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diff --git a/src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/PCABasedCorrelationDistanceFunction.java b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/PCABasedCorrelationDistanceFunction.java
deleted file mode 100644
index b7a22b32..00000000
--- a/src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/PCABasedCorrelationDistanceFunction.java
+++ /dev/null
@@ -1,291 +0,0 @@
-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) 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 de.lmu.ifi.dbs.elki.data.NumberVector;
-import de.lmu.ifi.dbs.elki.database.ids.DBIDRef;
-import de.lmu.ifi.dbs.elki.database.relation.Relation;
-import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractIndexBasedDistanceFunction;
-import de.lmu.ifi.dbs.elki.distance.distancefunction.FilteredLocalPCABasedDistanceFunction;
-import de.lmu.ifi.dbs.elki.distance.distancevalue.PCACorrelationDistance;
-import de.lmu.ifi.dbs.elki.index.IndexFactory;
-import de.lmu.ifi.dbs.elki.index.preprocessed.localpca.FilteredLocalPCAIndex;
-import de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex;
-import de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix;
-import de.lmu.ifi.dbs.elki.math.linearalgebra.Vector;
-import de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredResult;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.CommonConstraints;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;
-
-/**
- * Provides the correlation distance for real valued vectors.
- * 
- * @author Elke Achtert
- * 
- * @apiviz.has Instance
- */
-public class PCABasedCorrelationDistanceFunction extends AbstractIndexBasedDistanceFunction<NumberVector<?>, FilteredLocalPCAIndex<NumberVector<?>>, PCACorrelationDistance> implements FilteredLocalPCABasedDistanceFunction<NumberVector<?>, FilteredLocalPCAIndex<NumberVector<?>>, PCACorrelationDistance> {
-  /**
-   * Parameter to specify the threshold of a distance between a vector q and a
-   * given space that indicates that q adds a new dimension to the space, must
-   * be a double equal to or greater than 0.
-   * <p>
-   * Default value: {@code 0.25}
-   * </p>
-   * <p>
-   * Key: {@code -pcabasedcorrelationdf.delta}
-   * </p>
-   */
-  public static final OptionID DELTA_ID = new OptionID("pcabasedcorrelationdf.delta", "Threshold of a distance between a vector q and a given space that indicates that " + "q adds a new dimension to the space.");
-
-  /**
-   * Holds the value of {@link #DELTA_ID}.
-   */
-  private double delta;
-
-  /**
-   * Constructor
-   * 
-   * @param indexFactory index factory
-   * @param delta Delta parameter
-   */
-  public PCABasedCorrelationDistanceFunction(IndexFactory<NumberVector<?>, FilteredLocalPCAIndex<NumberVector<?>>> indexFactory, double delta) {
-    super(indexFactory);
-    this.delta = delta;
-  }
-
-  @Override
-  public PCACorrelationDistance getDistanceFactory() {
-    return PCACorrelationDistance.FACTORY;
-  }
-
-  @Override
-  public <T extends NumberVector<?>> Instance<T> instantiate(Relation<T> database) {
-    // We can't really avoid these warnings, due to a limitation in Java
-    // Generics (AFAICT)
-    @SuppressWarnings("unchecked")
-    FilteredLocalPCAIndex<T> indexinst = (FilteredLocalPCAIndex<T>) indexFactory.instantiate((Relation<NumberVector<?>>) database);
-    return new Instance<>(database, indexinst, delta, this);
-  }
-
-  @Override
-  public boolean equals(Object obj) {
-    if(obj == null) {
-      return false;
-    }
-    if(!this.getClass().equals(obj.getClass())) {
-      return false;
-    }
-    PCABasedCorrelationDistanceFunction other = (PCABasedCorrelationDistanceFunction) obj;
-    return (this.delta == other.delta);
-  }
-
-  /**
-   * The actual instance bound to a particular database.
