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diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/TestKMeansBisecting.java b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/TestKMeansBisecting.java
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+package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2013
+ 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.kmeans.quality.WithinClusterVarianceQualityMeasure;
+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.database.Database;
+import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
+import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
+
+/**
+ * Tests the KMeansBisecting
+ * 
+ * @author Stephan Baier
+ */
+public class TestKMeansBisecting extends AbstractSimpleAlgorithmTest implements JUnit4Test {
+  /**
+   * Run KMeansBisecting with fixed parameters and compare cluster size to
+   * expected value.
+   */
+  @Test
+  public void testKMeansBisectingClusterSize() {
+    Database db = makeSimpleDatabase(UNITTEST + "bisecting-test.csv", 300);
+
+    // Setup algorithm
+    ListParameterization params = new ListParameterization();
+    params.addParameter(KMeans.K_ID, 3);
+    params.addParameter(BestOfMultipleKMeans.Parameterizer.TRIALS_ID, 5);
+    params.addParameter(BestOfMultipleKMeans.Parameterizer.KMEANS_ID, KMeansLloyd.class);
+    params.addParameter(BestOfMultipleKMeans.Parameterizer.QUALITYMEASURE_ID, WithinClusterVarianceQualityMeasure.class);
+
+    KMeansBisecting<DoubleVector, DoubleDistance, MeanModel<DoubleVector>> kmeans = ClassGenericsUtil.parameterizeOrAbort(KMeansBisecting.class, params);
+    testParameterizationOk(params);
+
+    // run KMedians on database
+    Clustering<MeanModel<DoubleVector>> result = kmeans.run(db);
+    testClusterSizes(result, new int[] { 103, 97, 100 });
+  }
+
+  /**
+   * Run KMeansBisecting with fixed parameters (k = 2) and compare f-measure to
+   * golden standard.
+   */
+  @Test
+  public void testKMeansBisectingFMeasure() {
+    Database db = makeSimpleDatabase(UNITTEST + "bisecting-test.csv", 300);
+
+    // Setup algorithm
+    ListParameterization params = new ListParameterization();
+    params.addParameter(KMeans.K_ID, 2);
+    params.addParameter(BestOfMultipleKMeans.Parameterizer.TRIALS_ID, 5);
+    params.addParameter(BestOfMultipleKMeans.Parameterizer.KMEANS_ID, KMeansLloyd.class);
+    params.addParameter(BestOfMultipleKMeans.Parameterizer.QUALITYMEASURE_ID, WithinClusterVarianceQualityMeasure.class);
+
+    KMeansBisecting<DoubleVector, DoubleDistance, MeanModel<DoubleVector>> kmeans = ClassGenericsUtil.parameterizeOrAbort(KMeansBisecting.class, params);
+    testParameterizationOk(params);
+
+    // run KMedians on database
+    Clustering<MeanModel<DoubleVector>> result = kmeans.run(db);
+    testFMeasure(db, result, 0.7408);
+  }
+}