+      }
+
+      if(progressOFlags != null) {
+        progressOFlags.setProcessed(counter, LOG);
+      }
+    }
+    LOG.ensureCompleted(progressOFlags);
+    return scores;
+  }
+
+  @Override
+  protected Logging getLogger() {
+    return LOG;
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer<O> extends AbstractDBOutlier.Parameterizer<O> {
+    /**
+     * Parameter to specify the minimum fraction of objects that must be outside
+     * the D- neighborhood of an outlier
+     */
+    public static final OptionID P_ID = new OptionID("dbod.p", "minimum fraction of objects that must be outside the D-neighborhood of an outlier");
+
+    /**
+     * Density threshold p.
+     */
+    protected double p = 0.0;
+
+    @Override
+    protected void makeOptions(Parameterization config) {
+      super.makeOptions(config);
+      final DoubleParameter pP = new DoubleParameter(P_ID);
+      if(config.grab(pP)) {
+        p = pP.getValue();
+      }
+    }
+
+    @Override
+    protected DBOutlierDetection<O> makeInstance() {
+      return new DBOutlierDetection<>(distanceFunction, d, p);
+    }
+  }
+}
+\ No newline at end of file