-   * 
-   * @author Erich Schubert
-   */
-  public static class Instance<V extends NumberVector<?>> extends AbstractIndexBasedDistanceFunction.Instance<V, FilteredLocalPCAIndex<V>, PCACorrelationDistance, PCABasedCorrelationDistanceFunction> implements FilteredLocalPCABasedDistanceFunction.Instance<V, FilteredLocalPCAIndex<V>, PCACorrelationDistance> {
-    /**
-     * Delta value
-     */
-    final double delta;
-
-    /**
-     * Constructor.
-     * 
-     * @param database Database
-     * @param index Index to use
-     * @param delta Delta
-     * @param distanceFunction Distance function
-     */
-    public Instance(Relation<V> database, FilteredLocalPCAIndex<V> index, double delta, PCABasedCorrelationDistanceFunction distanceFunction) {
-      super(database, index, distanceFunction);
-      this.delta = delta;
-    }
-
-    @Override
-    public PCACorrelationDistance distance(DBIDRef id1, DBIDRef id2) {
-      PCAFilteredResult pca1 = index.getLocalProjection(id1);
-      PCAFilteredResult pca2 = index.getLocalProjection(id2);
-      V dv1 = relation.get(id1);
-      V dv2 = relation.get(id2);
-
-      int correlationDistance = correlationDistance(pca1, pca2, dv1.getDimensionality());
-      double euclideanDistance = euclideanDistance(dv1, dv2);
-
-      return new PCACorrelationDistance(correlationDistance, euclideanDistance);
-    }
-
-    /**
-     * Computes the correlation distance between the two subspaces defined by
-     * the specified PCAs.
-     * 
-     * @param pca1 first PCA
-     * @param pca2 second PCA
-     * @param dimensionality the dimensionality of the data space
-     * @return the correlation distance between the two subspaces defined by the
-     *         specified PCAs
-     */
-    public int correlationDistance(PCAFilteredResult pca1, PCAFilteredResult pca2, int dimensionality) {
-      // TODO nur in eine Richtung?
-      // pca of rv1
-      Matrix v1 = pca1.getEigenvectors().copy();
-      Matrix v1_strong = pca1.adapatedStrongEigenvectors().copy();
-      Matrix e1_czech = pca1.selectionMatrixOfStrongEigenvectors().copy();
-      int lambda1 = pca1.getCorrelationDimension();
-
-      // pca of rv2
-      Matrix v2 = pca2.getEigenvectors().copy();
-      Matrix v2_strong = pca2.adapatedStrongEigenvectors().copy();
-      Matrix e2_czech = pca2.selectionMatrixOfStrongEigenvectors().copy();
-      int lambda2 = pca2.getCorrelationDimension();
-
-      // for all strong eigenvectors of rv2
-      Matrix m1_czech = pca1.dissimilarityMatrix();
-      for(int i = 0; i < v2_strong.getColumnDimensionality(); i++) {
-        Vector v2_i = v2_strong.getCol(i);
-        // check, if distance of v2_i to the space of rv1 > delta
-        // (i.e., if v2_i spans up a new dimension)
-        double dist = Math.sqrt(v2_i.transposeTimes(v2_i) - v2_i.transposeTimesTimes(m1_czech, v2_i));
-
-        // if so, insert v2_i into v1 and adjust v1
-        // and compute m1_czech new, increase lambda1
-        if(lambda1 < dimensionality && dist > delta) {
-          adjust(v1, e1_czech, v2_i, lambda1++);
-          m1_czech = v1.times(e1_czech).timesTranspose(v1);
-        }
-      }
-
-      // for all strong eigenvectors of rv1
-      Matrix m2_czech = pca2.dissimilarityMatrix();
-      for(int i = 0; i < v1_strong.getColumnDimensionality(); i++) {
-        Vector v1_i = v1_strong.getCol(i);
-        // check, if distance of v1_i to the space of rv2 > delta
-        // (i.e., if v1_i spans up a new dimension)
-        double dist = Math.sqrt(v1_i.transposeTimes(v1_i) - v1_i.transposeTimes(m2_czech).times(v1_i).get(0));
-
-        // if so, insert v1_i into v2 and adjust v2
-        // and compute m2_czech new , increase lambda2
-        if(lambda2 < dimensionality && dist > delta) {
-          adjust(v2, e2_czech, v1_i, lambda2++);
-          m2_czech = v2.times(e2_czech).timesTranspose(v2);
-        }
-      }
-
-      int correlationDistance = Math.max(lambda1, lambda2);
-
-      // TODO delta einbauen
-      // Matrix m_1_czech = pca1.dissimilarityMatrix();
-      // double dist_1 = normalizedDistance(dv1, dv2, m1_czech);
-      // Matrix m_2_czech = pca2.dissimilarityMatrix();
-      // double dist_2 = normalizedDistance(dv1, dv2, m2_czech);
-      // if (dist_1 > delta || dist_2 > delta) {
-      // correlationDistance++;
-      // }
-
-      return correlationDistance;
-    }
-
-    /**
-     * Inserts the specified vector into the given orthonormal matrix
-     * <code>v</code> at column <code>corrDim</code>. After insertion the matrix
-     * <code>v</code> is orthonormalized and column <code>corrDim</code> of
-     * matrix <code>e_czech</code> is set to the <code>corrDim</code>-th unit
-     * vector.
