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diff --git a/src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/WeightedPearsonCorrelationDistanceFunction.java b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/WeightedPearsonCorrelationDistanceFunction.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) 2011
+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.Arrays;
+
+import de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractVectorDoubleDistanceFunction;
+import de.lmu.ifi.dbs.elki.math.MathUtil;
+
+/**
+ * Pearson correlation distance function for feature vectors.
+ * 
+ * The Pearson correlation distance is computed from the Pearson correlation
+ * coefficient <code>r</code> as: <code>1-r</code>. Hence, possible values of
+ * this distance are between 0 and 2.
+ * 
+ * The distance between two vectors will be low (near 0), if their attribute
+ * values are dimension-wise strictly positively correlated, it will be high
+ * (near 2), if their attribute values are dimension-wise strictly negatively
+ * correlated. For Features with uncorrelated attributes, the distance value
+ * will be intermediate (around 1).
+ * 
+ * This variation is for weighted dimensions.
+ * 
+ * @author Arthur Zimek
+ * @author Erich Schubert
+ */
+public class WeightedPearsonCorrelationDistanceFunction extends AbstractVectorDoubleDistanceFunction {
+  /**
+   * Weights
+   */
+  private double[] weights;
+
+  /**
+   * Provides a PearsonCorrelationDistanceFunction.
+   * 
+   * @param weights Weights
+   */
+  public WeightedPearsonCorrelationDistanceFunction(double[] weights) {
+    super();
+    this.weights = weights;
+  }
+
+  /**
+   * Computes the Pearson correlation distance for two given feature vectors.
+   * 
+   * The Pearson correlation distance is computed from the Pearson correlation
+   * coefficient <code>r</code> as: <code>1-r</code>. Hence, possible values of
+   * this distance are between 0 and 2.
+   * 
+   * @param v1 first feature vector
+   * @param v2 second feature vector
+   * @return the Pearson correlation distance for two given feature vectors v1
+   *         and v2
+   */
+  @Override
+  public double doubleDistance(NumberVector<?, ?> v1, NumberVector<?, ?> v2) {
+    return 1 - MathUtil.weightedPearsonCorrelationCoefficient(v1, v2, weights);
+  }
+
+  @Override
+  public boolean equals(Object obj) {
+    if(this == obj) {
+      return true;
+    }
+    if(obj == null) {
+      return false;
+    }
+    if (!this.getClass().equals(obj.getClass())) {
+      return false;
+    }
+    return Arrays.equals(this.weights, ((WeightedPearsonCorrelationDistanceFunction)obj).weights);
+  }
+}
+\ No newline at end of file