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diff --git a/src/de/lmu/ifi/dbs/elki/math/statistics/dependence/SpearmanCorrelationDependenceMeasure.java b/src/de/lmu/ifi/dbs/elki/math/statistics/dependence/SpearmanCorrelationDependenceMeasure.java
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+package de.lmu.ifi.dbs.elki.math.statistics.dependence;
+
+/*
+ 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.utilities.datastructures.arraylike.NumberArrayAdapter;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
+
+/**
+ * Spearman rank-correlation coefficient, also known as Spearmans Rho.
+ * 
+ * @author Erich Schubert
+ */
+public class SpearmanCorrelationDependenceMeasure extends AbstractDependenceMeasure {
+  /**
+   * Static instance.
+   */
+  public static final SpearmanCorrelationDependenceMeasure STATIC = new SpearmanCorrelationDependenceMeasure();
+
+  /**
+   * Constructor - use {@link #STATIC} instance.
+   */
+  protected SpearmanCorrelationDependenceMeasure() {
+    super();
+  }
+
+  @Override
+  public <A, B> double dependence(NumberArrayAdapter<?, A> adapter1, A data1, NumberArrayAdapter<?, B> adapter2, B data2) {
+    final int len = size(adapter1, data1, adapter2, data2);
+    double[] ranks1 = computeNormalizedRanks(adapter1, data1, len);
+    double[] ranks2 = computeNormalizedRanks(adapter2, data2, len);
+
+    // Second pass: variances and covariance
+    double v1 = 0., v2 = 0., cov = 0.;
+    for(int i = 0; i < len; i++) {
+      double d1 = ranks1[i] - .5, d2 = ranks2[i] - .5;
+      v1 += d1 * d1;
+      v2 += d2 * d2;
+      cov += d1 * d2;
+    }
+    // Note: we did not normalize by len, as this cancels out.
+    return cov / Math.sqrt(v1 * v2);
+  }
+
+  /**
+   * Parameterization class
+   * 
+   * @author Erich Schubert
+   *
+   * @apiviz.exclude
+   */
+  public static class Parameterizer extends AbstractParameterizer {
+    @Override
+    protected SpearmanCorrelationDependenceMeasure makeInstance() {
+      return STATIC;
+    }
+  }
+}