-     * 
-     * @param v the orthonormal matrix of the eigenvectors
-     * @param e_czech the selection matrix of the strong eigenvectors
-     * @param vector the vector to be inserted
-     * @param corrDim the column at which the vector should be inserted
-     */
-    private void adjust(Matrix v, Matrix e_czech, Vector vector, int corrDim) {
-      int dim = v.getRowDimensionality();
-
-      // set e_czech[corrDim][corrDim] := 1
-      e_czech.set(corrDim, corrDim, 1);
-
-      // normalize v
-      Vector v_i = vector.copy();
-      Vector sum = new Vector(dim);
-      for(int k = 0; k < corrDim; k++) {
-        Vector v_k = v.getCol(k);
-        sum.plusTimesEquals(v_k, v_i.transposeTimes(v_k));
-      }
-      v_i.minusEquals(sum);
-      v_i.normalize();
-      v.setCol(corrDim, v_i);
-    }
-
-    /**
-     * Computes the Euclidean distance between the given two vectors.
-     * 
-     * @param dv1 first FeatureVector
-     * @param dv2 second FeatureVector
-     * @return the Euclidean distance between the given two vectors
-     */
-    private double euclideanDistance(V dv1, V dv2) {
-      if(dv1.getDimensionality() != dv2.getDimensionality()) {
-        throw new IllegalArgumentException("Different dimensionality of FeatureVectors\n  first argument: " + dv1.toString() + "\n  second argument: " + dv2.toString());
-      }
-
-      double sqrDist = 0;
-      for(int i = 0; i < dv1.getDimensionality(); i++) {
-        double manhattanI = dv1.doubleValue(i) - dv2.doubleValue(i);
-        sqrDist += manhattanI * manhattanI;
-      }
-      return Math.sqrt(sqrDist);
-    }
-  }
-
-  /**
-   * Parameterization class.
-   * 
-   * @author Erich Schubert
-   * 
-   * @apiviz.exclude
-   */
-  public static class Parameterizer extends AbstractIndexBasedDistanceFunction.Parameterizer<FilteredLocalPCAIndex.Factory<NumberVector<?>, FilteredLocalPCAIndex<NumberVector<?>>>> {
-    protected double delta = 0.0;
-
-    @Override
-    protected void makeOptions(Parameterization config) {
-      super.makeOptions(config);
-      configIndexFactory(config, FilteredLocalPCAIndex.Factory.class, KNNQueryFilteredPCAIndex.Factory.class);
-
-      final DoubleParameter param = new DoubleParameter(DELTA_ID, 0.25);
-      param.addConstraint(CommonConstraints.GREATER_EQUAL_ZERO_DOUBLE);
-      if(config.grab(param)) {
-        delta = param.doubleValue();
-      }
-    }
-
-    @Override
-    protected PCABasedCorrelationDistanceFunction makeInstance() {
-      return new PCABasedCorrelationDistanceFunction(factory, delta);
-    }
-  }
-}