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diff --git a/src/de/lmu/ifi/dbs/elki/index/preprocessed/knn/KNNJoinMaterializeKNNPreprocessor.java b/src/de/lmu/ifi/dbs/elki/index/preprocessed/knn/KNNJoinMaterializeKNNPreprocessor.java
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+++ b/src/de/lmu/ifi/dbs/elki/index/preprocessed/knn/KNNJoinMaterializeKNNPreprocessor.java
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+package de.lmu.ifi.dbs.elki.index.preprocessed.knn;
+
+import de.lmu.ifi.dbs.elki.algorithm.KNNJoin;
+import de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.database.relation.Relation;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
+import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
+import de.lmu.ifi.dbs.elki.index.tree.spatial.SpatialEntry;
+import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar.RStarTreeNode;
+import de.lmu.ifi.dbs.elki.logging.Logging;
+import de.lmu.ifi.dbs.elki.utilities.datastructures.heap.KNNList;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2012
+ 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/>.
+ */
+
+/**
+ * Class to materialize the kNN using a spatial join on an R-tree.
+ * 
+ * @author Erich Schubert
+ * 
+ * @param <V> vector type
+ * @param <D> distance type
+ */
+public class KNNJoinMaterializeKNNPreprocessor<V extends NumberVector<V, ?>, D extends Distance<D>> extends AbstractMaterializeKNNPreprocessor<V, D, KNNList<D>> {
+  /**
+   * Logging class.
+   */
+  private static final Logging logger = Logging.getLogger(KNNJoinMaterializeKNNPreprocessor.class);
+
+  /**
+   * Constructor.
+   * 
+   * @param relation Relation to index
+   * @param distanceFunction Distance function
+   * @param k k
+   */
+  public KNNJoinMaterializeKNNPreprocessor(Relation<V> relation, DistanceFunction<? super V, D> distanceFunction, int k) {
+    super(relation, distanceFunction, k);
+  }
+
+  @Override
+  protected void preprocess() {
+    // Run KNNJoin
+    KNNJoin<V, D, ?, ?> knnjoin = new KNNJoin<V, D, RStarTreeNode, SpatialEntry>(distanceFunction, k);
+    storage = knnjoin.run(relation.getDatabase(), relation);
+  }
+
+  @Override
+  protected Logging getLogger() {
+    return logger;
+  }
+
+  @Override
+  public String getLongName() {
+    return "knn-join materialized neighbors";
+  }
+
+  @Override
+  public String getShortName() {
+    return "knn-join";
+  }
+
+  /**
+   * The parameterizable factory.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.landmark
+   * @apiviz.stereotype factory
+   * @apiviz.uses AbstractMaterializeKNNPreprocessor oneway - - «create»
+   * 
+   * @param <O> The object type
+   * @param <D> The distance type
+   */
+  public static class Factory<O extends NumberVector<O, ?>, D extends Distance<D>> extends AbstractMaterializeKNNPreprocessor.Factory<O, D, KNNList<D>> {
+    /**
+     * Constructor.
+     * 
+     * @param k K
+     * @param distanceFunction distance function
+     */
+    public Factory(int k, DistanceFunction<? super O, D> distanceFunction) {
+      super(k, distanceFunction);
+    }
+
+    @Override
+    public KNNJoinMaterializeKNNPreprocessor<O, D> instantiate(Relation<O> relation) {
+      return new KNNJoinMaterializeKNNPreprocessor<O, D>(relation, distanceFunction, k);
+    }
+
+    /**
+     * Parameterization class
+     * 
+     * @author Erich Schubert
+     * 
+     * @apiviz.exclude
+     * 
+     * @param <O> Object type
+     * @param <D> Distance type
+     */
+    public static class Parameterizer<O extends NumberVector<O, ?>, D extends Distance<D>> extends AbstractMaterializeKNNPreprocessor.Factory.Parameterizer<O, D> {
+      @Override
+      protected KNNJoinMaterializeKNNPreprocessor.Factory<O, D> makeInstance() {
+        return new KNNJoinMaterializeKNNPreprocessor.Factory<O, D>(k, distanceFunction);
+      }
+    }
+  }
+}
+\ No newline at end of file