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diff --git a/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/quality/WithinClusterMeanDistanceQualityMeasureTest.java b/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/quality/WithinClusterMeanDistanceQualityMeasureTest.java
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+++ b/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/quality/WithinClusterMeanDistanceQualityMeasureTest.java
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+package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2015
+ 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 static org.junit.Assert.assertEquals;
+
+import org.junit.Test;
+
+import de.lmu.ifi.dbs.elki.JUnit4Test;
+import de.lmu.ifi.dbs.elki.algorithm.AbstractSimpleAlgorithmTest;
+import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans;
+import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans;
+import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd;
+import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.FirstKInitialMeans;
+import de.lmu.ifi.dbs.elki.data.Clustering;
+import de.lmu.ifi.dbs.elki.data.DoubleVector;
+import de.lmu.ifi.dbs.elki.data.model.MeanModel;
+import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
+import de.lmu.ifi.dbs.elki.database.Database;
+import de.lmu.ifi.dbs.elki.database.relation.Relation;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.NumberVectorDistanceFunction;
+import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
+
+/**
+ * Test cluster quality measure computations.
+ *
+ * @author Stephan Baier
+ * @since 0.6.0
+ */
+public class WithinClusterMeanDistanceQualityMeasureTest extends AbstractSimpleAlgorithmTest implements JUnit4Test {
+  /**
+   * Test cluster average overall distance.
+   */
+  @Test
+  public void testOverallDistance() {
+    Database db = makeSimpleDatabase(UNITTEST + "quality-measure-test.csv", 7);
+    Relation<DoubleVector> rel = db.getRelation(TypeUtil.DOUBLE_VECTOR_FIELD);
+
+    // Setup algorithm
+    ListParameterization params = new ListParameterization();
+    params = new ListParameterization();
+    params.addParameter(KMeans.K_ID, 2);
+    params.addParameter(KMeans.INIT_ID, FirstKInitialMeans.class);
+    AbstractKMeans<DoubleVector, ?> kmeans = ClassGenericsUtil.parameterizeOrAbort(KMeansLloyd.class, params);
+    testParameterizationOk(params);
+
+    // run KMeans on database
+    @SuppressWarnings("unchecked")
+    Clustering<MeanModel> result = (Clustering<MeanModel>) kmeans.run(db);
+    final NumberVectorDistanceFunction<? super DoubleVector> dist = kmeans.getDistanceFunction();
+
+    // Test Cluster Average Overall Distance
+    KMeansQualityMeasure<? super DoubleVector> overall = new WithinClusterMeanDistanceQualityMeasure();
+    final double quality = overall.quality(result, dist, rel);
+
+    assertEquals("Avarage overall distance not as expected.", 0.8888888888888888, quality, 1e-10);
+  }
+}