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diff --git a/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/KMeans.java b/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/kmeans/KMeans.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) 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 de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm;
+import de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm;
+import de.lmu.ifi.dbs.elki.data.Clustering;
+import de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.data.model.Model;
+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.NumberVectorDistanceFunction;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
+
+/**
+ * Some constants and options shared among kmeans family algorithms.
+ *
+ * @author Erich Schubert
+ *
+ * @param <V> Number vector type
+ * @param <M> Actual model type
+ */
+public interface KMeans<V extends NumberVector, M extends Model> extends ClusteringAlgorithm<Clustering<M>>, DistanceBasedAlgorithm<V> {
+  /**
+   * Parameter to specify the initialization method
+   */
+  public static final OptionID INIT_ID = new OptionID("kmeans.initialization", "Method to choose the initial means.");
+
+  /**
+   * Parameter to specify the number of clusters to find, must be an integer
+   * greater than 0.
+   */
+  public static final OptionID K_ID = new OptionID("kmeans.k", "The number of clusters to find.");
+
+  /**
+   * Parameter to specify the number of clusters to find, must be an integer
+   * greater or equal to 0, where 0 means no limit.
+   */
+  public static final OptionID MAXITER_ID = new OptionID("kmeans.maxiter", "The maximum number of iterations to do. 0 means no limit.");
+
+  /**
+   * Parameter to specify the random generator seed.
+   */
+  public static final OptionID SEED_ID = new OptionID("kmeans.seed", "The random number generator seed.");
+
+  /**
+   * Run the clustering algorithm.
+   *
+   * @param database Database to run on.
+   * @param rel Relation to process.
+   * @return Clustering result
+   */
+  Clustering<M> run(Database database, Relation<V> rel);
+
+  /**
+   * Set the value of k. Needed for some types of nested k-means.
+   *
+   * @param k K parameter
+   */
+  void setK(int k);
+
+  /**
+   * Set the distance function to use.
+   *
+   * @param distanceFunction Distance function.
+   */
+  void setDistanceFunction(NumberVectorDistanceFunction<? super V> distanceFunction);
+}