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diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/TestOnlineLOF.java b/test/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/TestOnlineLOF.java
deleted file mode 100644
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--- a/test/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/TestOnlineLOF.java
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@@ -1,168 +0,0 @@
-package de.lmu.ifi.dbs.elki.algorithm.outlier.lof;
-
-/*
- This file is part of ELKI:
- Environment for Developing KDD-Applications Supported by Index-Structures
-
- Copyright (C) 2012
- 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.assertTrue;
-import static org.junit.Assert.fail;
-
-import java.util.ArrayList;
-import java.util.Random;
-
-import org.junit.Test;
-
-import de.lmu.ifi.dbs.elki.JUnit4Test;
-import de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF;
-import de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF;
-import de.lmu.ifi.dbs.elki.algorithm.outlier.lof.OnlineLOF;
-import de.lmu.ifi.dbs.elki.data.DoubleVector;
-import de.lmu.ifi.dbs.elki.data.NumberVector;
-import de.lmu.ifi.dbs.elki.data.VectorUtil;
-import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
-import de.lmu.ifi.dbs.elki.database.HashmapDatabase;
-import de.lmu.ifi.dbs.elki.database.UpdatableDatabase;
-import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
-import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
-import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
-import de.lmu.ifi.dbs.elki.database.relation.Relation;
-import de.lmu.ifi.dbs.elki.database.relation.RelationUtil;
-import de.lmu.ifi.dbs.elki.datasource.FileBasedDatabaseConnection;
-import de.lmu.ifi.dbs.elki.datasource.bundle.MultipleObjectsBundle;
-import de.lmu.ifi.dbs.elki.distance.distancefunction.CosineDistanceFunction;
-import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
-import de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction;
-import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
-import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
-import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
-import de.lmu.ifi.dbs.elki.utilities.exceptions.UnableToComplyException;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
-
-/**
- * Tests the OnlineLOF algorithm. Compares the result of the static LOF
- * algorithm to the result of the OnlineLOF algorithm, where some insertions and
- * deletions (of the previously inserted objects) have been applied to the
- * database.
- * 
- * @author Elke Achtert
- * 
- */
-public class TestOnlineLOF implements JUnit4Test {
-  // the following values depend on the data set used!
-  static String dataset = "data/testdata/unittests/3clusters-and-noise-2d.csv";
-
-  // parameter k for LOF and OnlineLOF
-  static int k = 5;
-
-  // neighborhood distance function for LOF and OnlineLOF
-  @SuppressWarnings("rawtypes")
-  static DistanceFunction neighborhoodDistanceFunction = EuclideanDistanceFunction.STATIC;
-
-  // reachability distance function for LOF and OnlineLOF
-  @SuppressWarnings("rawtypes")
-  static DistanceFunction reachabilityDistanceFunction = CosineDistanceFunction.STATIC;
-
-  // seed for the generator
-  static int seed = 5;
-
-  // size of the data set
-  static int size = 50;
-
-  /**
-   * First, run the {@link LOF} algorithm on the database. Second, run the
-   * {@link OnlineLOF} algorithm on the database, insert new objects and
-   * afterwards delete them. Then, compare the two results for equality.
-   * 
-   * @throws UnableToComplyException
-   */
-  @SuppressWarnings("unchecked")
-  @Test
-  public void testOnlineLOF() throws UnableToComplyException {
-    UpdatableDatabase db = getDatabase();
-
-    // 1. Run LOF
-    FlexibleLOF<DoubleVector, DoubleDistance> lof = new FlexibleLOF<>(k, k, neighborhoodDistanceFunction, reachabilityDistanceFunction);
-    OutlierResult result1 = lof.run(db);
-
-    // 2. Run OnlineLOF (with insertions and removals) on database
-    OutlierResult result2 = runOnlineLOF(db);
-
-    // 3. Compare results
-    Relation<Double> scores1 = result1.getScores();
-    Relation<Double> scores2 = result2.getScores();
-
-    for(DBIDIter id = scores1.getDBIDs().iter(); id.valid(); id.advance()) {
-      Double lof1 = scores1.get(id);
-      Double lof2 = scores2.get(id);
-      assertTrue("lof(" + DBIDUtil.toString(id) + ") != lof(" + DBIDUtil.toString(id) + "): " + lof1 + " != " + lof2, lof1.equals(lof2));
-    }
-  }
-
-  /**
-   * Run OnlineLOF (with insertions and removals) on database.
-   */
-  @SuppressWarnings("unchecked")
-  private static OutlierResult runOnlineLOF(UpdatableDatabase db) throws UnableToComplyException {
-    Relation<DoubleVector> rep = db.getRelation(TypeUtil.DOUBLE_VECTOR_FIELD);
-
-    // setup algorithm
-    OnlineLOF<DoubleVector, DoubleDistance> lof = new OnlineLOF<>(k, k, neighborhoodDistanceFunction, reachabilityDistanceFunction);
-
-    // run OnlineLOF on database
-    OutlierResult result = lof.run(db);
-
-    // insert new objects
-    ArrayList<DoubleVector> insertions = new ArrayList<>();
-    NumberVector.Factory<DoubleVector, ?> o = RelationUtil.getNumberVectorFactory(rep);
-    int dim = RelationUtil.dimensionality(rep);
-    Random random = new Random(seed);
-    for(int i = 0; i < size; i++) {
-      DoubleVector obj = VectorUtil.randomVector(o, dim, random);
-      insertions.add(obj);
-    }
-    DBIDs deletions = db.insert(MultipleObjectsBundle.makeSimple(rep.getDataTypeInformation(), insertions));
-
-    // delete objects
-    db.delete(deletions);
-
-    return result;
-  }
-
-  /**
-   * Returns the database.
-   */
-  private static UpdatableDatabase getDatabase() {
-    ListParameterization params = new ListParameterization();
-    params.addParameter(FileBasedDatabaseConnection.Parameterizer.INPUT_ID, dataset);
-
-    UpdatableDatabase db = ClassGenericsUtil.parameterizeOrAbort(HashmapDatabase.class, params);
-    params.failOnErrors();
-    if(params.hasUnusedParameters()) {
-      fail("Unused parameters: " + params.getRemainingParameters());
-    }
-
-    // get database
-    db.initialize();
-    return db;
-  }
-
-}