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diff --git a/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/AnderbergHierarchicalClusteringTest.java b/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/AnderbergHierarchicalClusteringTest.java
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+++ b/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/AnderbergHierarchicalClusteringTest.java
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+package de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical;
+
+/*
+ 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 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.hierarchical.extraction.ExtractFlatClusteringFromHierarchy;
+import de.lmu.ifi.dbs.elki.data.Clustering;
+import de.lmu.ifi.dbs.elki.database.Database;
+import de.lmu.ifi.dbs.elki.result.Result;
+import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
+import de.lmu.ifi.dbs.elki.workflow.AlgorithmStep;
+
+/**
+ * Perform agglomerative hierarchical clustering, using the anderberg improved
+ * algorithm.
+ *
+ * @author Erich Schubert
+ * @since 0.6.0
+ */
+public class AnderbergHierarchicalClusteringTest extends AbstractSimpleAlgorithmTest implements JUnit4Test {
+  // TODO: add more data sets.
+
+  /**
+   * Run agglomerative hierarchical clustering with fixed parameters and compare
+   * the result to a golden standard.
+   */
+  @Test
+  public void testSingleLink() {
+    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);
+
+    // Setup algorithm
+    ListParameterization params = new ListParameterization();
+    params.addParameter(ExtractFlatClusteringFromHierarchy.Parameterizer.MINCLUSTERS_ID, 3);
+    params.addParameter(AlgorithmStep.Parameterizer.ALGORITHM_ID, AnderbergHierarchicalClustering.class);
+    params.addParameter(AGNES.Parameterizer.LINKAGE_ID, SingleLinkageMethod.class);
+    ExtractFlatClusteringFromHierarchy c = ClassGenericsUtil.parameterizeOrAbort(ExtractFlatClusteringFromHierarchy.class, params);
+    testParameterizationOk(params);
+
+    // run clustering algorithm on database
+    Result result = c.run(db);
+    Clustering<?> clustering = findSingleClustering(result);
+    testFMeasure(db, clustering, 0.6829722);
+    testClusterSizes(clustering, new int[] { 9, 200, 429 });
+  }
+
+  /**
+   * Run agglomerative hierarchical clustering with fixed parameters and compare
+   * the result to a golden standard.
+   */
+  @Test
+  public void testWard() {
+    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);
+
+    // Setup algorithm
+    ListParameterization params = new ListParameterization();
+    params.addParameter(ExtractFlatClusteringFromHierarchy.Parameterizer.MINCLUSTERS_ID, 3);
+    params.addParameter(AlgorithmStep.Parameterizer.ALGORITHM_ID, AnderbergHierarchicalClustering.class);
+    ExtractFlatClusteringFromHierarchy c = ClassGenericsUtil.parameterizeOrAbort(ExtractFlatClusteringFromHierarchy.class, params);
+    testParameterizationOk(params);
+
+    // run clustering algorithm on database
+    Result result = c.run(db);
+    Clustering<?> clustering = findSingleClustering(result);
+    testFMeasure(db, clustering, 0.93866265);
+    testClusterSizes(clustering, new int[] { 200, 211, 227 });
+  }
+
+  /**
+   * Run agglomerative hierarchical clustering with fixed parameters and compare
+   * the result to a golden standard.
+   */
+  @Test
+  public void testGroupAverage() {
+    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);
+
+    // Setup algorithm
+    ListParameterization params = new ListParameterization();
+    params.addParameter(ExtractFlatClusteringFromHierarchy.Parameterizer.MINCLUSTERS_ID, 3);
+    params.addParameter(AlgorithmStep.Parameterizer.ALGORITHM_ID, AnderbergHierarchicalClustering.class);
+    params.addParameter(AGNES.Parameterizer.LINKAGE_ID, GroupAverageLinkageMethod.class);
+    ExtractFlatClusteringFromHierarchy c = ClassGenericsUtil.parameterizeOrAbort(ExtractFlatClusteringFromHierarchy.class, params);
+    testParameterizationOk(params);
+
+    // run clustering algorithm on database
+    Result result = c.run(db);
+    Clustering<?> clustering = findSingleClustering(result);
+    testFMeasure(db, clustering, 0.93866265);
+    testClusterSizes(clustering, new int[] { 200, 211, 227 });
+  }
+
+  /**
+   * Run agglomerative hierarchical clustering with fixed parameters and compare
+   * the result to a golden standard.
+   */
+  @Test
+  public void testCompleteLink() {
+    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);
+
+    // Setup algorithm
+    ListParameterization params = new ListParameterization();
+    params.addParameter(ExtractFlatClusteringFromHierarchy.Parameterizer.MINCLUSTERS_ID, 3);
+    params.addParameter(AlgorithmStep.Parameterizer.ALGORITHM_ID, AnderbergHierarchicalClustering.class);
+    params.addParameter(AGNES.Parameterizer.LINKAGE_ID, CompleteLinkageMethod.class);
+    ExtractFlatClusteringFromHierarchy c = ClassGenericsUtil.parameterizeOrAbort(ExtractFlatClusteringFromHierarchy.class, params);
+    testParameterizationOk(params);
+
+    // run clustering algorithm on database
+    Result result = c.run(db);
+    Clustering<?> clustering = findSingleClustering(result);
+    testFMeasure(db, clustering, 0.938167802);
+    testClusterSizes(clustering, new int[] { 200, 217, 221 });
+  }
+}