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diff --git a/src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/AbsolutePearsonCorrelationDistanceFunction.java b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/AbsolutePearsonCorrelationDistanceFunction.java
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+package de.lmu.ifi.dbs.elki.distance.distancefunction.correlation;
+
+/*
+ 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.data.NumberVector;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractNumberVectorDistanceFunction;
+import de.lmu.ifi.dbs.elki.math.MathUtil;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
+
+/**
+ * Absolute Pearson correlation distance function for feature vectors.
+ * 
+ * The absolute Pearson correlation distance is computed from the Pearson
+ * correlation coefficient <code>r</code> as: <code>1-abs(r)</code>.
+ * 
+ * The distance between two vectors will be low (near 0), if their attribute
+ * values are dimension-wise strictly positively or negatively correlated, it
+ * will be high (near 1), if their attribute values are dimension-wise
+ * uncorrelated.
+ * 
+ * @author Erich Schubert
+ */
+public class AbsolutePearsonCorrelationDistanceFunction extends AbstractNumberVectorDistanceFunction {
+  /**
+   * Static instance.
+   */
+  public static final AbsolutePearsonCorrelationDistanceFunction STATIC = new AbsolutePearsonCorrelationDistanceFunction();
+
+  /**
+   * Constructor - use {@link #STATIC} instead.
+   * 
+   * @deprecated Use static instance!
+   */
+  @Deprecated
+  public AbsolutePearsonCorrelationDistanceFunction() {
+    super();
+  }
+
+  /**
+   * Computes the absolute Pearson correlation distance for two given feature
+   * vectors.
+   * 
+   * The absolute Pearson correlation distance is computed from the Pearson
+   * correlation coefficient <code>r</code> as: <code>1-abs(r)</code>. Hence,
+   * possible values of this distance are between 0 and 1.
+   * 
+   * @param v1 first feature vector
+   * @param v2 second feature vector
+   * @return the absolute Pearson correlation distance for two given feature
+   *         vectors v1 and v2
+   */
+  @Override
+  public double distance(NumberVector v1, NumberVector v2) {
+    return 1 - Math.abs(MathUtil.pearsonCorrelationCoefficient(v1, v2));
+  }
+
+  @Override
+  public String toString() {
+    return "AbsolutePearsonCorrelationDistance";
+  }
+
+  @Override
+  public boolean equals(Object obj) {
+    if(obj == null) {
+      return false;
+    }
+    return this.getClass().equals(obj.getClass());
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer extends AbstractParameterizer {
+    @Override
+    protected AbsolutePearsonCorrelationDistanceFunction makeInstance() {
+      return AbsolutePearsonCorrelationDistanceFunction.STATIC;
+    }
+  }
+}
+\ No newline at end of file