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diff --git a/src/de/lmu/ifi/dbs/elki/math/dimensionsimilarity/SURFINGDimensionSimilarity.java b/src/de/lmu/ifi/dbs/elki/math/dimensionsimilarity/SURFINGDimensionSimilarity.java
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+package de.lmu.ifi.dbs.elki.math.dimensionsimilarity;
+
+/*
+ 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/>.
+ */
+
+import java.util.BitSet;
+
+import de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.database.Database;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
+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.subspace.SubspaceEuclideanDistanceFunction;
+import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
+import de.lmu.ifi.dbs.elki.math.Mean;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
+
+/**
+ * Compute the similarity of dimensions using the SURFING score. The parameter k
+ * for the k nearest neighbors is currently hard-coded to 10% of the set size.
+ * 
+ * Note that the complexity is roughly O(n n k) * O(d^2), so this is a rather
+ * slow method without index support, and with k at 10% of n, is actually cubic.
+ * So try to use an appropriate index!
+ * 
+ * Reference:
+ * <p>
+ * Christian Baumgartner, Claudia Plant, Karin Kailing, Hans-Peter Kriegel, and
+ * Peer Kröger<br />
+ * Subspace Selection for Clustering High-Dimensional Data<br />
+ * In IEEE International Conference on Data Mining, 2004.
+ * </p>
+ * 
+ * TODO: make the subspace distance function and k parameterizable.
+ * 
+ * @author Robert Rödler
+ * @author Erich Schubert
+ * 
+ * @apiviz.uses SubspaceEuclideanDistanceFunction
+ */
+@Reference(authors = "Christian Baumgartner, Claudia Plant, Karin Kailing, Hans-Peter Kriegel, and Peer Kröger", title = "Subspace Selection for Clustering High-Dimensional Data", booktitle = "IEEE International Conference on Data Mining, 2004", url = "http://dx.doi.org/10.1109/ICDM.2004.10112")
+public class SURFINGDimensionSimilarity implements DimensionSimilarity<NumberVector<?>> {
+  /**
+   * Static instance.
+   */
+  public static final SURFINGDimensionSimilarity STATIC = new SURFINGDimensionSimilarity();
+
+  /**
+   * Constructor. Use static instance instead!
+   */
+  protected SURFINGDimensionSimilarity() {
+    super();
+  }
+
+  @Override
+  public void computeDimensionSimilarites(Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix) {
+    final int dim = matrix.size();
+    final Database db = relation.getDatabase();
+    Mean kdistmean = new Mean();
+    final int k = Math.max(1, subset.size() / 10);
+
+    double[] knns = new double[subset.size()];
+
+    // TODO: optimize by using 1d indexes?
+    for (int x = 0; x < dim; x++) {
+      final int i = matrix.dim(x);
+      for (int y = x + 1; y < dim; y++) {
+        final int j = matrix.dim(y);
+        BitSet dims = new BitSet(dim);
+        dims.set(i);
+        dims.set(j);
+        DistanceQuery<? extends NumberVector<?>, DoubleDistance> dq = db.getDistanceQuery(relation, new SubspaceEuclideanDistanceFunction(dims));
+        KNNQuery<? extends NumberVector<?>, DoubleDistance> knnq = db.getKNNQuery(dq, k);
+
+        kdistmean.reset();
+        int knn = 0;
+        for (DBIDIter id1 = subset.iter(); id1.valid(); id1.advance(), knn++) {
+          final double kdist = knnq.getKNNForDBID(id1, k).getKNNDistance().doubleValue();
+          kdistmean.put(kdist);
+          knns[knn] = kdist;
+        }
+        double mean = kdistmean.getMean();
+        // Deviation from mean:
+        double diff = 0.;
+        int below = 0;
+        for (int l = 0; l < knns.length; l++) {
+          diff += Math.abs(mean - knns[l]);
+          if (knns[l] < mean) {
+            below++;
+          }
+        }
+        matrix.set(x, y, (below > 0) ? diff / (2. * mean * below) : 0);
+      }
+    }
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer extends AbstractParameterizer {
+    @Override
+    protected SURFINGDimensionSimilarity makeInstance() {
+      return STATIC;
+    }
+  }
+}