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diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/distance/KNNOutlier.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/distance/KNNOutlier.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.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.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.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.math.DoubleMinMax;
+import de.lmu.ifi.dbs.elki.result.outlier.BasicOutlierScoreMeta;
+import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
+import de.lmu.ifi.dbs.elki.result.outlier.OutlierScoreMeta;
+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.constraints.CommonConstraints;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
+
+/**
+ * Outlier Detection based on the distance of an object to its k nearest
+ * neighbor.
+ * 
+ * This implementation differs from the original pseudocode: the k nearest
+ * neighbors do not exclude the point that is currently evaluated. I.e. for k=1
+ * the resulting score is the distance to the 1-nearest neighbor that is not the
+ * query point and therefore should match k=2 in the exact pseudocode - a value
+ * of k=1 in the original code does not make sense, as the 1NN distance will be
+ * 0 for every point in the database. If you for any reason want to use the
+ * original algorithm, subtract 1 from the k parameter.
+ * 
+ * Reference:
+ * <p>
+ * S. Ramaswamy, R. Rastogi, K. Shim:<br />
+ * Efficient Algorithms for Mining Outliers from Large Data Sets.<br />
+ * In: Proc. of the Int. Conf. on Management of Data, Dallas, Texas, 2000.
+ * </p>
+ * 
+ * @author Lisa Reichert
+ * 
+ * @apiviz.has KNNQuery
+ * 
+ * @param <O> the type of DatabaseObjects handled by this Algorithm
+ */
+@Title("KNN outlier: Efficient Algorithms for Mining Outliers from Large Data Sets")
+@Description("Outlier Detection based on the distance of an object to its k nearest neighbor.")
+@Reference(authors = "S. Ramaswamy, R. Rastogi, K. Shim", //
+title = "Efficient Algorithms for Mining Outliers from Large Data Sets", //
+booktitle = "Proc. of the Int. Conf. on Management of Data, Dallas, Texas, 2000", //
+url = "http://dx.doi.org/10.1145/342009.335437")
+@Alias({ "de.lmu.ifi.dbs.elki.algorithm.outlier.KNNOutlier", "knno" })
+public class KNNOutlier<O> extends AbstractDistanceBasedAlgorithm<O, OutlierResult> implements OutlierAlgorithm {
+  /**
+   * The logger for this class.
+   */
+  private static final Logging LOG = Logging.getLogger(KNNOutlier.class);
+
+  /**
+   * The parameter k (including query point!)
+   */
+  private int k;
+
+  /**
+   * Constructor for a single kNN query.
+   * 
+   * @param distanceFunction distance function to use
+   * @param k Value of k (including query point!)
+   */
+  public KNNOutlier(DistanceFunction<? super O> distanceFunction, int k) {
+    super(distanceFunction);
+    this.k = k;
+  }
+
+  /**
+   * Runs the algorithm in the timed evaluation part.
+   */
+  public OutlierResult run(Database database, Relation<O> relation) {
+    final DistanceQuery<O> distanceQuery = database.getDistanceQuery(relation, getDistanceFunction());
+    final KNNQuery<O> knnQuery = database.getKNNQuery(distanceQuery, k + 1);
+
+    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("kNN distance for objects", relation.size(), LOG) : null;
+
+    DoubleMinMax minmax = new DoubleMinMax();
+    WritableDoubleDataStore knno_score = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);
+    // compute distance to the k nearest neighbor.
+    for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+      // distance to the kth nearest neighbor
+      // (assuming the query point is always included, with distance 0)
+      final KNNList knns = knnQuery.getKNNForDBID(iditer, k + 1);
+      final double dkn = knns.getKNNDistance();
+
+      knno_score.putDouble(iditer, dkn);
+      minmax.put(dkn);
+
+      LOG.incrementProcessed(prog);
+    }
+    LOG.ensureCompleted(prog);
+    DoubleRelation scoreres = new MaterializedDoubleRelation("kNN Outlier Score", "knn-outlier", knno_score, relation.getDBIDs());
+    OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 0.0);
+    return new OutlierResult(meta, scoreres);
+  }
+
+  @Override
+  public TypeInformation[] getInputTypeRestriction() {
+    return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
+  }
+
+  @Override
+  protected Logging getLogger() {
+    return LOG;
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer<O> extends AbstractDistanceBasedAlgorithm.Parameterizer<O> {
+    /**
+     * Parameter to specify the k nearest neighbor
+     */
+    public static final OptionID K_ID = new OptionID("knno.k", //
+    "The k nearest neighbor, excluding the query point "//
+        + "(i.e. query point is the 0-nearest-neighbor)");
+
+    /**
+     * k parameter
+     */
+    protected int k = 0;
+
+    @Override
+    protected void makeOptions(Parameterization config) {
+      super.makeOptions(config);
+      final IntParameter kP = new IntParameter(K_ID)//
+      .addConstraint(CommonConstraints.GREATER_EQUAL_ONE_INT);
+      if(config.grab(kP)) {
+        k = kP.getValue();
+      }
+    }
+
+    @Override
+    protected KNNOutlier<O> makeInstance() {
+      return new KNNOutlier<>(distanceFunction, k);
+    }
+  }
+}
+\ No newline at end of file