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diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/em/MultivariateGaussianModelFactory.java b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/em/MultivariateGaussianModelFactory.java
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+package de.lmu.ifi.dbs.elki.algorithm.clustering.em;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2014
+ 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 java.util.ArrayList;
+import java.util.List;
+
+import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.KMeansInitialization;
+import de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.data.model.EMModel;
+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.PrimitiveDistanceFunction;
+import de.lmu.ifi.dbs.elki.math.MathUtil;
+import de.lmu.ifi.dbs.elki.math.linearalgebra.Vector;
+
+/**
+ * Factory for EM with multivariate gaussian models (with covariance; also known
+ * as Gaussian Mixture Modeling, GMM).
+ * 
+ * These models have individual covariance matrixes, so this corresponds to the
+ * {@code 'VVV'} model in Mclust (R).
+ * 
+ * @author Erich Schubert
+ * 
+ * @apiviz.has MultivariateGaussianModel
+ * 
+ * @param <V> vector type
+ */
+public class MultivariateGaussianModelFactory<V extends NumberVector> extends AbstractEMModelFactory<V, EMModel> {
+  /**
+   * Constructor.
+   * 
+   * @param initializer Class for choosing the inital seeds.
+   */
+  public MultivariateGaussianModelFactory(KMeansInitialization<V> initializer) {
+    super(initializer);
+  }
+
+  @Override
+  public List<MultivariateGaussianModel> buildInitialModels(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector> df) {
+    final List<Vector> initialMeans = initializer.chooseInitialMeans(database, relation, k, df, Vector.FACTORY);
+    assert (initialMeans.size() == k);
+    final int dimensionality = initialMeans.get(0).getDimensionality();
+    final double norm = MathUtil.powi(MathUtil.TWOPI, dimensionality);
+    List<MultivariateGaussianModel> models = new ArrayList<>(k);
+    for(Vector nv : initialMeans) {
+      models.add(new MultivariateGaussianModel(1. / k, nv, norm));
+    }
+    return models;
+  }
+
+  /**
+   * Parameterization class
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   * 
+   * @param <V> Vector type
+   */
+  public static class Parameterizer<V extends NumberVector> extends AbstractEMModelFactory.Parameterizer<V> {
+    @Override
+    protected MultivariateGaussianModelFactory<V> makeInstance() {
+      return new MultivariateGaussianModelFactory<>(initializer);
+    }
+  }
+}