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diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/distance/parallel/KNNWeightProcessor.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/distance/parallel/KNNWeightProcessor.java
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+package de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel;
+
+/*
+ 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.ids.DBIDRef;
+import de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter;
+import de.lmu.ifi.dbs.elki.database.ids.KNNList;
+import de.lmu.ifi.dbs.elki.parallel.Executor;
+import de.lmu.ifi.dbs.elki.parallel.processor.AbstractDoubleProcessor;
+import de.lmu.ifi.dbs.elki.parallel.processor.KNNProcessor;
+import de.lmu.ifi.dbs.elki.parallel.variables.SharedDouble;
+import de.lmu.ifi.dbs.elki.parallel.variables.SharedObject;
+
+/**
+ * Compute the kNN weight score, used by {@link ParallelKNNWeightOutlier}.
+ * 
+ * Needs the k nearest neighbors as input, for example from {@link KNNProcessor}
+ * 
+ * @author Erich Schubert
+ */
+public class KNNWeightProcessor extends AbstractDoubleProcessor {
+  /**
+   * K parameter
+   */
+  int k;
+
+  /**
+   * Constructor.
+   * 
+   * @param k K parameter
+   */
+  public KNNWeightProcessor(int k) {
+    super();
+    this.k = k;
+  }
+
+  /**
+   * KNN query object
+   */
+  SharedObject<? extends KNNList> input;
+
+  /**
+   * Connect the input channel.
+   * 
+   * @param input Input channel
+   */
+  public void connectKNNInput(SharedObject<? extends KNNList> input) {
+    this.input = input;
+  }
+
+  @Override
+  public Instance instantiate(Executor executor) {
+    return new Instance(k, executor.getInstance(input), executor.getInstance(output));
+  }
+
+  /**
+   * Instance for precomputing the kNN.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  private static class Instance extends AbstractDoubleProcessor.Instance {
+    /**
+     * k Parameter
+     */
+    int k;
+
+    /**
+     * kNN query
+     */
+    SharedObject.Instance<? extends KNNList> input;
+
+    /**
+     * Constructor.
+     * 
+     * @param k K parameter
+     * @param input kNN list input
+     * @param store Datastore to write to
+     */
+    protected Instance(int k, SharedObject.Instance<? extends KNNList> input, SharedDouble.Instance store) {
+      super(store);
+      this.k = k;
+      this.input = input;
+    }
+
+    @Override
+    public void map(DBIDRef id) {
+      final KNNList list = input.get();
+      int i = 0;
+      double sum = 0;
+      for(DoubleDBIDListIter iter = list.iter(); iter.valid() && i < k; iter.advance(), ++i) {
+        sum += iter.doubleValue();
+      }
+      output.set(sum);
+    }
+  }
+}